Editorial

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Open AccessEditorial Advances in Sensors Applied to Agriculture and Forestry

Sensors 2011, 11(9), 8930-8932; doi:10.3390/s110908930

Received: 14 September 2011 / Accepted: 15 September 2011 / Published: 15 September 2011

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Abstract

In agriculture and forestry, the need to increase production and the simultaneous efforts to minimize the environmental impact of agricultural production processes and save costs find in sensor systems the best allied tool. The use of sensors helps exploit all available resources appropriately

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In agriculture and forestry, the need to increase production and the simultaneous efforts to minimize the environmental impact of agricultural production processes and save costs find in sensor systems the best allied tool. The use of sensors helps exploit all available resources appropriately and to apply hazardous products moderately. When nutrients in the soil, humidity, solar radiation, density of weeds and a broad set of factors and data affecting the production are known, this situation improves and the use of chemical products such as fertilizers, herbicides and other pollutants can be reduced considerably. Part of this knowledge allows also monitoring photosynthetic parameters of high relevance for photosynthesis. Most of the associated activities fall within the scope of what it is called Precision Agriculture, an emerging area receiving special attention in recent years. [...] Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Research

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Open AccessArticle Validation of a CFD Model by Using 3D Sonic Anemometers to Analyse the Air Velocity Generated by an Air-Assisted Sprayer Equipped with Two Axial Fans

by F. Javier García-Ramos, Hugo Malón, A. Javier Aguirre, Antonio Boné, Javier Puyuelo and Mariano Vidal

Sensors 2015, 15(2), 2399-2418; doi:10.3390/s150202399

Received: 9 November 2014 / Accepted: 14 January 2015 / Published: 22 January 2015

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Abstract

A computational fluid dynamics (CFD) model of the air flow generated by an air-assisted sprayer equipped with two axial fans was developed and validated by practical experiments in the laboratory. The CFD model was developed by considering the total air flow supplied by

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A computational fluid dynamics (CFD) model of the air flow generated by an air-assisted sprayer equipped with two axial fans was developed and validated by practical experiments in the laboratory. The CFD model was developed by considering the total air flow supplied by the sprayer fan to be the main parameter, rather than the outlet air velocity. The model was developed for three air flows corresponding to three fan blade settings and assuming that the sprayer is stationary. Actual measurements of the air velocity near the sprayer were taken using 3D sonic anemometers. The workspace sprayer was divided into three sections, and the air velocity was measured in each section on both sides of the machine at a horizontal distance of 1.5, 2.5, and 3.5 m from the machine, and at heights of 1, 2, 3, and 4 m above the ground The coefficient of determination (R²) between the simulated and measured values was 0.859, which demonstrates a good correlation between the simulated and measured data. Considering the overall data, the air velocity values produced by the CFD model were not significantly different from the measured values. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Design and Testing of an Agricultural Implement for Underground Application of Rodenticide Bait

by Hugo Malón, A. Javier Aguirre, Antonio Boné, Mariano Vidal and F. Javier García-Ramos

Sensors 2015, 15(1), 2006-2020; doi:10.3390/s150102006

Received: 9 November 2014 / Accepted: 7 January 2015 / Published: 16 January 2015

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Abstract

An agricultural implement for underground application of rodenticide bait to control the Mediterranean pocket gopher (Microtus Duodecimcostatus) in fruit orchards has been designed and tested. The main objective of this research was to design and test the implement by using the

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An agricultural implement for underground application of rodenticide bait to control the Mediterranean pocket gopher (Microtus Duodecimcostatus) in fruit orchards has been designed and tested. The main objective of this research was to design and test the implement by using the finite element method (FEM) and considering a range of loads generated on most commonly used furrow openers in agricultural implements. As a second step, the prototype was tested in the field by analysing the effects of forward speed and application depth on the mechanical behaviour of the implement structure. The FEM was used in the design phase and a prototype was manufactured. The structural strains on the prototype chassis under working conditions were tested by using strain gauges to validate the design phase. Three forward speeds (4.5, 5.5, and 7.0 km/h), three application depths (0.12, 0.15, and 0.17 m), and two types of soil (clayey-silty-loam and clayey-silty-sandy) were considered. The prototype was validated successfully by analysing the information obtained from the strain gauges. The Von Mises stresses indicated a safety coefficient of 1.9 for the most critical load case. Although both forward speed and application depth had a significant effect on the stresses generated on the chassis, the latter parameter critically affected the structural behaviour of the implement. The effects of the application depth on the strains were linear such that strains increased with depth. In contrast, strains remained roughly constant regardless of variation in the forward speed. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Modeling Apple Surface Temperature Dynamics Based on Weather Data

by Lei Li, Troy Peters, Qin Zhang, Jingjin Zhang and Danfeng Huang

Sensors 2014, 14(11), 20217-20234; doi:10.3390/s141120217

Received: 13 August 2014 / Revised: 9 October 2014 / Accepted: 16 October 2014 / Published: 27 October 2014

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Abstract

The exposure of fruit surfaces to direct sunlight during the summer months can result in sunburn damage. Losses due to sunburn damage are a major economic problem when marketing fresh apples. The objective of this study was to develop and validate a model

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The exposure of fruit surfaces to direct sunlight during the summer months can result in sunburn damage. Losses due to sunburn damage are a major economic problem when marketing fresh apples. The objective of this study was to develop and validate a model for simulating fruit surface temperature (FST) dynamics based on energy balance and measured weather data. A series of weather data (air temperature, humidity, solar radiation, and wind speed) was recorded for seven hours between 11:00–18:00 for two months at fifteen minute intervals. To validate the model, the FSTs of “Fuji” apples were monitored using an infrared camera in a natural orchard environment. The FST dynamics were measured using a series of thermal images. For the apples that were completely exposed to the sun, the RMSE of the model for estimating FST was less than 2.0 °C. A sensitivity analysis of the emissivity of the apple surface and the conductance of the fruit surface to water vapour showed that accurate estimations of the apple surface emissivity were important for the model. The validation results showed that the model was capable of accurately describing the thermal performances of apples under different solar radiation intensities. Thus, this model could be used to more accurately estimate the FST relative to estimates that only consider the air temperature. In addition, this model provides useful information for sunburn protection management. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle The Effect on Wireless Sensor Communication When Deployed in Biomass

by Jakob Juul Larsen, Ole Green, Esmaeil S. Nadimi and Thomas Skjøodeberg Toftegaard

Sensors 2011, 11(9), 8295-8308; doi:10.3390/s110908295

Received: 1 August 2011 / Revised: 22 August 2011 / Accepted: 23 August 2011 / Published: 25 August 2011

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Abstract

Wireless sensor networks (WSN) have been studied in a variety of scenarios over recent years, but work has almost exclusively been done using air as the transmission media. In this article some of the challenges of deploying a WSN in a heterogeneous biomass,

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Wireless sensor networks (WSN) have been studied in a variety of scenarios over recent years, but work has almost exclusively been done using air as the transmission media. In this article some of the challenges of deploying a WSN in a heterogeneous biomass, in this case silage, is handled. The dielectric constant of silage is measured using an open-ended coaxial probe. Results were successfully obtained in the frequency range from 400 MHz to 4 GHz, but large variations suggested that a larger probe should be used for more stable results. Furthermore, the detuning of helix and loop antennas and the transmission loss of the two types of antennas embedded in silage was measured. It was found that the loop antenna suffered less from detuning but was worse when transmitting. Lastly, it is suggested that taking the dielectric properties of silage into account during hardware development could result in much better achievable communication range. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Characterizing Olive Grove Canopies by Means of Ground-Based Hemispherical Photography and Spaceborne RADAR Data

by Iñigo Molina, Carmen Morillo, Eduardo García-Meléndez, Rafael Guadalupe and Maria Isabel Roman

Sensors 2011, 11(8), 7476-7501; doi:10.3390/s110807476

Received: 24 June 2011 / Revised: 20 July 2011 / Accepted: 21 July 2011 / Published: 28 July 2011

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Abstract

One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All

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One of the main strengths of active microwave remote sensing, in relation to frequency, is its capacity to penetrate vegetation canopies and reach the ground surface, so that information can be drawn about the vegetation and hydrological properties of the soil surface. All this information is gathered in the so called backscattering coefficient (s⁰). The subject of this research have been olive groves canopies, where which types of canopy biophysical variables can be derived by a specific optical sensor and then integrated into microwave scattering models has been investigated. This has been undertaken by means of hemispherical photographs and gap fraction procedures. Then, variables such as effective and true Leaf Area Indices have been estimated. Then, in order to characterize this kind of vegetation canopy, two models based on Radiative Transfer theory have been applied and analyzed. First, a generalized two layer geometry model made up of homogeneous layers of soil and vegetation has been considered. Then, a modified version of the Xu and Steven Water Cloud Model has been assessed integrating the canopy biophysical variables derived by the suggested optical procedure. The backscattering coefficients at various polarized channels have been acquired from RADARSAT 2 (C-band), with 38.5° incidence angle at the scene center. For the soil simulation, the best results have been reached using a Dubois scattering model and the VV polarized channel (r² = 0.88). In turn, when effective LAI (LAI_eff) has been taken into account, the parameters of the scattering canopy model are better estimated (r² = 0.89). Additionally, an inversion procedure of the vegetation microwave model with the adjusted parameters has been undertaken, where the biophysical values of the canopy retrieved by this methodology fit properly with field measured values. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed

by Nadir Sainz-Costa, Angela Ribeiro, Xavier P. Burgos-Artizzu, María Guijarro and Gonzalo Pajares

Sensors 2011, 11(7), 7095-7109; doi:10.3390/s110707095

Received: 6 April 2011 / Revised: 4 July 2011 / Accepted: 6 July 2011 / Published: 11 July 2011

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Abstract

This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for

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This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for image sequence stabilization was needed and consequently designed and developed. The proposed stabilization method uses the centers of some crop rows in the image sequence as features to be tracked, which compensates for the lateral movement (sway) of the camera and leaves the pitch unchanged. A region of interest is selected using the tracked features, and an inverse perspective technique transforms the selected region into a bird’s-eye view that is centered on the image and that enables map generation. The algorithm developed has been tested on several video sequences of different fields recorded at different times and under different lighting conditions, with good initial results. Indeed, lateral displacements of up to 66% of the inter-row spacing were suppressed through the stabilization process, and crop rows in the resulting maps appear straight. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Steering a Tractor by Means of an EMG-Based Human-Machine Interface

by Jaime Gomez-Gil, Israel San-Jose-Gonzalez, Luis Fernando Nicolas-Alonso and Sergio Alonso-Garcia

Sensors 2011, 11(7), 7110-7126; doi:10.3390/s110707110

Received: 14 June 2011 / Revised: 4 July 2011 / Accepted: 7 July 2011 / Published: 11 July 2011

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Abstract

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An

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An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Use of Sensor Imagery Data for Surface Boundary Conditions in Regional Climate Modeling

by Hyun Il Choi

Sensors 2011, 11(7), 6728-6742; doi:10.3390/s110706728

Received: 25 March 2011 / Revised: 22 June 2011 / Accepted: 27 June 2011 / Published: 28 June 2011

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Abstract

Mesoscale climate and hydrology modeling studies have increased in sophistication and are being run at increasingly higher resolutions. Data resolution sufficiently finer than that of the computational model is required not only to support sophisticated linkages and process interactions at small scales but

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Mesoscale climate and hydrology modeling studies have increased in sophistication and are being run at increasingly higher resolutions. Data resolution sufficiently finer than that of the computational model is required not only to support sophisticated linkages and process interactions at small scales but to assess their cumulative impact at larger scales. The global distributions at fine spatial and temporal scales can be described by means of various senor imagery data collected through remote sensing techniques, sensor image and photo programs, scanning and digitizing skills for existing maps, etc. The availability of global sensor imagery maps facilitates assimilation in land surface models to account for terrestrial dynamics. This study focuses on the use of global imagery data for development and construction of surface boundary conditions (SBCs) specifically designed for mesoscale regional climate model (RCM) applications. The several SBCs are currently presented in a RCM domain for the continent of Asia at 30-km spacing by using sensor imagery data. Geographic Information System (GIS) software application tools are mainly used to convert data information from various raw data onto RCM-specific grids. The raw data sources and processing procedures are elaborated in detail, by which the SBCs can be readily constructed for any specific RCM domain anywhere in the world. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Enviro-Net: From Networks of Ground-Based Sensor Systems to a Web Platform for Sensor Data Management

by Gilberto Z. Pastorello, G. Arturo Sanchez-Azofeifa and Mario A. Nascimento

Sensors 2011, 11(6), 6454-6479; doi:10.3390/s110606454

Received: 22 April 2011 / Accepted: 15 June 2011 / Published: 17 June 2011

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Abstract

Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems

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Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle An Image Segmentation Based on a Genetic Algorithm for Determining Soil Coverage by Crop Residues

by Angela Ribeiro, Juan Ranz, Xavier P. Burgos-Artizzu, Gonzalo Pajares, Maria J. Sanchez del Arco and Luis Navarrete

Sensors 2011, 11(6), 6480-6492; doi:10.3390/s110606480

Received: 29 April 2011 / Revised: 29 May 2011 / Accepted: 7 June 2011 / Published: 17 June 2011

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Abstract

Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically

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Determination of the soil coverage by crop residues after ploughing is a fundamental element of Conservation Agriculture. This paper presents the application of genetic algorithms employed during the fine tuning of the segmentation process of a digital image with the aim of automatically quantifying the residue coverage. In other words, the objective is to achieve a segmentation that would permit the discrimination of the texture of the residue so that the output of the segmentation process is a binary image in which residue zones are isolated from the rest. The RGB images used come from a sample of images in which sections of terrain were photographed with a conventional camera positioned in zenith orientation atop a tripod. The images were taken outdoors under uncontrolled lighting conditions. Up to 92% similarity was achieved between the images obtained by the segmentation process proposed in this paper and the templates made by an elaborate manual tracing process. In addition to the proposed segmentation procedure and the fine tuning procedure that was developed, a global quantification of the soil coverage by residues for the sampled area was achieved that differed by only 0.85% from the quantification obtained using template images. Moreover, the proposed method does not depend on the type of residue present in the image. The study was conducted at the experimental farm “El Encín” in Alcalá de Henares (Madrid, Spain). Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Field Calibrations of Soil Moisture Sensors in a Forested Watershed

by Farhat Abbas, Ali Fares and Samira Fares

Sensors 2011, 11(6), 6354-6369; doi:10.3390/s110606354

Received: 28 April 2011 / Revised: 20 May 2011 / Accepted: 7 June 2011 / Published: 16 June 2011

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Abstract

Spatially variable soil properties influence the performance of soil water content monitoring sensors. The objectives of this research were to: (i) study the spatial variability of bulk density (ρ_b), total porosity (θ_t), clay content (CC), electrical conductivity

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Spatially variable soil properties influence the performance of soil water content monitoring sensors. The objectives of this research were to: (i) study the spatial variability of bulk density (ρ_b), total porosity (θ_t), clay content (CC), electrical conductivity (EC), and pH in the upper Mākaha Valley watershed soils; (ii) explore the effect of variations in ρ_b and θ_t on soil water content dynamics, and (iii) establish field calibration equations for EC-20 (Decagon Devices, Inc), ML2x (Delta-T-Devices), and SM200 (Delta-T-Devices) sensors to mitigate the effect of soil spatial variability on their performance. The studied soil properties except pH varied significantly (P < 0.05) across the soil water content monitoring depths (20 and 80 cm) and six locations. There was a linear positive and a linear inverse correlation between the soil water content at sampling and ρ_b, and between the soil water content at sampling and θ_t, respectively. Values of laboratory measured actual θ_t correlated (r = 0.75) with those estimated from the relationship θ_t = 1 − ρ_b/ρ_s, where ρ_s is the particle density. Variations in the studied soil properties affected the performance of the default equations of the three tested sensors; they showed substantial under-estimations of the actual water content. The individual and the watershed-scale field calibrations were more accurate than their corresponding default calibrations. In conclusion, the sensors used in this study need site-specific calibrations in order to mitigate the effects of varying properties of the highly weathered tropical soils. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

by José Ramiro Martínez-de Dios, Luis Merino, Fernando Caballero and Anibal Ollero

Sensors 2011, 11(6), 6328-6353; doi:10.3390/s110606328

Received: 1 April 2011 / Revised: 31 May 2011 / Accepted: 2 June 2011 / Published: 16 June 2011

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Abstract

This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front,

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This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Relation of Biospeckle Activity with Quality Attributes of Apples

by Artur Zdunek and Justyna Cybulska

Sensors 2011, 11(6), 6317-6327; doi:10.3390/s110606317

Received: 27 April 2011 / Revised: 7 June 2011 / Accepted: 8 June 2011 / Published: 14 June 2011

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Abstract

Biospeckle is nondestructive optical technique based on the analysis of variations of laser light scattered from biological samples. Biospeckle activity reflects the state of the investigated object. In this study the relation of biospeckle activity (BA) with firmness, soluble solids content (SSC), titratable

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Biospeckle is nondestructive optical technique based on the analysis of variations of laser light scattered from biological samples. Biospeckle activity reflects the state of the investigated object. In this study the relation of biospeckle activity (BA) with firmness, soluble solids content (SSC), titratable acidity (TA) and starch content (SC) during the shelf life of seven apple cultivars was studied. The results showed that the quality attributes change significantly during storage. Significant and pronounced positive correlation between BA and SC was found. This result shows that degradation of starch granules, which could be stimulated to vibration by intracellular cyclosis, causes a lesser number of laser light scattering centers and results in smaller apparent biospeckle activity. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

by Hong Y. Jeon, Lei F. Tian and Heping Zhu

Sensors 2011, 11(6), 6270-6283; doi:10.3390/s110606270

Received: 18 April 2011 / Revised: 18 May 2011 / Accepted: 7 June 2011 / Published: 10 June 2011

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Abstract

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated

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An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA). Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Georeferenced LiDAR 3D Vine Plantation Map Generation

by Jordi Llorens, Emilio Gil, Jordi Llop and Meritxell Queraltó

Sensors 2011, 11(6), 6237-6256; doi:10.3390/s110606237

Received: 5 May 2011 / Revised: 26 May 2011 / Accepted: 7 June 2011 / Published: 9 June 2011

Cited by 21 | PDF Full-text (1968 KB) | HTML Full-text | XML Full-text

Abstract

The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be

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The use of electronic devices for canopy characterization has recently been widely discussed. Among such devices, LiDAR sensors appear to be the most accurate and precise. Information obtained with LiDAR sensors during reading while driving a tractor along a crop row can be managed and transformed into canopy density maps by evaluating the frequency of LiDAR returns. This paper describes a proposed methodology to obtain a georeferenced canopy map by combining the information obtained with LiDAR with that generated using a GPS receiver installed on top of a tractor. Data regarding the velocity of LiDAR measurements and UTM coordinates of each measured point on the canopy were obtained by applying the proposed transformation process. The process allows overlap of the canopy density map generated with the image of the intended measured area using Google Earth^®, providing accurate information about the canopy distribution and/or location of damage along the rows. This methodology was applied and tested on different vine varieties and crop stages in two important vine production areas in Spain. The results indicate that the georeferenced information obtained with LiDAR sensors appears to be an interesting tool with the potential to improve crop management processes. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle An Air-Ground Wireless Sensor Network for Crop Monitoring

by João Valente, David Sanz, Antonio Barrientos, Jaime del Cerro, Ángela Ribeiro and Claudio Rossi

Sensors 2011, 11(6), 6088-6108; doi:10.3390/s110606088

Received: 21 March 2011 / Revised: 25 May 2011 / Accepted: 30 May 2011 / Published: 7 June 2011

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Abstract

This paper presents a collaborative system made up of a Wireless Sensor Network (WSN) and an aerial robot, which is applied to real-time frost monitoring in vineyards. The core feature of our system is a dynamic mobile node carried by an aerial robot,

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This paper presents a collaborative system made up of a Wireless Sensor Network (WSN) and an aerial robot, which is applied to real-time frost monitoring in vineyards. The core feature of our system is a dynamic mobile node carried by an aerial robot, which ensures communication between sparse clusters located at fragmented parcels and a base station. This system overcomes some limitations of the wireless networks in areas with such characteristics. The use of a dedicated communication channel enables data routing to/from unlimited distances. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing

by Jaime Lloret, Ignacio Bosch, Sandra Sendra and Arturo Serrano

Sensors 2011, 11(6), 6165-6196; doi:10.3390/s110606165

Received: 10 May 2011 / Revised: 31 May 2011 / Accepted: 31 May 2011 / Published: 7 June 2011

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Abstract

The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of

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The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically. In this paper, we present a wireless sensor network where each sensor node takes images from the field and internally uses image processing techniques to detect any unusual status in the leaves. This symptom could be caused by a deficiency, pest, disease or other harmful agent. When it is detected, the sensor node sends a message to a sink node through the wireless sensor network in order to notify the problem to the farmer. The wireless sensor uses the IEEE 802.11 a/b/g/n standard, which allows connections from large distances in open air. This paper describes the wireless sensor network design, the wireless sensor deployment, how the node processes the images in order to monitor the vineyard, and the sensor network traffic obtained from a test bed performed in a flat vineyard in Spain. Although the system is not able to distinguish between deficiency, pest, disease or other harmful agents, a symptoms image database and a neuronal network could be added in order learn from the experience and provide an accurate problem diagnosis. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Digital Image Sensor-Based Assessment of the Status of Oat (Avena sativa L.) Crops after Frost Damage

by Antonia Macedo-Cruz, Gonzalo Pajares, Matilde Santos and Isidro Villegas-Romero

Sensors 2011, 11(6), 6015-6036; doi:10.3390/s110606015

Received: 11 April 2011 / Revised: 18 May 2011 / Accepted: 30 May 2011 / Published: 3 June 2011

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Abstract

The aim of this paper is to classify the land covered with oat crops, and the quantification of frost damage on oats, while plants are still in the flowering stage. The images are taken by a digital colour camera CCD-based sensor. Unsupervised classification

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The aim of this paper is to classify the land covered with oat crops, and the quantification of frost damage on oats, while plants are still in the flowering stage. The images are taken by a digital colour camera CCD-based sensor. Unsupervised classification methods are applied because the plants present different spectral signatures, depending on two main factors: illumination and the affected state. The colour space used in this application is CIELab, based on the decomposition of the colour in three channels, because it is the closest to human colour perception. The histogram of each channel is successively split into regions by thresholding. The best threshold to be applied is automatically obtained as a combination of three thresholding strategies: (a) Otsu’s method, (b) Isodata algorithm, and (c) Fuzzy thresholding. The fusion of these automatic thresholding techniques and the design of the classification strategy are some of the main findings of the paper, which allows an estimation of the damages and a prediction of the oat production. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Innovative LIDAR 3D Dynamic Measurement System to Estimate Fruit-Tree Leaf Area

by Ricardo Sanz-Cortiella, Jordi Llorens-Calveras, Alexandre Escolà, Jaume Arnó-Satorra, Manel Ribes-Dasi, Joan Masip-Vilalta, Ferran Camp, Felip Gràcia-Aguilá, Francesc Solanelles-Batlle, Santiago Planas-DeMartí, Tomàs Pallejà-Cabré, Jordi Palacin-Roca, Eduard Gregorio-Lopez, Ignacio Del-Moral-Martínez and Joan R. Rosell-Polo

Sensors 2011, 11(6), 5769-5791; doi:10.3390/s110605769

Received: 11 April 2011 / Revised: 9 May 2011 / Accepted: 12 May 2011 / Published: 27 May 2011

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Abstract

In this work, a LIDAR-based 3D Dynamic Measurement System is presented and evaluated for the geometric characterization of tree crops. Using this measurement system, trees were scanned from two opposing sides to obtain two three-dimensional point clouds. After registration of the point clouds,

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In this work, a LIDAR-based 3D Dynamic Measurement System is presented and evaluated for the geometric characterization of tree crops. Using this measurement system, trees were scanned from two opposing sides to obtain two three-dimensional point clouds. After registration of the point clouds, a simple and easily obtainable parameter is the number of impacts received by the scanned vegetation. The work in this study is based on the hypothesis of the existence of a linear relationship between the number of impacts of the LIDAR sensor laser beam on the vegetation and the tree leaf area. Tests performed under laboratory conditions using an ornamental tree and, subsequently, in a pear tree orchard demonstrate the correct operation of the measurement system presented in this paper. The results from both the laboratory and field tests confirm the initial hypothesis and the 3D Dynamic Measurement System is validated in field operation. This opens the door to new lines of research centred on the geometric characterization of tree crops in the field of agriculture and, more specifically, in precision fruit growing. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle A Simple Method to Improve Autonomous GPS Positioning for Tractors

by Jaime Gomez-Gil, Sergio Alonso-Garcia, Francisco Javier Gómez-Gil and Tim Stombaugh

Sensors 2011, 11(6), 5630-5644; doi:10.3390/s110605630

Received: 14 April 2011 / Revised: 9 May 2011 / Accepted: 20 May 2011 / Published: 26 May 2011

Cited by 20 | PDF Full-text (759 KB) | HTML Full-text | XML Full-text

Abstract

Error is always present in the GPS guidance of a tractor along a desired trajectory. One way to reduce GPS guidance error is by improving the tractor positioning. The most commonly used ways to do this are either by employing more precise GPS

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Error is always present in the GPS guidance of a tractor along a desired trajectory. One way to reduce GPS guidance error is by improving the tractor positioning. The most commonly used ways to do this are either by employing more precise GPS receivers and differential corrections or by employing GPS together with some other local positioning systems such as electronic compasses or Inertial Navigation Systems (INS). However, both are complex and expensive solutions. In contrast, this article presents a simple and low cost method to improve tractor positioning when only a GPS receiver is used as the positioning sensor. The method is based on placing the GPS receiver ahead of the tractor, and on applying kinematic laws of tractor movement, or a geometric approximation, to obtain the midpoint position and orientation of the tractor rear axle more precisely. This precision improvement is produced by the fusion of the GPS data with tractor kinematic control laws. Our results reveal that the proposed method effectively reduces the guidance GPS error along a straight trajectory. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Sensing the Structural Differences in Cellulose from Apple and Bacterial Cell Wall Materials by Raman and FT-IR Spectroscopy

by Monika Szymańska-Chargot, Justyna Cybulska and Artur Zdunek

Sensors 2011, 11(6), 5543-5560; doi:10.3390/s110605543

Received: 9 March 2011 / Revised: 3 April 2011 / Accepted: 7 April 2011 / Published: 25 May 2011

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Abstract

Raman and Fourier Transform Infrared (FT-IR) spectroscopy was used for assessment of structural differences of celluloses of various origins. Investigated celluloses were: bacterial celluloses cultured in presence of pectin and/or xyloglucan, as well as commercial celluloses and cellulose extracted from apple parenchyma. FT-IR

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Raman and Fourier Transform Infrared (FT-IR) spectroscopy was used for assessment of structural differences of celluloses of various origins. Investigated celluloses were: bacterial celluloses cultured in presence of pectin and/or xyloglucan, as well as commercial celluloses and cellulose extracted from apple parenchyma. FT-IR spectra were used to estimate of the I_β content, whereas Raman spectra were used to evaluate the degree of crystallinity of the cellulose. The crystallinity index (X_C^RAMAN%) varied from −25% for apple cellulose to 53% for microcrystalline commercial cellulose. Considering bacterial cellulose, addition of xyloglucan has an impact on the percentage content of cellulose I_β. However, addition of only xyloglucan or only pectins to pure bacterial cellulose both resulted in a slight decrease of crystallinity. However, culturing bacterial cellulose in the presence of mixtures of xyloglucan and pectins results in an increase of crystallinity. The results confirmed that the higher degree of crystallinity, the broader the peak around 913 cm⁻¹. Among all bacterial celluloses the bacterial cellulose cultured in presence of xyloglucan and pectin (BCPX) has the most similar structure to those observed in natural primary cell walls. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

by Eetu Puttonen, Anttoni Jaakkola, Paula Litkey and Juha Hyyppä

Sensors 2011, 11(5), 5158-5182; doi:10.3390/s110505158

Received: 18 March 2011 / Revised: 28 April 2011 / Accepted: 5 May 2011 / Published: 11 May 2011

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Abstract

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual

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Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle A Diagnostic System for Improving Biomass Quality Based on a Sensor Network

by Dionysis D. Bochtis, Claus G. Sørensen, Ole Green and Thomas Bartzanas

Sensors 2011, 11(5), 4990-5004; doi:10.3390/s110504990

Received: 28 March 2011 / Revised: 29 April 2011 / Accepted: 3 May 2011 / Published: 4 May 2011

Cited by 4 | PDF Full-text (605 KB) | HTML Full-text | XML Full-text

Abstract

Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of

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Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of silage decomposition and form the basis for preventive measures. This study presents a framework for a diagnostic system capable of detecting potential changes in specific physicochemical properties, i.e., temperature and the oxygen content, during the biomass storage process. The diagnostic system comprises a monitoring tool based on a wireless sensors network and a prediction tool based on a validated computation fluid dynamics model. It is shown that the system can provide the manager (end-user) with continuously updated information about specific biomass quality parameters. The system encompasses graphical visualization of the information to the end-user as a first step and, as a second step, the system identifies alerts depicting real differences between actual and predicted values of the monitored properties. The perspective is that this diagnostic system will provide managers with a solid basis for necessary preventive measures. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Improved Calibration Functions of Three Capacitance Probes for the Measurement of Soil Moisture in Tropical Soils

by Ali Fares, Farhat Abbas, Domingos Maria and Alan Mair

Sensors 2011, 11(5), 4858-4874; doi:10.3390/s110504858

Received: 14 February 2011 / Revised: 7 April 2011 / Accepted: 29 April 2011 / Published: 3 May 2011

Cited by 6 | PDF Full-text (348 KB) | HTML Full-text | XML Full-text

Abstract

Single capacitance sensors are sensitive to soil property variability. The objectives of this study were to: (i) establish site-specific laboratory calibration equations of three single capacitance sensors (EC-20, EC-10, and ML2x) for tropical soils, and (ii) evaluate the accuracy and precision of these

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Single capacitance sensors are sensitive to soil property variability. The objectives of this study were to: (i) establish site-specific laboratory calibration equations of three single capacitance sensors (EC-20, EC-10, and ML2x) for tropical soils, and (ii) evaluate the accuracy and precision of these sensors. Intact soil cores and bulk samples, collected from the top 20 and 80 cm soil depths at five locations across the Upper Mākaha Valley watershed, were analyzed to determine their soil bulk density (ρ_b), total porosity (θ_t), particle size distribution, and electrical conductivity (EC). Laboratory calibration equations were established using soil packed columns at six water content levels (0–0.5 cm³ cm⁻³). Soil bulk density and θ_t significantly varied with sampling depths; whereas, soil clay content (CC) and EC varied with sampling locations. Variations of ρ_b and θ_t at the two depths significantly affected the EC-20 and ML2x laboratory calibration functions; however, there was no effect of these properties on calibration equation functions of EC-10. There was no significant effect of sampling locations on the laboratory calibration functions suggesting watershed-specific equations for EC-20 and ML2x for the two depths; a single watershed-specific equation was needed for EC-10 for both sampling depths. The laboratory calibration equations for all sensors were more accurate than the corresponding default equations. ML2x exhibited better precision than EC-10, followed by EC-20. We conclude that the laboratory calibration equations can mitigate the effects of varying soil properties and improve the sensors’ accuracy for water content measurements. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Soft Water Level Sensors for Characterizing the Hydrological Behaviour of Agricultural Catchments

by Armand Crabit, François Colin, Jean Stéphane Bailly, Hervé Ayroles and François Garnier

Sensors 2011, 11(5), 4656-4673; doi:10.3390/s110504656

Received: 28 February 2011 / Revised: 14 March 2011 / Accepted: 16 April 2011 / Published: 28 April 2011

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Abstract

An innovative soft water level sensor is proposed to characterize the hydrological behaviour of agricultural catchments by measuring rainfall and stream flows. This sensor works as a capacitor coupled with a capacitance to frequency converter and measures water level at an adjustable time

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An innovative soft water level sensor is proposed to characterize the hydrological behaviour of agricultural catchments by measuring rainfall and stream flows. This sensor works as a capacitor coupled with a capacitance to frequency converter and measures water level at an adjustable time step acquisition. It was designed to be handy, minimally invasive and optimized in terms of energy consumption and low-cost fabrication so as to multiply its use on several catchments under natural conditions. It was used as a stage recorder to measure water level dynamics in a channel during a runoff event and as a rain gauge to measure rainfall amount and intensity. Based on the Manning equation, a method allowed estimation of water discharge with a given uncertainty and hence runoff volume at an event or annual scale. The sensor was tested under controlled conditions in the laboratory and under real conditions in the field. Comparisons of the sensor to reference devices (tipping bucket rain gauge, hydrostatic pressure transmitter limnimeter, Venturi channels…) showed accurate results: rainfall intensities and dynamic responses were accurately reproduced and discharges were estimated with an uncertainty usually acceptable in hydrology. Hence, it was used to monitor eleven small agricultural catchments located in the Mediterranean region. Both catchment reactivity and water budget have been calculated. Dynamic response of the catchments has been studied at the event scale through the rising time determination and at the annual scale by calculating the frequency of occurrence of runoff events. It provided significant insight into catchment hydrological behaviour which could be useful for agricultural management perspectives involving pollutant transport, flooding event and global water balance. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle A New HLA-Based Distributed Control Architecture for Agricultural Teams of Robots in Hybrid Applications with Real and Simulated Devices or Environments

by Patricio Nebot, Joaquín Torres-Sospedra and Rafael J. Martínez

Sensors 2011, 11(4), 4385-4400; doi:10.3390/s110404385

Received: 15 February 2011 / Revised: 2 April 2011 / Accepted: 13 April 2011 / Published: 14 April 2011

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Abstract

The control architecture is one of the most important part of agricultural robotics and other robotic systems. Furthermore its importance increases when the system involves a group of heterogeneous robots that should cooperate to achieve a global goal. A new control architecture is

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The control architecture is one of the most important part of agricultural robotics and other robotic systems. Furthermore its importance increases when the system involves a group of heterogeneous robots that should cooperate to achieve a global goal. A new control architecture is introduced in this paper for groups of robots in charge of doing maintenance tasks in agricultural environments. Some important features such as scalability, code reuse, hardware abstraction and data distribution have been considered in the design of the new architecture. Furthermore, coordination and cooperation among the different elements in the system is allowed in the proposed control system. By integrating a network oriented device server Player, Java Agent Development Framework (JADE) and High Level Architecture (HLA), the previous concepts have been considered in the new architecture presented in this paper. HLA can be considered the most important part because it not only allows the data distribution and implicit communication among the parts of the system but also allows to simultaneously operate with simulated and real entities, thus allowing the use of hybrid systems in the development of applications. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Methodology for the Regulation of Boom Sprayers Operating in Circular Trajectories

by Francisco Javier Garcia-Ramos, Mariano Vidal, Antonio Boné and Alfredo Serreta

Sensors 2011, 11(4), 4295-4311; doi:10.3390/s110404295

Received: 1 February 2011 / Revised: 23 March 2011 / Accepted: 11 April 2011 / Published: 13 April 2011

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Abstract

A methodology for the regulation of boom sprayers working in circular trajectories has been developed. In this type of trajectory, the areas of the plots of land treated by the outer nozzles of the boom are treated at reduced rates, and those treated

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A methodology for the regulation of boom sprayers working in circular trajectories has been developed. In this type of trajectory, the areas of the plots of land treated by the outer nozzles of the boom are treated at reduced rates, and those treated by the inner nozzles are treated in excess. The goal of this study was to establish the methodology to determine the flow of the individual nozzles on the boom to guarantee that the dose of the product applied per surface unit is similar across the plot. This flow is a function of the position of the equipment (circular trajectory radius) and of the displacement velocity such that the treatment applied per surface unit is uniform. GPS technology was proposed as a basis to establish the position and displacement velocity of the tractor. The viability of this methodology was simulated considering two circular plots with radii of 160 m and 310 m, using three sets of equipment with boom widths of 14.5, 24.5 and 29.5 m. Data showed as increasing boom widths produce bigger errors in the surface dose applied (L/m²). Error also increases with decreasing plot surface. As an example, considering the three boom widths of 14.5, 24.5 and 29.5 m working on a circular plot with a radius of 160 m, the percentage of surface with errors in the applied surface dose greater than 5% was 30%, 58% and 65% respectively. Considering a circular plot with radius of 310 m the same errors were 8%, 22% and 31%. To obtain a uniform superficial dose two sprayer regulation alternatives have been simulated considering a 14.5 m boom: the regulation of the pressure of each nozzle and the regulation of the pressure of each boom section. The viability of implementing the proposed methodology on commercial boom sprayers using GPS antennas to establish the position and displacement velocity of the tractor was justified with a field trial in which a self-guiding commercial GPS system was used along with three precision GPS systems located in the sprayer boom. The use of an unique central GPS unit should allow the estimation of the work parameters of the boom nozzles (including those located at the boom ends) with great accuracy. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Determination of Moisture Content in Oil Palm Fruits Using a Five-Port Reflectometer

by Lee Kim Yee, Zulkifly Abbas, Mohamad Ashry Jusoh, You Kok Yeow and Cheng Ee Meng

Sensors 2011, 11(4), 4073-4085; doi:10.3390/s110404073

Received: 8 March 2011 / Revised: 5 April 2011 / Accepted: 5 April 2011 / Published: 6 April 2011

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Abstract

This paper presents the development of a PC-based microwave five-port reflectometer for the determination of moisture content in oil palm fruits. The reflectometer was designed to measure both the magnitude and phase of the reflection coefficient of any passive microwave device. The stand-alone

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This paper presents the development of a PC-based microwave five-port reflectometer for the determination of moisture content in oil palm fruits. The reflectometer was designed to measure both the magnitude and phase of the reflection coefficient of any passive microwave device. The stand-alone reflectometer consists of a PC, a microwave source, diode detectors and an analog to digital converter. All the measurement and data acquisition were done using Agilent VEE graphical programming software. The relectometer can be used with any reflection based microwave sensor. In this work, the application of the reflectometer as a useful instrument to determine the moisture content in oil palm fruits using monopole and coaxial sensors was demonstrated. Calibration equations between reflection coefficients and moisture content have been established for both sensors. The equation based on phase measurement of monopole sensor was found to be accurate within 5% in predicting moisture content in the fruits when compared to the conventional oven drying method. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle A New Approach to Visual-Based Sensory System for Navigation into Orange Groves

by Joaquin Torres-Sospedra and Patricio Nebot

Sensors 2011, 11(4), 4086-4103; doi:10.3390/s110404086

Received: 6 February 2011 / Revised: 30 March 2011 / Accepted: 31 March 2011 / Published: 6 April 2011

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Abstract

One of the most important parts of an autonomous robot is to establish the path by which it should navigate in order to successfully achieve its goals. In the case of agricultural robotics, a procedure that determines this desired path can be useful.

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One of the most important parts of an autonomous robot is to establish the path by which it should navigate in order to successfully achieve its goals. In the case of agricultural robotics, a procedure that determines this desired path can be useful. In this paper, a new virtual sensor is introduced in order to classify the elements of an orange grove. This proposed sensor will be based on a color CCD camera with auto iris lens which is in charge of doing the captures of the real environment and an ensemble of neural networks which processes the capture and differentiates each element of the image. Then, the Hough’s transform and other operations will be applied in order to extract the desired path from the classification performed by the virtual sensory system. With this approach, the robotic system can correct its deviation with respect to the desired path. The results show that the sensory system properly classifies the elements of the grove and can set trajectory of the robot. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Three-Dimensional Modeling of Tea-Shoots Using Images and Models

by Jian Wang, Xianyin Zeng and Jianbing Liu

Sensors 2011, 11(4), 3803-3815; doi:10.3390/s110403803

Received: 3 February 2011 / Revised: 3 March 2011 / Accepted: 21 March 2011 / Published: 29 March 2011

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Abstract

In this paper, a method for three-dimensional modeling of tea-shoots with images and calculation models is introduced. The process is as follows: the tea shoots are photographed with a camera, color space conversion is conducted, using an improved algorithm that is based on

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In this paper, a method for three-dimensional modeling of tea-shoots with images and calculation models is introduced. The process is as follows: the tea shoots are photographed with a camera, color space conversion is conducted, using an improved algorithm that is based on color and regional growth to divide the tea shoots in the images, and the edges of the tea shoots extracted with the help of edge detection; after that, using the divided tea-shoot images, the three-dimensional coordinates of the tea shoots are worked out and the feature parameters extracted, matching and calculation conducted according to the model database, and finally the three-dimensional modeling of tea-shoots is completed. According to the experimental results, this method can avoid a lot of calculations and has better visual effects and, moreover, performs better in recovering the three-dimensional information of the tea shoots, thereby providing a new method for monitoring the growth of and non-destructive testing of tea shoots. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Hyperspectral and Chlorophyll Fluorescence Imaging to Analyse the Impact of Fusarium culmorum on the Photosynthetic Integrity of Infected Wheat Ears

by Elke Bauriegel, Antje Giebel and Werner B. Herppich

Sensors 2011, 11(4), 3765-3779; doi:10.3390/s110403765

Received: 24 January 2011 / Revised: 23 March 2011 / Accepted: 25 March 2011 / Published: 28 March 2011

Cited by 33 | PDF Full-text (612 KB) | HTML Full-text | XML Full-text

Abstract

Head blight on wheat, caused by Fusarium spp., is a serious problem for both farmers and food production due to the concomitant production of highly toxic mycotoxins in infected cereals. For selective mycotoxin analyses, information about the on-field status of infestation would be

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Head blight on wheat, caused by Fusarium spp., is a serious problem for both farmers and food production due to the concomitant production of highly toxic mycotoxins in infected cereals. For selective mycotoxin analyses, information about the on-field status of infestation would be helpful. Early symptom detection directly on ears, together with the corresponding geographic position, would be important for selective harvesting. Hence, the capabilities of various digital imaging methods to detect head blight disease on winter wheat were tested. Time series of images of healthy and artificially Fusarium-infected ears were recorded with a laboratory hyperspectral imaging system (wavelength range: 400 nm to 1,000 nm). Disease-specific spectral signatures were evaluated with an imaging software. Applying the ‘Spectral Angle Mapper’ method, healthy and infected ear tissue could be clearly classified. Simultaneously, chlorophyll fluorescence imaging of healthy and infected ears, and visual rating of the severity of disease was performed. Between six and eleven days after artificial inoculation, photosynthetic efficiency of infected compared to healthy ears decreased. The severity of disease highly correlated with photosynthetic efficiency. Above an infection limit of 5% severity of disease, chlorophyll fluorescence imaging reliably recognised infected ears. With this technique, differentiation of the severity of disease was successful in steps of 10%. Depending on the quality of chosen regions of interests, hyperspectral imaging readily detects head blight 7 d after inoculation up to a severity of disease of 50%. After beginning of ripening, healthy and diseased ears were hardly distinguishable with the evaluated methods. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Performance of an Ultrasonic Ranging Sensor in Apple Tree Canopies

by Alexandre Escolà, Santiago Planas, Joan Ramon Rosell, Jesús Pomar, Ferran Camp, Francesc Solanelles, Felip Gracia, Jordi Llorens and Emilio Gil

Sensors 2011, 11(3), 2459-2477; doi:10.3390/s110302459

Received: 13 December 2010 / Revised: 19 January 2011 / Accepted: 14 February 2011 / Published: 28 February 2011

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Abstract

Electronic canopy characterization is an important issue in tree crop management. Ultrasonic and optical sensors are the most used for this purpose. The objective of this work was to assess the performance of an ultrasonic sensor under laboratory and field conditions in order

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Electronic canopy characterization is an important issue in tree crop management. Ultrasonic and optical sensors are the most used for this purpose. The objective of this work was to assess the performance of an ultrasonic sensor under laboratory and field conditions in order to provide reliable estimations of distance measurements to apple tree canopies. To this purpose, a methodology has been designed to analyze sensor performance in relation to foliage ranging and to interferences with adjacent sensors when working simultaneously. Results show that the average error in distance measurement using the ultrasonic sensor in laboratory conditions is ±0.53 cm. However, the increase of variability in field conditions reduces the accuracy of this kind of sensors when estimating distances to canopies. The average error in such situations is ±5.11 cm. When analyzing interferences of adjacent sensors 30 cm apart, the average error is ±17.46 cm. When sensors are separated 60 cm, the average error is ±9.29 cm. The ultrasonic sensor tested has been proven to be suitable to estimate distances to the canopy in field conditions when sensors are 60 cm apart or more and could, therefore, be used in a system to estimate structural canopy parameters in precision horticulture. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Accuracy and Feasibility of Optoelectronic Sensors for Weed Mapping in Wide Row Crops

by Dionisio Andújar, Ángela Ribeiro, César Fernández-Quintanilla and José Dorado

Sensors 2011, 11(3), 2304-2318; doi:10.3390/s110302304

Received: 20 December 2010 / Revised: 28 January 2011 / Accepted: 10 February 2011 / Published: 24 February 2011

Cited by 19 | PDF Full-text (309 KB) | HTML Full-text | XML Full-text

Abstract

The main objectives of this study were to assess the accuracy of a ground-based weed mapping system that included optoelectronic sensors for weed detection, and to determine the sampling resolution required for accurate weed maps in maize crops. The optoelectronic sensors were located

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The main objectives of this study were to assess the accuracy of a ground-based weed mapping system that included optoelectronic sensors for weed detection, and to determine the sampling resolution required for accurate weed maps in maize crops. The optoelectronic sensors were located in the inter-row area of maize to distinguish weeds against soil background. The system was evaluated in three maize fields in the early spring. System verification was performed with highly reliable data from digital images obtained in a regular 12 m × 12 m grid throughout the three fields. The comparison in all these sample points showed a good relationship (83% agreement on average) between the data of weed presence/absence obtained from the optoelectronic mapping system and the values derived from image processing software (“ground truth”). Regarding the optimization of sampling resolution, the comparison between the detailed maps (all crop rows with sensors separated 0.75 m) with maps obtained with various simulated distances between sensors (from 1.5 m to 6.0 m) indicated that a 4.5 m distance (equivalent to one in six crop rows) would be acceptable to construct accurate weed maps. This spatial resolution makes the system cheap and robust enough to generate maps of inter-row weeds. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle 3-D Modeling of Tomato Canopies Using a High-Resolution Portable Scanning Lidar for Extracting Structural Information

by Fumiki Hosoi, Kazushige Nakabayashi and Kenji Omasa

Sensors 2011, 11(2), 2166-2174; doi:10.3390/s110202166

Received: 31 December 2010 / Revised: 31 January 2011 / Accepted: 2 February 2011 / Published: 15 February 2011

Cited by 29 | PDF Full-text (535 KB) | HTML Full-text | XML Full-text

Abstract

In the present study, an attempt was made to produce a precise 3D image of a tomato canopy using a portable high-resolution scanning lidar. The tomato canopy was scanned by the lidar from three positions surrounding it. Through the scanning, the point cloud

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In the present study, an attempt was made to produce a precise 3D image of a tomato canopy using a portable high-resolution scanning lidar. The tomato canopy was scanned by the lidar from three positions surrounding it. Through the scanning, the point cloud data of the canopy were obtained and they were co-registered. Then, points corresponding to leaves were extracted and converted into polygon images. From the polygon images, leaf areas were accurately estimated with a mean absolute percent error of 4.6%. Vertical profile of leaf area density (LAD) and leaf area index (LAI) could be also estimated by summing up each leaf area derived from the polygon images. Leaf inclination angle could be also estimated from the 3-D polygon image. It was shown that leaf inclination angles had different values at each part of a leaf. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle A Stereovision Matching Strategy for Images Captured with Fish-Eye Lenses in Forest Environments

by Pedro Javier Herrera, Gonzalo Pajares, María Guijarro, José J. Ruz and Jesús M. Cruz

Sensors 2011, 11(2), 1756-1783; doi:10.3390/s110201756

Received: 21 December 2010 / Revised: 12 January 2011 / Accepted: 27 January 2011 / Published: 31 January 2011

Cited by 8 | PDF Full-text (673 KB) | HTML Full-text | XML Full-text

Abstract

We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. This is achieved by applying a

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We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. This is achieved by applying a pattern recognition strategy based on the combination of two classifiers: Fuzzy Clustering and Bayesian. At a second stage, a stereovision matching process is performed based on the application of four stereovision matching constraints: epipolar, similarity, uniqueness and smoothness. The epipolar constraint guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a weighted fuzzy similarity approach, obtaining a disparity map. This map is later filtered through the Hopfield Neural Network framework by considering the smoothness constraint. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Developing a New Wireless Sensor Network Platform and Its Application in Precision Agriculture

by Raúl Aquino-Santos, Apolinar González-Potes, Arthur Edwards-Block and Raúl Alejandro Virgen-Ortiz

Sensors 2011, 11(1), 1192-1211; doi:10.3390/s110101192

Received: 20 December 2010 / Revised: 7 January 2011 / Accepted: 18 January 2011 / Published: 20 January 2011

Cited by 22 | PDF Full-text (1042 KB) | HTML Full-text | XML Full-text

Abstract

Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of “smart dust” offer great advantages due to their small size, low power consumption, easy integration and support for “green” applications. Green applications are considered

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Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of “smart dust” offer great advantages due to their small size, low power consumption, easy integration and support for “green” applications. Green applications are considered a hot topic in intelligent environments, ubiquitous and pervasive computing. This work evaluates a new wireless sensor network platform and its application in precision agriculture, including its embedded operating system and its routing algorithm. To validate the technological platform and the embedded operating system, two different routing strategies were compared: hierarchical and flat. Both of these routing algorithms were tested in a small-scale network applied to a watermelon field. However, we strongly believe that this technological platform can be also applied to precision agriculture because it incorporates a modified version of LORA-CBF, a wireless location-based routing algorithm that uses cluster-based flooding. Cluster-based flooding addresses the scalability concerns of wireless sensor networks, while the modified LORA-CBF routing algorithm includes a metric to monitor residual battery energy. Furthermore, results show that the modified version of LORA-CBF functions well with both the flat and hierarchical algorithms, although it functions better with the flat algorithm in a small-scale agricultural network. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Soil pH Mapping with an On-The-Go Sensor

by Michael Schirrmann, Robin Gebbers, Eckart Kramer and Jan Seidel

Sensors 2011, 11(1), 573-598; doi:10.3390/s110100573

Received: 11 November 2010 / Revised: 23 December 2010 / Accepted: 29 December 2010 / Published: 7 January 2011

Cited by 20 | PDF Full-text (2267 KB) | HTML Full-text | XML Full-text

Abstract

Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager^TM, a sensor for high-resolution mapping of soil pH at the field scale, has been

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Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager^TM, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager^TM under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager^TM were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r²) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Using Eddy Covariance Sensors to Quantify Carbon Metabolism of Peatlands: A Case Study in Turkey

by Fatih Evrendilek, Nusret Karakaya, Guler Aslan and Can Ertekin

Sensors 2011, 11(1), 522-538; doi:10.3390/s110100522

Received: 4 November 2010 / Revised: 23 November 2010 / Accepted: 4 January 2011 / Published: 6 January 2011

Cited by 6 | PDF Full-text (392 KB) | HTML Full-text | XML Full-text

Abstract

Net ecosystem exchange (NEE) of carbon dioxide (CO₂) was measured in a cool temperate peatland in northwestern Turkey on a continuous basis using eddy covariance (EC) sensors and multiple (non-)linear regression-M(N)LR-models. Our results showed that hourly NEE varied between −1.26 and

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Net ecosystem exchange (NEE) of carbon dioxide (CO₂) was measured in a cool temperate peatland in northwestern Turkey on a continuous basis using eddy covariance (EC) sensors and multiple (non-)linear regression-M(N)LR-models. Our results showed that hourly NEE varied between −1.26 and 1.06 mg CO₂ m⁻² s⁻¹, with a mean value of 0.11 mg CO₂ m⁻² s⁻¹. Nighttime ecosystem respiration (R_E) was on average measured as 0.23 ± 0.09 mg CO₂ m⁻² s⁻¹. Two best-fit M(N)LR models estimated daytime R_E as 0.64 ± 0.31 and 0.24 ± 0.05 mg CO₂ m⁻² s⁻¹. Total R_E as the sum of nighttime and daytime R_E ranged from 0.47 to 0.87 mg CO₂ m⁻² s⁻¹, thus yielding estimates of gross primary productivity (GPP) at −0.35 ± 0.18 and −0.74 ± 0.43 mg CO₂ m⁻² s⁻¹. Use of EC sensors and M(N)LR models is one of the most direct ways to quantify turbulent CO₂ exchanges among the soil, vegetation and atmosphere within the atmospheric boundary layer, as well as source and sink behaviors of ecosystems. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle A Solar Energy Powered Autonomous Wireless Actuator Node for Irrigation Systems

by Rafael Lajara, Jorge Alberola and José Pelegrí-Sebastiá

Sensors 2011, 11(1), 329-340; doi:10.3390/s110100329

Received: 16 November 2010 / Revised: 7 December 2010 / Accepted: 28 December 2010 / Published: 30 December 2010

Cited by 12 | PDF Full-text (307 KB) | HTML Full-text | XML Full-text

Abstract

The design of a fully autonomous and wireless actuator node (“wEcoValve mote”) based on the IEEE 802.15.4 standard is presented. The system allows remote control (open/close) of a 3-lead magnetic latch solenoid, commonly used in drip irrigation systems in applications such as agricultural

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The design of a fully autonomous and wireless actuator node (“wEcoValve mote”) based on the IEEE 802.15.4 standard is presented. The system allows remote control (open/close) of a 3-lead magnetic latch solenoid, commonly used in drip irrigation systems in applications such as agricultural areas, greenhouses, gardens, etc. The very low power consumption of the system in conjunction with the low power consumption of the valve, only when switching positions, allows the system to be solar powered, thus eliminating the need of wires and facilitating its deployment. By using supercapacitors recharged from a specifically designed solar power module, the need to replace batteries is also eliminated and the system is completely autonomous and maintenance free. The “wEcoValve mote” firmware is based on a synchronous protocol that allows a bidirectional communication with a latency optimized for real-time work, with a synchronization time between nodes of 4 s, thus achieving a power consumption average of 2.9 mW. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Estimation of Aboveground Biomass in Alpine Forests: A Semi-Empirical Approach Considering Canopy Transparency Derived from Airborne LiDAR Data

by Andreas Jochem, Markus Hollaus, Martin Rutzinger and Bernhard Höfle

Sensors 2011, 11(1), 278-295; doi:10.3390/s110100278

Received: 29 October 2010 / Revised: 1 December 2010 / Accepted: 23 December 2010 / Published: 29 December 2010

Cited by 21 | PDF Full-text (3794 KB) | HTML Full-text | XML Full-text

Abstract

In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means

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In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can be measured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R2 (R2 = 0.70 to R2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Characterization of Buoyant Fluorescent Particles for Field Observations of Water Flows

by Flavia Tauro, Matteo Aureli, Maurizio Porfiri and Salvatore Grimaldi

Sensors 2010, 10(12), 11512-11529; doi:10.3390/s101211512

Received: 9 October 2010 / Revised: 10 November 2010 / Accepted: 11 November 2010 / Published: 15 December 2010

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Abstract

In this paper, the feasibility of off-the-shelf buoyant fluorescent microspheres as particle tracers in turbid water flows is investigated. Microspheres’ fluorescence intensity is experimentally measured and detected in placid aqueous suspensions of increasing concentrations of clay to simulate typical conditions occurring in natural

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In this paper, the feasibility of off-the-shelf buoyant fluorescent microspheres as particle tracers in turbid water flows is investigated. Microspheres’ fluorescence intensity is experimentally measured and detected in placid aqueous suspensions of increasing concentrations of clay to simulate typical conditions occurring in natural drainage networks. Experiments are conducted in a broad range of clay concentrations and particle immersion depths by using photoconductive cells and image-based sensing technologies. Results obtained with both methodologies exhibit comparable trends and show that the considered particles are fairly detectable in critically turbid water flows. Further information on performance and integration of the studied microspheres in low-cost measurement instrumentation for field observations is obtained through experiments conducted in a custom built miniature water channel. This experimental characterization provides a first assessment of the feasibility of commercially available buoyant fluorescent beads in the analysis of high turbidity surface water flows. The proposed technology may serve as a minimally invasive sensing system for hazardous events, such as pollutant diffusion in natural streams and flash flooding due to extreme rainfall. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Sensor Architecture and Task Classification for Agricultural Vehicles and Environments

by Francisco Rovira-Más

Sensors 2010, 10(12), 11226-11247; doi:10.3390/s101211226

Received: 20 October 2010 / Revised: 26 November 2010 / Accepted: 1 December 2010 / Published: 8 December 2010

Cited by 14 | PDF Full-text (1176 KB) | HTML Full-text | XML Full-text

Abstract

The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently

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The long time wish of endowing agricultural vehicles with an increasing degree of autonomy is becoming a reality thanks to two crucial facts: the broad diffusion of global positioning satellite systems and the inexorable progress of computers and electronics. Agricultural vehicles are currently the only self-propelled ground machines commonly integrating commercial automatic navigation systems. Farm equipment manufacturers and satellite-based navigation system providers, in a joint effort, have pushed this technology to unprecedented heights; yet there are many unresolved issues and an unlimited potential still to uncover. The complexity inherent to intelligent vehicles is rooted in the selection and coordination of the optimum sensors, the computer reasoning techniques to process the acquired data, and the resulting control strategies for automatic actuators. The advantageous design of the network of onboard sensors is necessary for the future deployment of advanced agricultural vehicles. This article analyzes a variety of typical environments and situations encountered in agricultural fields, and proposes a sensor architecture especially adapted to cope with them. The strategy proposed groups sensors into four specific subsystems: global localization, feedback control and vehicle pose, non-visual monitoring, and local perception. The designed architecture responds to vital vehicle tasks classified within three layers devoted to safety, operative information, and automatic actuation. The success of this architecture, implemented and tested in various agricultural vehicles over the last decade, rests on its capacity to integrate redundancy and incorporate new technologies in a practical way. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Design and Implementation of a GPS Guidance System for Agricultural Tractors Using Augmented Reality Technology

by Javier Santana-Fernández, Jaime Gómez-Gil and Laura Del-Pozo-San-Cirilo

Sensors 2010, 10(11), 10435-10447; doi:10.3390/s101110435

Received: 28 October 2010 / Revised: 10 November 2010 / Accepted: 11 November 2010 / Published: 18 November 2010

Cited by 6 | PDF Full-text (1192 KB) | HTML Full-text | XML Full-text

Abstract

Current commercial tractor guidance systems present to the driver information to perform agricultural tasks in the best way. This information generally includes a treated zones map referenced to the tractor’s position. Unlike actual guidance systems where the tractor driver must mentally associate treated

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Current commercial tractor guidance systems present to the driver information to perform agricultural tasks in the best way. This information generally includes a treated zones map referenced to the tractor’s position. Unlike actual guidance systems where the tractor driver must mentally associate treated zone maps and the plot layout, this paper presents a guidance system that using Augmented Reality (AR) technology, allows the tractor driver to see the real plot though eye monitor glasses with the treated zones in a different color. The paper includes a description of the system hardware and software, a real test done with image captures seen by the tractor driver, and a discussion predicting that the historical evolution of guidance systems could involve the use of AR technology in the agricultural guidance and monitoring systems. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Performance Evaluation of an Infrared Thermocouple

by Chiachung Chen, Yu-Kai Weng and Te-Ching Shen

Sensors 2010, 10(11), 10081-10094; doi:10.3390/s101110081

Received: 28 October 2010 / Revised: 2 November 2010 / Accepted: 8 November 2010 / Published: 10 November 2010

Cited by 3 | PDF Full-text (209 KB) | HTML Full-text | XML Full-text

Abstract

The measurement of the leaf temperature of forests or agricultural plants is an important technique for the monitoring of the physiological state of crops. The infrared thermometer is a convenient device due to its fast response and nondestructive measurement technique. Nowadays, a novel

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The measurement of the leaf temperature of forests or agricultural plants is an important technique for the monitoring of the physiological state of crops. The infrared thermometer is a convenient device due to its fast response and nondestructive measurement technique. Nowadays, a novel infrared thermocouple, developed with the same measurement principle of the infrared thermometer but using a different detector, has been commercialized for non-contact temperature measurement. The performances of two-kinds of infrared thermocouples were evaluated in this study. The standard temperature was maintained by a temperature calibrator and a special black cavity device. The results indicated that both types of infrared thermocouples had good precision. The error distribution ranged from −1.8 °C to 18 °C as the reading values served as the true values. Within the range from 13 °C to 37 °C, the adequate calibration equations were the high-order polynomial equations. Within the narrower range from 20 °C to 35 °C, the adequate equation was a linear equation for one sensor and a two-order polynomial equation for the other sensor. The accuracy of the two kinds of infrared thermocouple was improved by nearly 0.4 °C with the calibration equations. These devices could serve as mobile monitoring tools for in situ and real time routine estimation of leaf temperatures. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra

by Vicent Gasso-Tortajada, Alastair J. Ward, Hasib Mansur, Torben Brøchner, Claus G. Sørensen and Ole Green

Sensors 2010, 10(11), 10027-10039; doi:10.3390/s101110027

Received: 14 September 2010 / Revised: 1 November 2010 / Accepted: 5 November 2010 / Published: 9 November 2010

Cited by 2 | PDF Full-text (1539 KB) | HTML Full-text | XML Full-text

Abstract

A non-destructive and novel in situ acoustic sensor approach based on the sound absorption spectra was developed for identifying and classifying different seed types. The absorption coefficient spectra were determined by using the impedance tube measurement method. Subsequently, a multivariate statistical analysis, i.e

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A non-destructive and novel in situ acoustic sensor approach based on the sound absorption spectra was developed for identifying and classifying different seed types. The absorption coefficient spectra were determined by using the impedance tube measurement method. Subsequently, a multivariate statistical analysis, i.e., principal component analysis (PCA), was performed as a way to generate a classification of the seeds based on the soft independent modelling of class analogy (SIMCA) method. The results show that the sound absorption coefficient spectra of different seed types present characteristic patterns which are highly dependent on seed size and shape. In general, seed particle size and sphericity were inversely related with the absorption coefficient. PCA presented reliable grouping capabilities within the diverse seed types, since the 95% of the total spectral variance was described by the first two principal components. Furthermore, the SIMCA classification model based on the absorption spectra achieved optimal results as 100% of the evaluation samples were correctly classified. This study contains the initial structuring of an innovative method that will present new possibilities in agriculture and industry for classifying and determining physical properties of seeds and other materials. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessArticle Non-Destructive Optical Monitoring of Grape Maturation by Proximal Sensing

by Naïma Ben Ghozlen, Zoran G. Cerovic, Claire Germain, Sandrine Toutain and Gwendal Latouche

Sensors 2010, 10(11), 10040-10068; doi:10.3390/s101110040

Received: 13 September 2010 / Revised: 19 October 2010 / Accepted: 26 October 2010 / Published: 9 November 2010

Cited by 63 | PDF Full-text (670 KB) | HTML Full-text | XML Full-text | Supplementary Files

Abstract

A new, commercial, fluorescence-based optical sensor for plant constituent assessment was recently introduced. This sensor, called the Multiplex^® (FORCE-A, Orsay, France), was used to monitor grape maturation by specifically monitoring anthocyanin accumulation. We derived the empirical anthocyanin content calibration curves for Champagne

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A new, commercial, fluorescence-based optical sensor for plant constituent assessment was recently introduced. This sensor, called the Multiplex^® (FORCE-A, Orsay, France), was used to monitor grape maturation by specifically monitoring anthocyanin accumulation. We derived the empirical anthocyanin content calibration curves for Champagne red grape cultivars, and we also propose a general model for the influence of the proportion of red berries, skin anthocyanin content and berry size on Multiplex^® indices. The Multiplex^® was used on both berry samples in the laboratory and on intact clusters in the vineyard. We found that the inverted and log-transformed far-red fluorescence signal called the FERARI index, although sensitive to sample size and distance, is potentially the most widely applicable. The more robust indices, based on chlorophyll fluorescence excitation ratios, showed three ranges of dependence on anthocyanin content. We found that up to 0.16 mg cm⁻², equivalent to approximately 0.6 mg g⁻¹, all indices increase with accumulation of skin anthocyanin content. Excitation ratio-based indices decrease with anthocyanin accumulation beyond 0.27 mg cm⁻². We showed that the Multiplex^® can be advantageously used in vineyards on intact clusters for the non-destructive assessment of anthocyanin content of vine blocks and can now be tested on other fruits and vegetables based on the same model. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Multitemporal Observations of Sugarcane by TerraSAR-X Images

by Nicolas Baghdadi, Rémi Cresson, Pierre Todoroff and Soizic Moinet

Sensors 2010, 10(10), 8899-8919; doi:10.3390/s101008899

Received: 21 July 2010 / Revised: 14 September 2010 / Accepted: 19 September 2010 / Published: 28 September 2010

Cited by 24 | PDF Full-text (527 KB) | HTML Full-text | XML Full-text

Abstract

The objective of this study is to investigate the potential of TerraSAR-X (X-band) in monitoring sugarcane growth on Reunion Island (located in the Indian Ocean). Multi-temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV)

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The objective of this study is to investigate the potential of TerraSAR-X (X-band) in monitoring sugarcane growth on Reunion Island (located in the Indian Ocean). Multi-temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV) were analyzed in order to study the behaviour of SAR (synthetic aperture radar) signal as a function of sugarcane height and NDVI (Normalized Difference Vegetation Index). The potential of TerraSAR for mapping the sugarcane harvest was also studied. Radar signal increased quickly with crop height until a threshold height, which depended on polarization and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is slightly higher with cross polarization and higher incidence angles (47° in comparison with 17° and 31°). Results also showed that the co-polarizations channels (HH and VV) were well correlated. High correlation between SAR signal and NDVI calculated from SPOT-4/5 images was observed. TerraSAR data showed that after strong rains the soil contribution to the backscattering of sugarcane fields can be important for canes with heights of terminal visible dewlap (htvd) less than 50 cm (total cane heights around 155 cm). This increase in radar signal after strong rains could involve an ambiguity between young and mature canes. Indeed, the radar signal on TerraSAR images acquired in wet soil conditions could be of the same order for fields recently harvested and mature sugarcane fields, making difficult the detection of cuts. Finally, TerraSAR data at high spatial resolution were shown to be useful for monitoring sugarcane harvest when the fields are of small size or when the cut is spread out in time. The comparison between incidence angles of 17°, 37° and 58° shows that 37° is more suitable to monitor the sugarcane harvest. The cut is easily detectable on TerraSAR images for data acquired less than two or three months after the cut. The radar signal decreases about 5dB for images acquired some days after the cut and 3 dB for data acquired two month after the cut (VV-37°). The difference in radar signal becomes negligible ( Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle FPGA-based Fused Smart Sensor for Real-Time Plant-Transpiration Dynamic Estimation

by Jesus Roberto Millan-Almaraz, Rene de Jesus Romero-Troncoso, Ramon Gerardo Guevara-Gonzalez, Luis Miguel Contreras-Medina, Roberto Valentin Carrillo-Serrano, Roque Alfredo Osornio-Rios, Carlos Duarte-Galvan, Miguel Angel Rios-Alcaraz and Irineo Torres-Pacheco

Sensors 2010, 10(9), 8316-8331; doi:10.3390/s100908316

Received: 21 July 2010 / Revised: 6 August 2010 / Accepted: 20 August 2010 / Published: 2 September 2010

Cited by 9 | PDF Full-text (593 KB) | HTML Full-text | XML Full-text

Abstract

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are

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Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Open AccessArticle Using Automated Point Dendrometers to Analyze Tropical Treeline Stem Growth at Nevado de Colima, Mexico

by Franco Biondi and Peter Hartsough

Sensors 2010, 10(6), 5827-5844; doi:10.3390/s100605827

Received: 10 May 2010 / Revised: 2 June 2010 / Accepted: 4 June 2010 / Published: 9 June 2010

Cited by 21 | PDF Full-text (416 KB) | HTML Full-text | XML Full-text

Abstract

The relationship between wood growth and environmental variability at the tropical treeline of North America was investigated using automated, solar-powered sensors (a meteorological station and two dendrometer clusters) installed on Nevado de Colima, Mexico (19° 35’ N, 103° 37’ W, 3,760 m a.s.l.).

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The relationship between wood growth and environmental variability at the tropical treeline of North America was investigated using automated, solar-powered sensors (a meteorological station and two dendrometer clusters) installed on Nevado de Colima, Mexico (19° 35’ N, 103° 37’ W, 3,760 m a.s.l.). Pure stands of Pinus hartwegii Lindl. (Mexican mountain pine) were targeted because of their suitability for tree-ring analysis in low-latitude, high-elevation, North American Monsoon environments. Stem size and hydroclimatic variables recorded at half-hour intervals were summarized on a daily timescale. Power outages, insect outbreaks, and sensor failures limited the analysis to non-consecutive months during 2001–2003 at one dendrometer site, and during 2002–2005 at the other. Combined data from the two sites showed that maximum radial growth rates occur in late spring (May), as soil temperature increases, and incoming short-wave radiation reaches its highest values. Early season (April–May) radial increment correlated directly with temperature, especially of the soil, and with solar radiation. Stem expansion at the start of the summer monsoon (June–July) was mostly influenced by moisture, and revealed a drought signal, while late season relationships were more varied. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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Review

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Open AccessReview Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

Sensors 2011, 11(8), 7954-7981; doi:10.3390/s110807954

Received: 18 July 2011 / Revised: 8 August 2011 / Accepted: 8 August 2011 / Published: 12 August 2011

Cited by 38 | PDF Full-text (1319 KB) | HTML Full-text | XML Full-text

Abstract

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology

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This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

Open AccessReview Geosensors to Support Crop Production: Current Applications and User Requirements

by Sirpa Thessler, Lammert Kooistra, Frederick Teye, Hanna Huitu and Arnold K. Bregt

Sensors 2011, 11(7), 6656-6684; doi:10.3390/s110706656

Received: 16 May 2011 / Revised: 16 June 2011 / Accepted: 22 June 2011 / Published: 27 June 2011

Cited by 11 | PDF Full-text (342 KB) | HTML Full-text | XML Full-text

Abstract

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have

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Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load. Full article

(This article belongs to the Special Issue Sensors in Agriculture and Forestry)

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