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Special Issue "Sensor-Based Technologies and Processes in Agriculture and Forestry"

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 January 2013)

Special Issue Editors

Guest Editor
Prof. Dr. Gonzalo Pajares Martinsanz

Department Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain
Website | E-Mail
Phone: +34.1.3947546
Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, spatio-temporal image change detection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution image sensors
Guest Editor
Prof. Dr. Andrea Peruzzi

Department of Agronomy and Agroecosystem Management, University of Pisa, Via S. Michele degli Scalzi 2, 56124 Pisa, Italy
Website | E-Mail
Fax: +39 050 2218966
Interests: innovative strategies for mechanical an thermal weed control; innovative strategies and techniques for soil disinfection; innovative machines for seed-bank control; innovative tools for mechanical weed control; innovative tools for thermal weed control; automated machines for mechanical weed control; automated machines for thermal weed control
Guest Editor
Prof. Dr. Pablo Gonzalez-de-Santos

Centre for Automation and Robotics, Spanish National Research Council - (CSIC), 28500 Arganda del Rey, Madrid, Spain
E-Mail
Interests: mobile robotics; agile locomotion; haptic interface

Special Issue Information

Dear Colleagues,

Advances in modern Agriculture and forestry are demanding solutions with the aim of increasing production or accurate inventories while reducing environmental impacts and costs. Sensors are excellent tools for achieving the above goals. They provide data, which conveniently processed, allow to extract relevant information oriented towards the best utilization of resources and also to apply hazardous products moderately.

Sensors devices capabilities, materials, technologies and tools, applications and problems addressed, sensors domain-oriented devices and methods or procedures applied for processing the data sensed can be conveniently designed for such purposes, the following is a list of main topics covered by this special issue but not limited to:

  • Sensors for physical treatments: new precision technologies, thermal and/or mechanical tools (including management of weed flora).
  • Sensors for chemical analysis or application of chemical spraying: new precision technologies, herbicides and pesticides distribution for crop protection (weed, diseases, pest control), nutrients, fertilizers, CO2 transformation, chlorophyll analysis.
  • Sensors for variable measurements: humidity, solar radiation, crop/weeds identification, canopy, greenness, soil compaction, energy consumption, fruit identification and ripeness, diameter of trees, crown height or bark thickness.
  • Sensors for obtaining 3D structures, crop rows detection or plants alignments.
  • Sensors for autonomous or guided navigation and actuation: obstacle avoidance, vehicle(s) control, localization, motion, geometry, communications, path planning or mission supervision.
  • Simulations of the above sensor technologies, methods and procedures for data processing.

Welcome will be all authors invited to submit an extended paper to this special issue from the First International Conference on Robotics and associated High-technologies and Equipment (www.rhea-conference.eu/2012). Extension of papers will be at least 50%.

PS: Topics related to Remote Sensing and Wireless Sensor Networks are excluded.

Prof. Dr. Gonzalo Pajares
Prof. Dr. Andrea Peruzzi
Prof. Dr. Pablo Gonzalez-de-Santos
Guest Editors

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs).

Related Special Issue

Published Papers (45 papers)

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Editorial

Jump to: Research, Review

Open AccessEditorial Sensors in Agriculture and Forestry
Sensors 2013, 13(9), 12132-12139; doi:10.3390/s130912132
Received: 5 September 2013 / Accepted: 9 September 2013 / Published: 10 September 2013
Cited by 5 | PDF Full-text (118 KB) | HTML Full-text | XML Full-text
Abstract
Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical
[...] Read more.
Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical practices. In addition, the immediate consequence of this “trend” is the reduction of production costs. [...] Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)

Research

Jump to: Editorial, Review

Open AccessArticle Investigation of Tree Spectral Reflectance Characteristics Using a Mobile Terrestrial Line Spectrometer and Laser Scanner
Sensors 2013, 13(7), 9305-9320; doi:10.3390/s130709305
Received: 3 June 2013 / Revised: 10 July 2013 / Accepted: 16 July 2013 / Published: 19 July 2013
Cited by 6 | PDF Full-text (1015 KB) | HTML Full-text | XML Full-text
Abstract
In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of
[...] Read more.
In mobile terrestrial hyperspectral imaging, individual trees often present large variations in spectral reflectance that may impact the relevant applications, but the related studies have been seldom reported. To fill this gap, this study was dedicated to investigating the spectral reflectance characteristics of individual trees with a Sensei mobile mapping system, which comprises a Specim line spectrometer and an Ibeo Lux laser scanner. The addition of the latter unit facilitates recording the structural characteristics of the target trees synchronously, and this is beneficial for revealing the characteristics of the spatial distributions of tree spectral reflectance with variations at different levels. Then, the parts of trees with relatively low-level variations can be extracted. At the same time, since it is difficult to manipulate the whole spectrum, the traditional concept of vegetation indices (VI) based on some particular spectral bands was taken into account here. Whether the assumed VIs capable of behaving consistently for the whole crown of each tree was also checked. The specific analyses were deployed based on four deciduous tree species and six kinds of VIs. The test showed that with the help of the laser scanner data, the parts of individual trees with relatively low-level variations can be located. Based on these parts, the relatively stable spectral reflectance characteristics for different tree species can be learnt. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Development and Testing of a Decision Making Based Method to Adjust Automatically the Harrowing Intensity
Sensors 2013, 13(5), 6254-6271; doi:10.3390/s130506254
Received: 15 March 2013 / Revised: 2 May 2013 / Accepted: 3 May 2013 / Published: 13 May 2013
Cited by 2 | PDF Full-text (3414 KB) | HTML Full-text | XML Full-text
Abstract
Harrowing is often used to reduce weed competition, generally using a constant intensity across a whole field. The efficacy of weed harrowing in wheat and barley can be optimized, if site-specific conditions of soil, weed infestation and crop growth stage are taken into
[...] Read more.
Harrowing is often used to reduce weed competition, generally using a constant intensity across a whole field. The efficacy of weed harrowing in wheat and barley can be optimized, if site-specific conditions of soil, weed infestation and crop growth stage are taken into account. This study aimed to develop and test an algorithm to automatically adjust the harrowing intensity by varying the tine angle and number of passes. The field variability of crop leaf cover, weed density and soil density was acquired with geo-referenced sensors to investigate the harrowing selectivity and crop recovery. Crop leaf cover and weed density were assessed using bispectral cameras through differential images analysis. The draught force of the soil opposite to the direction of travel was measured with electronic load cell sensor connected to a rigid tine mounted in front of the harrow. Optimal harrowing intensity levels were derived in previously implemented experiments, based on the weed control efficacy and yield gain. The assessments of crop leaf cover, weed density and soil density were combined via rules with the aforementioned optimal intensities, in a linguistic fuzzy inference system (LFIS). The system was evaluated in two field experiments that compared constant intensities with variable intensities inferred by the system. A higher weed density reduction could be achieved when the harrowing intensity was not kept constant along the cultivated plot. Varying the intensity tended to reduce the crop leaf cover, though slightly improving crop yield. A real-time intensity adjustment with this system is achievable, if the cameras are attached in the front and at the rear or sides of the harrow. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle A Wearable Mobile Sensor Platform to Assist Fruit Grading
Sensors 2013, 13(5), 6109-6140; doi:10.3390/s130506109
Received: 20 April 2013 / Revised: 24 April 2013 / Accepted: 26 April 2013 / Published: 10 May 2013
Cited by 7 | PDF Full-text (3787 KB) | HTML Full-text | XML Full-text
Abstract
Wearable computing is a form of ubiquitous computing that offers flexible and useful tools for users. Specifically, glove-based systems have been used in the last 30 years in a variety of applications, but mostly focusing on sensing people’s attributes, such as finger bending
[...] Read more.
Wearable computing is a form of ubiquitous computing that offers flexible and useful tools for users. Specifically, glove-based systems have been used in the last 30 years in a variety of applications, but mostly focusing on sensing people’s attributes, such as finger bending and heart rate. In contrast, we propose in this work a novel flexible and reconfigurable instrumentation platform in the form of a glove, which can be used to analyze and measure attributes of fruits by just pointing or touching them with the proposed glove. An architecture for such a platform is designed and its application for intuitive fruit grading is also presented, including experimental results for several fruits. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Figures

Open AccessArticle Design of a Soil Cutting Resistance Sensor for Application in Site-Specific Tillage
Sensors 2013, 13(5), 5945-5957; doi:10.3390/s130505945
Received: 3 April 2013 / Revised: 3 May 2013 / Accepted: 3 May 2013 / Published: 10 May 2013
Cited by 1 | PDF Full-text (433 KB) | HTML Full-text | XML Full-text
Abstract
One objective of precision agriculture is to provide accurate information about soil and crop properties to optimize the management of agricultural inputs to meet site-specific needs. This paper describes the development of a sensor equipped with RTK-GPS technology that continuously and efficiently measures
[...] Read more.
One objective of precision agriculture is to provide accurate information about soil and crop properties to optimize the management of agricultural inputs to meet site-specific needs. This paper describes the development of a sensor equipped with RTK-GPS technology that continuously and efficiently measures soil cutting resistance at various depths while traversing the field. Laboratory and preliminary field tests verified the accuracy of this prototype soil strength sensor. The data obtained using a hand-operated soil cone penetrometer was used to evaluate this field soil compaction depth profile sensor. To date, this sensor has only been tested in one field under one gravimetric water content condition. This field test revealed that the relationships between the soil strength profile sensor (SSPS) cutting force and soil cone index values are assumed to be quadratic for the various depths considered: 0–10, 10–20 and 20–30 cm (r2 = 0.58, 0.45 and 0.54, respectively). Soil resistance contour maps illustrated its practical value. The developed sensor provides accurate, timely and affordable information on soil properties to optimize resources and improve agricultural economy. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Seedling Discrimination with Shape Features Derived from a Distance Transform
Sensors 2013, 13(5), 5585-5602; doi:10.3390/s130505585
Received: 6 March 2013 / Revised: 4 April 2013 / Accepted: 8 April 2013 / Published: 26 April 2013
Cited by 7 | PDF Full-text (1131 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this research is an improvement of plant seedling recognition by two new approaches of shape feature generation based on plant silhouettes. Experiments show that the proposed feature sets possess value in plant recognition when compared with other feature sets. Both
[...] Read more.
The aim of this research is an improvement of plant seedling recognition by two new approaches of shape feature generation based on plant silhouettes. Experiments show that the proposed feature sets possess value in plant recognition when compared with other feature sets. Both methods approximate a distance distribution of an object, either by resampling or by approximation of the distribution with a high degree Legendre polynomial. In the latter case, the polynomial coefficients constitute a feature set. The methods have been tested through a discrimination process where two similar plant species are to be distinguished into their respective classes. The used performance assessment is based on the classification accuracy of 4 different classifiers (a k-Nearest Neighbor, Naive-Bayes, Linear Support Vector Machine, Nonlinear Support Vector Machine). Another set of 21 well-known shape features described in the literature is used for comparison. The used data consisted of 139 samples of cornflower (Centaura cyanus L.) and 63 samples of nightshade (Solanum nigrum L.). The highest discrimination accuracy was achieved with the Legendre Polynomial feature set and amounted to 97.5%. This feature set consisted of 10 numerical values. Another feature set consisting of 21 common features achieved an accuracy of 92.5%. The results suggest that the Legendre Polynomial feature set can compete with or outperform the commonly used feature sets. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Camera Sensor Arrangement for Crop/Weed Detection Accuracy in Agronomic Images
Sensors 2013, 13(4), 4348-4366; doi:10.3390/s130404348
Received: 28 January 2013 / Revised: 25 March 2013 / Accepted: 27 March 2013 / Published: 2 April 2013
Cited by 8 | PDF Full-text (1594 KB) | HTML Full-text | XML Full-text
Abstract
In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing.
[...] Read more.
In Precision Agriculture, images coming from camera-based sensors are commonly used for weed identification and crop line detection, either to apply specific treatments or for vehicle guidance purposes. Accuracy of identification and detection is an important issue to be addressed in image processing. There are two main types of parameters affecting the accuracy of the images, namely: (a) extrinsic, related to the sensor’s positioning in the tractor; (b) intrinsic, related to the sensor specifications, such as CCD resolution, focal length or iris aperture, among others. Moreover, in agricultural applications, the uncontrolled illumination, existing in outdoor environments, is also an important factor affecting the image accuracy. This paper is exclusively focused on two main issues, always with the goal to achieve the highest image accuracy in Precision Agriculture applications, making the following two main contributions: (a) camera sensor arrangement, to adjust extrinsic parameters and (b) design of strategies for controlling the adverse illumination effects. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessCommunication A Small and Slim Coaxial Probe for Single Rice Grain Moisture Sensing
Sensors 2013, 13(3), 3652-3663; doi:10.3390/s130303652
Received: 28 December 2012 / Revised: 7 March 2013 / Accepted: 8 March 2013 / Published: 14 March 2013
Cited by 3 | PDF Full-text (497 KB) | HTML Full-text | XML Full-text
Abstract
A moisture detection of single rice grains using a slim and small open-ended coaxial probe is presented. The coaxial probe is suitable for the nondestructive measurement of moisture values in the rice grains ranging from from 9.5% to 26%. Empirical polynomial models are
[...] Read more.
A moisture detection of single rice grains using a slim and small open-ended coaxial probe is presented. The coaxial probe is suitable for the nondestructive measurement of moisture values in the rice grains ranging from from 9.5% to 26%. Empirical polynomial models are developed to predict the gravimetric moisture content of rice based on measured reflection coefficients using a vector network analyzer. The relationship between the reflection coefficient and relative permittivity were also created using a regression method and expressed in a polynomial model, whose model coefficients were obtained by fitting the data from Finite Element-based simulation. Besides, the designed single rice grain sample holder and experimental set-up were shown. The measurement of single rice grains in this study is more precise compared to the measurement in conventional bulk rice grains, as the random air gap present in the bulk rice grains is excluded. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Using a Standing-Tree Acoustic Tool to Identify Forest Stands for the Production of Mechanically-Graded Lumber
Sensors 2013, 13(3), 3394-3408; doi:10.3390/s130303394
Received: 25 January 2013 / Revised: 22 February 2013 / Accepted: 7 March 2013 / Published: 12 March 2013
Cited by 10 | PDF Full-text (1667 KB) | HTML Full-text | XML Full-text
Abstract
This study investigates how the use of a Hitman ST300 acoustic sensor can help identify the best forest stands to be used as supply sources for the production of Machine Stress-Rated (MSR) lumber. Using two piezoelectric sensors, the ST300 measures the velocity of
[...] Read more.
This study investigates how the use of a Hitman ST300 acoustic sensor can help identify the best forest stands to be used as supply sources for the production of Machine Stress-Rated (MSR) lumber. Using two piezoelectric sensors, the ST300 measures the velocity of a mechanical wave induced in a standing tree. Measurements were made on 333 black spruce (Picea mariana (Mill.) BSP) trees from the North Shore region, Quebec (Canada) selected across a range of locations and along a chronosequence of elapsed time since the last fire (TSF). Logs were cut from a subsample of 39 trees, and sawn into 77 pieces of 38 mm × 89 mm cross-section before undergoing mechanical testing according to ASTM standard D-4761. A linear regression model was developed to predict the static modulus of elasticity of lumber using tree acoustic velocity and stem diameter at 1.3 m above ground level (R2 = 0.41). Results suggest that, at a regional level, 92% of the black spruce trees meet the requirements of MSR grade 1650Fb-1.5E, whilst 64% and 34% meet the 2100Fb-1.8E and 2400Fb-2.0E, respectively. Mature stands with a TSF < 150 years had 11 and 18% more boards in the latter two categories, respectively, and therefore represented the best supply source for MSR lumber. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Development and Evaluation of a Combined Cultivator and Band Sprayer with a Row-Centering RTK-GPS Guidance System
Sensors 2013, 13(3), 3313-3330; doi:10.3390/s130303313
Received: 24 January 2013 / Revised: 28 February 2013 / Accepted: 8 March 2013 / Published: 11 March 2013
Cited by 4 | PDF Full-text (459 KB) | HTML Full-text | XML Full-text
Abstract
Typically, low-pressure sprayers are used to uniformly apply pre- and post-emergent herbicides to control weeds in crop rows. An innovative machine for weed control in inter-row and intra-row areas, with a unique combination of inter-row cultivation tooling and intra-row band spraying for six
[...] Read more.
Typically, low-pressure sprayers are used to uniformly apply pre- and post-emergent herbicides to control weeds in crop rows. An innovative machine for weed control in inter-row and intra-row areas, with a unique combination of inter-row cultivation tooling and intra-row band spraying for six rows and an electro-hydraulic side-shift frame controlled by a GPS system, was developed and evaluated. Two weed management strategies were tested in the field trials: broadcast spraying (the conventional method) and band spraying with mechanical weed control using RTK-GPS (the experimental method). This approach enabled the comparison between treatments from the perspective of cost savings and efficacy in weed control for a sugar beet crop. During the 2010–2011 season, the herbicide application rate (112 L ha1) of the experimental method was approximately 50% of the conventional method, and thus a significant reduction in the operating costs of weed management was achieved. A comparison of the 0.2-trimmed means of weed population post-treatment showed that the treatments achieved similar weed control rates at each weed survey date. Sugar beet yields were similar with both methods (p = 0.92). The use of the experimental equipment is cost-effective on ≥20 ha of crops. These initial results show good potential for reducing herbicide application in the Spanish beet industry. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat
Sensors 2013, 13(3), 3109-3130; doi:10.3390/s130303109
Received: 14 November 2012 / Revised: 15 January 2013 / Accepted: 19 February 2013 / Published: 5 March 2013
Cited by 8 | PDF Full-text (496 KB) | HTML Full-text | XML Full-text
Abstract
Various sensors have been used to obtain the canopy spectral reflectance for monitoring above-ground plant nitrogen (N) uptake in winter wheat. Comparison and intercalibration of spectral reflectance and vegetation indices derived from different sensors are important for multi-sensor data fusion and utilization. In
[...] Read more.
Various sensors have been used to obtain the canopy spectral reflectance for monitoring above-ground plant nitrogen (N) uptake in winter wheat. Comparison and intercalibration of spectral reflectance and vegetation indices derived from different sensors are important for multi-sensor data fusion and utilization. In this study, the spectral reflectance and its derived vegetation indices from three ground-based sensors (ASD Field Spec Pro spectrometer, CropScan MSR 16 and GreenSeeker RT 100) in six winter wheat field experiments were compared. Then, the best sensor (ASD) and its normalized difference vegetation index (NDVI (807, 736)) for estimating above-ground plant N uptake were determined (R2 of 0.885 and RMSE of 1.440 g·N·m−2 for model calibration). In order to better utilize the spectral reflectance from the three sensors, intercalibration models for vegetation indices based on different sensors were developed. The results indicated that the vegetation indices from different sensors could be intercalibrated, which should promote application of data fusion and make monitoring of above-ground plant N uptake more precise and accurate. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Mid-Infrared Lifetime Imaging for Viability Evaluation of Lettuce Seeds Based on Time-Dependent Thermal Decay Characterization
Sensors 2013, 13(3), 2986-2996; doi:10.3390/s130302986
Received: 7 February 2013 / Revised: 25 February 2013 / Accepted: 27 February 2013 / Published: 1 March 2013
Cited by 5 | PDF Full-text (753 KB) | HTML Full-text | XML Full-text
Abstract
An infrared lifetime thermal imaging technique for the measurement of lettuce seed viability was evaluated. Thermal emission signals from mid-infrared images of healthy seeds and seeds aged for 24, 48, and 72 h were obtained and reconstructed using regression analysis. The emission signals
[...] Read more.
An infrared lifetime thermal imaging technique for the measurement of lettuce seed viability was evaluated. Thermal emission signals from mid-infrared images of healthy seeds and seeds aged for 24, 48, and 72 h were obtained and reconstructed using regression analysis. The emission signals were fitted with a two-term exponential model that had two amplitudes and two time variables as lifetime parameters. The lifetime thermal decay parameters were significantly different for seeds with different aging times. Single-seed viability was visualized using thermal lifetime images constructed from the calculated lifetime parameter values. The time-dependent thermal signal decay characteristics, along with the decay amplitude and delay time images, can be used to distinguish aged lettuce seeds from normal seeds. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle BreedVision — A Multi-Sensor Platform for Non-Destructive Field-Based Phenotyping in Plant Breeding
Sensors 2013, 13(3), 2830-2847; doi:10.3390/s130302830
Received: 21 December 2012 / Revised: 15 February 2013 / Accepted: 16 February 2013 / Published: 27 February 2013
Cited by 45 | PDF Full-text (1127 KB) | HTML Full-text | XML Full-text
Abstract
To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently
[...] Read more.
To achieve the food and energy security of an increasing World population likely to exceed nine billion by 2050 represents a major challenge for plant breeding. Our ability to measure traits under field conditions has improved little over the last decades and currently constitutes a major bottleneck in crop improvement. This work describes the development of a tractor-pulled multi-sensor phenotyping platform for small grain cereals with a focus on the technological development of the system. Various optical sensors like light curtain imaging, 3D Time-of-Flight cameras, laser distance sensors, hyperspectral imaging as well as color imaging are integrated into the system to collect spectral and morphological information of the plants. The study specifies: the mechanical design, the system architecture for data collection and data processing, the phenotyping procedure of the integrated system, results from field trials for data quality evaluation, as well as calibration results for plant height determination as a quantified example for a platform application. Repeated measurements were taken at three developmental stages of the plants in the years 2011 and 2012 employing triticale (×Triticosecale Wittmack L.) as a model species. The technical repeatability of measurement results was high for nearly all different types of sensors which confirmed the high suitability of the platform under field conditions. The developed platform constitutes a robust basis for the development and calibration of further sensor and multi-sensor fusion models to measure various agronomic traits like plant moisture content, lodging, tiller density or biomass yield, and thus, represents a major step towards widening the bottleneck of non-destructive phenotyping for crop improvement and plant genetic studies. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Investigations on a Novel Inductive Concept Frequency Technique for the Grading of Oil Palm Fresh Fruit Bunches
Sensors 2013, 13(2), 2254-2266; doi:10.3390/s130202254
Received: 18 December 2012 / Revised: 30 January 2013 / Accepted: 4 February 2013 / Published: 7 February 2013
Cited by 6 | PDF Full-text (783 KB) | HTML Full-text | XML Full-text
Abstract
From the Malaysian harvester’s perspective, the determination of the ripeness of the oil palm (FFB) is a critical factor to maximize palm oil production. A preliminary study of a novel oil palm fruit sensor to detect the maturity of oil palm fruit bunches
[...] Read more.
From the Malaysian harvester’s perspective, the determination of the ripeness of the oil palm (FFB) is a critical factor to maximize palm oil production. A preliminary study of a novel oil palm fruit sensor to detect the maturity of oil palm fruit bunches is presented. To optimize the functionality of the sensor, the frequency characteristics of air coils of various diameters are investigated to determine their inductance and resonant characteristics. Sixteen samples from two categories, namely ripe oil palm fruitlets and unripe oil palm fruitlets, are tested from 100 Hz up to 100 MHz frequency. The results showed the inductance and resonant characteristics of the air coil sensors display significant changes among the samples of each category. The investigations on the frequency characteristics of the sensor air coils are studied to observe the effect of variations in the coil diameter. The effect of coil diameter yields a significant 0.02643 MHz difference between unripe samples to air and 0.01084 MHz for ripe samples to air. The designed sensor exhibits significant potential in determining the maturity of oil palm fruits. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques
Sensors 2013, 13(2), 2117-2130; doi:10.3390/s130202117
Received: 5 December 2012 / Revised: 26 January 2013 / Accepted: 30 January 2013 / Published: 6 February 2013
Cited by 17 | PDF Full-text (861 KB) | HTML Full-text | XML Full-text
Abstract
This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440–900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the
[...] Read more.
This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440–900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index (NDVI), Vogelmann red-edge index (VOG) and modified red-edge simple ratio (mSR) demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands (13 spectral features) as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle On the Design of a Bioacoustic Sensor for the Early Detection of the Red Palm Weevil
Sensors 2013, 13(2), 1706-1729; doi:10.3390/s130201706
Received: 21 December 2012 / Revised: 25 January 2013 / Accepted: 25 January 2013 / Published: 30 January 2013
Cited by 7 | PDF Full-text (882 KB) | HTML Full-text | XML Full-text
Abstract
During the last two decades Red Palm Weevil (RPW, Rynchophorus Ferrugineus) has become one of the most dangerous threats to palm trees in many parts of the World. Its early detection is difficult, since palm trees do not show visual evidence of
[...] Read more.
During the last two decades Red Palm Weevil (RPW, Rynchophorus Ferrugineus) has become one of the most dangerous threats to palm trees in many parts of the World. Its early detection is difficult, since palm trees do not show visual evidence of infection until it is too late for them to recover. For this reason the development of efficient early detection mechanisms is a critical element of RPW pest management systems. One of the early detection mechanisms proposed in the literature is based on acoustic monitoring, as the activity of RPW larvae inside the palm trunk is audible for human operators under acceptable environmental noise levels (rural areas, night periods, etc.). In this work we propose the design of an autonomous bioacoustic sensor that can be installed in every palm tree under study and is able to analyze the captured audio signal during large periods of time. The results of the audio analysis would be reported wirelessly to a control station, to be subsequently processed and conveniently stored. That control station is to be accessible via the Internet. It is programmed to send warning messages when predefined alarm thresholds are reached, thereby allowing supervisors to check on-line the status and evolution of the palm tree orchards. We have developed a bioacoustic sensor prototype and performed an extensive set of experiments to measure its detection capability, achieving average detection rates over 90%. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle A Sensor for the Measurement of the Moisture of Undisturbed Soil Samples
Sensors 2013, 13(2), 1692-1705; doi:10.3390/s130201692
Received: 25 November 2012 / Revised: 10 January 2013 / Accepted: 15 January 2013 / Published: 29 January 2013
Cited by 5 | PDF Full-text (339 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a very accurate sensor for the measurement of the moisture of undisturbed soil samples. The sensor relies on accurate estimation of the permittivity which is performed independently of the soil type, and a subsequent calibration. The sensor is designed as
[...] Read more.
This paper presents a very accurate sensor for the measurement of the moisture of undisturbed soil samples. The sensor relies on accurate estimation of the permittivity which is performed independently of the soil type, and a subsequent calibration. The sensor is designed as an upgrade of the conventional soil sampling equipment used in agriculture—the Kopecky cylinder. The detailed description of the device is given, and the method for determining soil moisture is explained in detail. Soil moisture of unknown test samples was measured with an absolute error below 0.0057 g/g, which is only 2.24% of the full scale output, illustrating the high accuracy of the sensor. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems
Sensors 2013, 13(2), 1523-1538; doi:10.3390/s130201523
Received: 27 November 2012 / Revised: 15 January 2013 / Accepted: 16 January 2013 / Published: 24 January 2013
Cited by 12 | PDF Full-text (1633 KB) | HTML Full-text | XML Full-text
Abstract
The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of
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The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique additionally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture. Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required. Therefore, much attention has been given to the development of alternative camera designs. In this study, the advantage of a smart one-chip camera design with NDVI image performance is demonstrated in terms of low cost and simplified design. The required assembly and pixel modifications are described, and new algorithms for establishing an enhanced NDVI image quality for data processing are discussed. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Using an Automatic Resistivity Profiler Soil Sensor On-The-Go in Precision Viticulture
Sensors 2013, 13(1), 1121-1136; doi:10.3390/s130101121
Received: 12 December 2012 / Revised: 3 January 2013 / Accepted: 14 January 2013 / Published: 16 January 2013
Cited by 5 | PDF Full-text (801 KB) | HTML Full-text | XML Full-text
Abstract
Spatial information on vineyard soil properties can be useful in precision viticulture. In this paper a combination of high resolution soil spatial information of soil electrical resistivity (ER) and ancillary topographic attributes, such as elevation and slope, were integrated to assess the spatial
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Spatial information on vineyard soil properties can be useful in precision viticulture. In this paper a combination of high resolution soil spatial information of soil electrical resistivity (ER) and ancillary topographic attributes, such as elevation and slope, were integrated to assess the spatial variability patterns of vegetative growth and yield of a commercial vineyard (Vitis vinifera L. cv. Tempranillo) located in the wine-producing region of La Rioja, Spain. High resolution continuous geoelectrical mapping was accomplished by an Automatic Resistivity Profiler (ARP) on-the-go sensor with an on-board GPS system; rolling electrodes enabled ER to be measured for a depth of investigation approximately up to 0.5, 1 and 2 m. Regression analysis and cluster analysis algorithm were used to jointly process soil resistivity data, landscape attributes and grapevine variables. ER showed a structured variability that matched well with trunk circumference spatial pattern and yield. Based on resistivity and a simple terrain attribute uniform management units were delineated. Once a spatial relationship to target variables is found, the integration of point measurement with continuous soil resistivity mapping is a useful technique to identify within-plots areas of vineyard with similar status. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Mechatronic Description of a Laser Autoguided Vehicle for Greenhouse Operations
Sensors 2013, 13(1), 769-784; doi:10.3390/s130100769
Received: 7 November 2012 / Revised: 27 December 2012 / Accepted: 28 December 2012 / Published: 8 January 2013
Cited by 5 | PDF Full-text (1130 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel approach for guiding mobile robots inside greenhouses demonstrated by promising preliminary physical experiments. It represents a comprehensive attempt to use the successful principles of AGVs (auto-guided vehicles) inside greenhouses, but avoiding the necessity of modifying the crop layout,
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This paper presents a novel approach for guiding mobile robots inside greenhouses demonstrated by promising preliminary physical experiments. It represents a comprehensive attempt to use the successful principles of AGVs (auto-guided vehicles) inside greenhouses, but avoiding the necessity of modifying the crop layout, and avoiding having to bury metallic pipes in the greenhouse floor. The designed vehicle can operate different tools, e.g., a spray system for applying plant-protection product, a lifting platform to reach the top part of the plants to perform pruning and harvesting tasks, and a trailer to transport fruits, plants, and crop waste. Regarding autonomous navigation, it follows the idea of AGVs, but now laser emitters are used to mark the desired route. The vehicle development is analyzed from a mechatronic standpoint (mechanics, electronics, and autonomous control). Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Use of a Terrestrial LIDAR Sensor for Drift Detection in Vineyard Spraying
Sensors 2013, 13(1), 516-534; doi:10.3390/s130100516
Received: 15 November 2012 / Revised: 13 December 2012 / Accepted: 18 December 2012 / Published: 2 January 2013
Cited by 14 | PDF Full-text (1556 KB) | HTML Full-text | XML Full-text
Abstract
The use of a scanning Light Detection and Ranging (LIDAR) system to characterize drift during pesticide application is described. The LIDAR system is compared with an ad hoc test bench used to quantify the amount of spray liquid moving beyond the canopy. Two
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The use of a scanning Light Detection and Ranging (LIDAR) system to characterize drift during pesticide application is described. The LIDAR system is compared with an ad hoc test bench used to quantify the amount of spray liquid moving beyond the canopy. Two sprayers were used during the field test; a conventional mist blower at two air flow rates (27,507 and 34,959 m3·h−1) equipped with two different nozzle types (conventional and air injection) and a multi row sprayer with individually oriented air outlets. A simple model based on a linear function was used to predict spray deposit using LIDAR measurements and to compare with the deposits measured over the test bench. Results showed differences in the effectiveness of the LIDAR sensor depending on the sprayed droplet size (nozzle type) and air intensity. For conventional mist blower and low air flow rate; the sensor detects a greater number of drift drops obtaining a better correlation (r = 0.91; p < 0.01) than for the case of coarse droplets or high air flow rate. In the case of the multi row sprayer; drift deposition in the test bench was very poor. In general; the use of the LIDAR sensor presents an interesting and easy technique to establish the potential drift of a specific spray situation as an adequate alternative for the evaluation of drift potential. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Glyphosate Detection by Means of a Voltammetric Electronic Tongue and Discrimination of Potential Interferents
Sensors 2012, 12(12), 17553-17568; doi:10.3390/s121217553
Received: 20 September 2012 / Revised: 11 December 2012 / Accepted: 13 December 2012 / Published: 18 December 2012
Cited by 5 | PDF Full-text (395 KB) | HTML Full-text | XML Full-text
Abstract
A new electronic tongue to monitor the presence of glyphosate (a non-selective systemic herbicide) has been developed. It is based on pulse voltammetry and consists in an array of three working electrodes (Pt, Co and Cu) encapsulated on a methacrylate cylinder. The electrochemical
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A new electronic tongue to monitor the presence of glyphosate (a non-selective systemic herbicide) has been developed. It is based on pulse voltammetry and consists in an array of three working electrodes (Pt, Co and Cu) encapsulated on a methacrylate cylinder. The electrochemical response of the sensing array was characteristic of the presence of glyphosate in buffered water (phosphate buffer 0.1 mol·dm−3, pH 6.7). Rotating disc electrode (RDE) studies were carried out with Pt, Co and Cu electrodes in water at room temperature and at pH 6.7 using 0.1 mol·dm−3 of phosphate as a buffer. In the presence of glyphosate, the corrosion current of the Cu and Co electrodes increased significantly, probably due to the formation of Cu2+ or Co2+ complexes. The pulse array waveform for the voltammetric tongue was designed by taking into account some of the redox processes observed in the electrochemical studies. The PCA statistical analysis required four dimensions to explain 95% of variance. Moreover, a two-dimensional representation of the two principal components differentiated the water mixtures containing glyphosate. Furthermore, the PLS statistical analyses allowed the creation of a model to correlate the electrochemical response of the electrodes with glyphosate concentrations, even in the presence of potential interferents such as humic acids and Ca2+. The system offers a PLS prediction model for glyphosate detection with values of 098, −2.3 × 10−5 and 0.94 for the slope, the intercept and the regression coefficient, respectively, which is in agreement with the good fit between the predicted and measured concentrations. The results suggest the feasibility of this system to help develop electronic tongues for glyphosate detection. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Simultaneous Monitoring of Soil Water Content and Salinity with a Low-Cost Capacitance-Resistance Probe
Sensors 2012, 12(12), 17588-17607; doi:10.3390/s121217588
Received: 12 November 2012 / Revised: 13 December 2012 / Accepted: 14 December 2012 / Published: 18 December 2012
Cited by 14 | PDF Full-text (691 KB) | HTML Full-text | XML Full-text
Abstract
Capacitance and resistivity sensors can be used to continuously monitor soil volumetric water content (θ) and pore-water electrical conductivity (ECp) with non-destructive methods. However, dielectric readings of capacitance sensors operating at low frequencies are normally biased by high
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Capacitance and resistivity sensors can be used to continuously monitor soil volumetric water content (θ) and pore-water electrical conductivity (ECp) with non-destructive methods. However, dielectric readings of capacitance sensors operating at low frequencies are normally biased by high soil electrical conductivity. A procedure to calibrate capacitance-resistance probes in saline conditions was implemented in contrasting soils. A low-cost capacitance-resistance probe (ECH2O-5TE, 70 MHz, Decagon Devices, Pullman, WA, USA) was used in five soils at four water contents (i.e., from dry conditions to saturation) and four salinity levels of the wetting solution (0, 5, 10, and 15 dS·m−1). θ was accurately predicted as a function of the dielectric constant, apparent electrical conductivity (ECa), texture and organic carbon content, even in high salinity conditions. Four models to estimate pore-water electrical conductivity were tested and a set of empirical predicting functions were identified to estimate the model parameters based on easily available soil properties (e.g., texture, soil organic matter). The four models were reformulated to estimate ECp as a function of ECa, dielectric readings, and soil characteristics, improving their performances with respect to the original model formulation. Low-cost capacitance-resistance probes, if properly calibrated, can be effectively used to monitor water and solute dynamics in saline soils. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Colonization of Potato Rhizosphere by GFP-Tagged Bacillus subtilis MB73/2, Pseudomonas sp. P482 and Ochrobactrum sp. A44 Shown on Large Sections of Roots Using Enrichment Sample Preparation and Confocal Laser Scanning Microscopy
Sensors 2012, 12(12), 17608-17619; doi:10.3390/s121217608
Received: 30 September 2012 / Revised: 19 November 2012 / Accepted: 21 November 2012 / Published: 18 December 2012
Cited by 7 | PDF Full-text (3838 KB) | HTML Full-text | XML Full-text
Abstract
The ability to colonize the host plants’ rhizospheres is a crucial feature to study in the case of Plant Growth Promoting Rhizobacteria (PGPRs) with potential agricultural applications. In this work, we have created GFP-tagged derivatives of three candidate PGPRs: Bacillus subtilis MB73/2, Pseudomonas
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The ability to colonize the host plants’ rhizospheres is a crucial feature to study in the case of Plant Growth Promoting Rhizobacteria (PGPRs) with potential agricultural applications. In this work, we have created GFP-tagged derivatives of three candidate PGPRs: Bacillus subtilis MB73/2, Pseudomonas sp. P482 and Ochrobactrum sp. A44. The presence of these strains in the rhizosphere of soil-grown potato (Solanum tuberosum L.) was detected with a classical fluorescence microscope and a confocal laser scanning microscope (CLSM). In this work, we have used a broad-field-of-view CLMS device, dedicated to in vivo analysis of macroscopic objects, equipped with an automated optical zoom system and tunable excitation and detection spectra. We show that features of this type of CLSM microscopes make them particularly well suited to study root colonization by microorganisms. To facilitate the detection of small and scattered bacterial populations, we have developed a fast and user-friendly enrichment method for root sample preparation. The described method, thanks to the in situ formation of mini-colonies, enables visualization of bacterial colonization sites on large root fragments. This approach can be easily modified to study colonization patterns of other fluorescently tagged strains. Additionally, dilution plating of the root extracts was performed to estimate the cell number of MB73/2, P482 and A44 in the rhizosphere of the inoculated plants. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle An Ultrasonic System for Weed Detection in Cereal Crops
Sensors 2012, 12(12), 17343-17357; doi:10.3390/s121217343
Received: 22 October 2012 / Revised: 2 December 2012 / Accepted: 10 December 2012 / Published: 13 December 2012
Cited by 14 | PDF Full-text (12122 KB) | HTML Full-text | XML Full-text
Abstract
Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as
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Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as well as the management practice requires additional efforts. A new sensor-based weed detection method is presented in this paper and its applicability to cereal crops is evaluated. An ultrasonic distance sensor for the determination of plant heights was used for weed detection. It was hypothesised that the weed infested zones have a higher amount of biomass than non-infested areas and that this can be determined by plant height measurements. Ultrasonic distance measurements were taken in a winter wheat field infested by grass weeds and broad-leaved weeds. A total of 80 and 40 circular-shaped samples of different weed densities and compositions were assessed at two different dates. The sensor was pointed directly to the ground for height determination. In the following, weeds were counted and then removed from the sample locations. Grass weeds and broad-leaved weeds were separately removed. Differences between weed infested and weed-free measurements were determined. Dry-matter of weeds and crop was assessed and evaluated together with the sensor measurements. RGB images were taken prior and after weed removal to determine the coverage percentages of weeds and crop per sampling point. Image processing steps included EGI (excess green index) computation and thresholding to separate plants and background. The relationship between ultrasonic readings and the corresponding coverage of the crop and weeds were assessed using multiple regression analysis. Results revealed a height difference between infested and non-infested sample locations. Density and biomass of weeds present in the sample influenced the ultrasonic readings. The possibilities of weed group discrimination were assessed by discriminant analysis. The ultrasonic readings permitted the separation between weed infested zones and non-infested areas with up to 92.8% of success. This system will potentially reduce the cost of weed detection and offers an opportunity to its use in non-selective methods for weed control. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions
Sensors 2012, 12(12), 16988-17006; doi:10.3390/s121216988
Received: 22 October 2012 / Revised: 5 December 2012 / Accepted: 6 December 2012 / Published: 12 December 2012
Cited by 40 | PDF Full-text (2379 KB) | HTML Full-text | XML Full-text
Abstract
The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images,
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The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds
Sensors 2012, 12(12), 17234-17246; doi:10.3390/s121217234
Received: 12 October 2012 / Revised: 27 November 2012 / Accepted: 10 December 2012 / Published: 12 December 2012
Cited by 40 | PDF Full-text (597 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging
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Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Development of a One-Step Immunocapture Real-Time RT-PCR Assay for Detection of Tobacco Mosaic Virus in Soil
Sensors 2012, 12(12), 16685-16694; doi:10.3390/s121216685
Received: 30 October 2012 / Revised: 13 November 2012 / Accepted: 14 November 2012 / Published: 4 December 2012
Cited by 6 | PDF Full-text (372 KB) | HTML Full-text | XML Full-text
Abstract
Tobacco mosaic virus (TMV) causes significant losses in many economically important crops. Contaminated soils may play roles as reservoirs and sources of transmission for TMV. In this study we report the development of an immunocapture real-time RT-PCR (IC-real-time RT-PCR) assay for direct detection
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Tobacco mosaic virus (TMV) causes significant losses in many economically important crops. Contaminated soils may play roles as reservoirs and sources of transmission for TMV. In this study we report the development of an immunocapture real-time RT-PCR (IC-real-time RT-PCR) assay for direct detection of TMV in soils without RNA isolation. A series of TMV infected leaf sap dilutions of 1:101, 1:102, 1:103, 1:104, 1:105 and 1:106 (w/v, g/mL) were added to one gram of soil. The reactivity of DAS-ELISA and conventional RT-PCR was in the range of 1:102 and 1:103 dilution in TMV-infested soils, respectively. Meanwhile, the detection limit of IC-real-time RT-PCR sensitivity was up to 1:106 dilution. However, in plant sap infected by TMV, both IC-real-time RT-PCR and real-time RT-PCR were up to 1:106 dilution, DAS-ELISA could detect at least 1:103 dilution. IC-real-time RT-PCR method can use either plant sample extracts or cultivated soils, and show higher sensitivity than RT-PCR and DAS-ELISA for detection of TMV in soils. Therefore, the proposed IC-real-time RT-PCR assay provides an alternative for quick and very sensitive detection of TMV in soils, with the advantage of not requiring a concentration or RNA purification steps while still allowing detection of TMV for disease control. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Monitoring Pest Insect Traps by Means of Low-Power Image Sensor Technologies
Sensors 2012, 12(11), 15801-15819; doi:10.3390/s121115801
Received: 3 September 2012 / Revised: 8 November 2012 / Accepted: 9 November 2012 / Published: 13 November 2012
Cited by 14 | PDF Full-text (522 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for
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Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.). Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Development of a Configurable Growth Chamber with a Computer Vision System to Study Circadian Rhythm in Plants
Sensors 2012, 12(11), 15356-15375; doi:10.3390/s121115356
Received: 9 July 2012 / Revised: 22 October 2012 / Accepted: 23 October 2012 / Published: 9 November 2012
Cited by 2 | PDF Full-text (1836 KB) | HTML Full-text | XML Full-text
Abstract
Plant development is the result of an endogenous morphogenetic program that integrates environmental signals. The so-called circadian clock is a set of genes that integrates environmental inputs into an internal pacing system that gates growth and other outputs. Study of circadian growth responses
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Plant development is the result of an endogenous morphogenetic program that integrates environmental signals. The so-called circadian clock is a set of genes that integrates environmental inputs into an internal pacing system that gates growth and other outputs. Study of circadian growth responses requires high sampling rates to detect changes in growth and avoid aliasing. We have developed a flexible configurable growth chamber comprising a computer vision system that allows sampling rates ranging between one image per 30 s to hours/days. The vision system has a controlled illumination system, which allows the user to set up different configurations. The illumination system used emits a combination of wavelengths ensuring the optimal growth of species under analysis. In order to obtain high contrast of captured images, the capture system is composed of two CCD cameras, for day and night periods. Depending on the sample type, a flexible image processing software calculates different parameters based on geometric calculations. As a proof of concept we tested the system in three different plant tissues, growth of petunia- and snapdragon (Antirrhinum majus) flowers and of cladodes from the cactus Opuntia ficus-indica. We found that petunia flowers grow at a steady pace and display a strong growth increase in the early morning, whereas Opuntia cladode growth turned out not to follow a circadian growth pattern under the growth conditions imposed. Furthermore we were able to identify a decoupling of increase in area and length indicating that two independent growth processes are responsible for the final size and shape of the cladode. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables
Sensors 2012, 12(10), 14129-14143; doi:10.3390/s121014129
Received: 5 September 2012 / Revised: 15 October 2012 / Accepted: 18 October 2012 / Published: 22 October 2012
Cited by 4 | PDF Full-text (1585 KB) | HTML Full-text | XML Full-text
Abstract
This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system
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This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch
Sensors 2012, 12(10), 14179-14195; doi:10.3390/s121014179
Received: 20 August 2012 / Revised: 5 October 2012 / Accepted: 10 October 2012 / Published: 22 October 2012
Cited by 11 | PDF Full-text (1047 KB) | HTML Full-text | XML Full-text
Abstract
Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images
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Ripeness classification of oil palm fresh fruit bunches (FFBs) during harvesting is important to ensure that they are harvested during optimum stage for maximum oil production. This paper presents the application of color vision for automated ripeness classification of oil palm FFB. Images of oil palm FFBs of type DxP Yangambi were collected and analyzed using digital image processing techniques. Then the color features were extracted from those images and used as the inputs for Artificial Neural Network (ANN) learning. The performance of the ANN for ripeness classification of oil palm FFB was investigated using two methods: training ANN with full features and training ANN with reduced features based on the Principal Component Analysis (PCA) data reduction technique. Results showed that compared with using full features in ANN, using the ANN trained with reduced features can improve the classification accuracy by 1.66% and is more effective in developing an automated ripeness classifier for oil palm FFB. The developed ripeness classifier can act as a sensor in determining the correct oil palm FFB ripeness category. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks
Sensors 2012, 12(10), 14004-14021; doi:10.3390/s121014004
Received: 2 August 2012 / Revised: 28 September 2012 / Accepted: 6 October 2012 / Published: 17 October 2012
Cited by 8 | PDF Full-text (1148 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless
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This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
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Open AccessArticle A TDR-Based Soil Moisture Monitoring System with Simultaneous Measurement of Soil Temperature and Electrical Conductivity
Sensors 2012, 12(10), 13545-13566; doi:10.3390/s121013545
Received: 16 July 2012 / Revised: 1 October 2012 / Accepted: 2 October 2012 / Published: 9 October 2012
Cited by 17 | PDF Full-text (2022 KB) | HTML Full-text | XML Full-text
Abstract
Elements of design and a field application of a TDR-based soil moisture and electrical conductivity monitoring system are described with detailed presentation of the time delay units with a resolution of 10 ps. Other issues discussed include the temperature correction of the applied
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Elements of design and a field application of a TDR-based soil moisture and electrical conductivity monitoring system are described with detailed presentation of the time delay units with a resolution of 10 ps. Other issues discussed include the temperature correction of the applied time delay units, battery supply characteristics and the measurement results from one of the installed ground measurement stations in the Polesie National Park in Poland. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Design of a New Sensor for Determination of the Effects of Tractor Field Usage in Southern Spain: Soil Sinkage and Alterations in the Cone Index and Dry Bulk Density
Sensors 2012, 12(10), 13480-13490; doi:10.3390/s121013480
Received: 21 August 2012 / Revised: 27 September 2012 / Accepted: 27 September 2012 / Published: 8 October 2012
Cited by 1 | PDF Full-text (526 KB) | HTML Full-text | XML Full-text
Abstract
Variations in sinkage and cone index are of crucial importance when planning fieldwork, and for determining the trafficability of farm machinery. Many studies have highlighted the link between higher values of these parameters and dramatic decreases in crop yield. Variations in the dry
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Variations in sinkage and cone index are of crucial importance when planning fieldwork, and for determining the trafficability of farm machinery. Many studies have highlighted the link between higher values of these parameters and dramatic decreases in crop yield. Variations in the dry bulk density and cone index of clayey soil in Southern Spain were measured following each of five successive passes over the same land with the three types of tractor most widely used in the area (tracked, two-wheel drive and four-wheel drive). In addition, sinkage (rut depth) of the running gear was measured using a laser microrelief profile meter. This device, which integrates three sensors, was specifically designed for these experiments, as was an electrical penetrometer to determine the cone index, and both instruments proved reliable and accurate in the field. The main goal of this study was to design, manufacture and test these new devices. The first pass caused most soil alteration when compared to successive passes for all types of tractor tested and soil conditions prevailing during the tests. (Heavier) four-wheel drive tractors were found to cause greater soil damage (sinkage, cone index and dry bulk density) than two-wheel drive and track tractors. There was no statistically significant difference between the two latter types. The greatest alterations were recorded in the top 10 cm of the soil. The results show that soil compaction should be avoided as much as possible. This can be achieved by ensuring that tractors always travel along the same tracks, especially in the wet season. At present these aspects are not considered by farmers in this area. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Accuracy Enhancement of Inertial Sensors Utilizing High Resolution Spectral Analysis
Sensors 2012, 12(9), 11638-11660; doi:10.3390/s120911638
Received: 6 July 2012 / Revised: 15 August 2012 / Accepted: 22 August 2012 / Published: 27 August 2012
Cited by 6 | PDF Full-text (1005 KB) | HTML Full-text | XML Full-text
Abstract
In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the
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In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the integration of INS with GPS are the subjects of widespread research. Wavelet de-noising of INS sensors has had limited success in removing the long-term (low-frequency) inertial sensor errors. The primary objective of this research is to develop a novel inertial sensor accuracy enhancement technique that can remove both short-term and long-term error components from inertial sensor measurements prior to INS mechanization and INS/GPS integration. A high resolution spectral analysis technique called the fast orthogonal search (FOS) algorithm is used to accurately model the low frequency range of the spectrum, which includes the vehicle motion dynamics and inertial sensor errors. FOS models the spectral components with the most energy first and uses an adaptive threshold to stop adding frequency terms when fitting a term does not reduce the mean squared error more than fitting white noise. The proposed method was developed, tested and validated through road test experiments involving both low-end tactical grade and low cost MEMS-based inertial systems. The results demonstrate that in most cases the position accuracy during GPS outages using FOS de-noised data is superior to the position accuracy using wavelet de-noising. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Using the Image Analysis Method for Describing Soil Detachment by a Single Water Drop Impact
Sensors 2012, 12(9), 11527-11543; doi:10.3390/s120911527
Received: 18 June 2012 / Revised: 6 August 2012 / Accepted: 16 August 2012 / Published: 24 August 2012
Cited by 4 | PDF Full-text (3160 KB) | HTML Full-text | XML Full-text
Abstract
The aim of the present work was to develop a method based on image analysis for describing soil detachment caused by the impact of a single water drop. The method consisted of recording tracks made by splashed particles on blotting paper under an
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The aim of the present work was to develop a method based on image analysis for describing soil detachment caused by the impact of a single water drop. The method consisted of recording tracks made by splashed particles on blotting paper under an optical microscope. The analysis facilitated division of the recorded particle tracks on the paper into drops, “comets” and single particles. Additionally, the following relationships were determined: (i) the distances of splash; (ii) the surface areas of splash tracks into relation to distance; (iii) the surface areas of the solid phase transported over a given distance; and (iv) the ratio of the solid phase to the splash track area in relation to distance. Furthermore, the proposed method allowed estimation of the weight of soil transported by a single water drop splash in relation to the distance of the water drop impact. It was concluded that the method of image analysis of splashed particles facilitated analysing the results at very low water drop energy and generated by single water drops. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Determination of Soil Pore Water Salinity Using an FDR Sensor Working at Various Frequencies up to 500 MHz
Sensors 2012, 12(8), 10890-10905; doi:10.3390/s120810890
Received: 18 June 2012 / Revised: 27 July 2012 / Accepted: 1 August 2012 / Published: 7 August 2012
Cited by 13 | PDF Full-text (1298 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents the application of a frequency-domain reflectometry (FDR) sensor designed for soil salinity assessment of sandy mineral soils in a wide range of soil moisture and bulk electrical conductivity, through the determination of soil complex dielectric permittivity spectra in the frequency
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This paper presents the application of a frequency-domain reflectometry (FDR) sensor designed for soil salinity assessment of sandy mineral soils in a wide range of soil moisture and bulk electrical conductivity, through the determination of soil complex dielectric permittivity spectra in the frequency range 10–500 MHz. The real part of dielectric permittivity was assessed from the 380–440 MHz, while the bulk electrical conductivity was calculated from the 165–325 MHz range. The FDR technique allows determination of bulk electrical conductivity from the imaginary part of the complex dielectric permittivity, without disregarding the dielectric losses. The soil salinity status was determined using the salinity index, defined as a partial derivative of the soil bulk electrical conductivity with respect to the real part of the soil complex dielectric permittivity. The salinity index method enables determining the soil water electrical conductivity value. For the five sandy mineral soils that have been tested, the relationship between bulk electrical conductivity and the real part of dielectric permittivity is essentially linear. As a result, the salinity index method applied for FDR measurements may be adapted to field use after examination of loam and clayey soils. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Hyperspectral Analysis of Soil Nitrogen, Carbon, Carbonate, and Organic Matter Using Regression Trees
Sensors 2012, 12(8), 10639-10658; doi:10.3390/s120810639
Received: 1 July 2012 / Revised: 27 July 2012 / Accepted: 1 August 2012 / Published: 3 August 2012
Cited by 12 | PDF Full-text (1793 KB) | HTML Full-text | XML Full-text
Abstract
The characterization of soil attributes using hyperspectral sensors has revealed patterns in soil spectra that are known to respond to mineral composition, organic matter, soil moisture and particle size distribution. Soil samples from different soil horizons of replicated soil series from sites located
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The characterization of soil attributes using hyperspectral sensors has revealed patterns in soil spectra that are known to respond to mineral composition, organic matter, soil moisture and particle size distribution. Soil samples from different soil horizons of replicated soil series from sites located within Washington and Oregon were analyzed with the FieldSpec Spectroradiometer to measure their spectral signatures across the electromagnetic range of 400 to 1,000 nm. Similarity rankings of individual soil samples reveal differences between replicate series as well as samples within the same replicate series. Using classification and regression tree statistical methods, regression trees were fitted to each spectral response using concentrations of nitrogen, carbon, carbonate and organic matter as the response variables. Statistics resulting from fitted trees were: nitrogen R2 0.91 (p < 0.01) at 403, 470, 687, and 846 nm spectral band widths, carbonate R2 0.95 (p < 0.01) at 531 and 898 nm band widths, total carbon R2 0.93 (p < 0.01) at 400, 409, 441 and 907 nm band widths, and organic matter R2 0.98 (p < 0.01) at 300, 400, 441, 832 and 907 nm band widths. Use of the 400 to 1,000 nm electromagnetic range utilizing regression trees provided a powerful, rapid and inexpensive method for assessing nitrogen, carbon, carbonate and organic matter for upper soil horizons in a nondestructive method. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Automatic Detection of Animals in Mowing Operations Using Thermal Cameras
Sensors 2012, 12(6), 7587-7597; doi:10.3390/s120607587
Received: 27 April 2012 / Revised: 30 May 2012 / Accepted: 4 June 2012 / Published: 7 June 2012
Cited by 7 | PDF Full-text (7158 KB) | HTML Full-text | XML Full-text
Abstract
During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine
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During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix) or pheasant (Phasianus colchicus) are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus) and fawns of roe deer (Capreolus capreolus) to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus) and a rabbit (Oryctolagus cuniculus) in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessArticle Analysis of the Air Flow Generated by an Air-Assisted Sprayer Equipped with Two Axial Fans Using a 3D Sonic Anemometer
Sensors 2012, 12(6), 7598-7613; doi:10.3390/s120607598
Received: 19 April 2012 / Revised: 29 May 2012 / Accepted: 1 June 2012 / Published: 7 June 2012
Cited by 8 | PDF Full-text (522 KB) | HTML Full-text | XML Full-text
Abstract
The flow of air generated by a new design of air assisted sprayer equipped with two axial fans of reversed rotation was analyzed. For this goal, a 3D sonic anemometer has been used (accuracy: 1.5%; measurement range: 0 to 45 m/s). The study
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The flow of air generated by a new design of air assisted sprayer equipped with two axial fans of reversed rotation was analyzed. For this goal, a 3D sonic anemometer has been used (accuracy: 1.5%; measurement range: 0 to 45 m/s). The study was divided into a static test and a dynamic test. During the static test, the air velocity in the working vicinity of the sprayer was measured considering the following machine configurations: (1) one activated fan regulated at three air flows (machine working as a traditional sprayer); (2) two activated fans regulated at three air flows for each fan. In the static test 72 measurement points were considered. The location of the measurement points was as follow: left and right sides of the sprayer; three sections of measurement (A, B and C); three measurement distances from the shaft of the machine (1.5 m, 2.5 m and 3.5 m); and four measurement heights (1 m, 2 m, 3 m and 4 m). The static test results have shown significant differences in the module and the vertical angle of the air velocity vector in function of the regulations of the sprayer. In the dynamic test, the air velocity was measured at 2.5 m from the axis of the sprayer considering four measurement heights (1 m, 2 m, 3 m and 4 m). In this test, the sprayer regulations were: one or two activated fans; one air flow for each fan; forward speed of 2.8 km/h. The use of one fan (back) or two fans (back and front) produced significant differences on the duration of the presence of wind in the measurement point and on the direction of the air velocity vector. The module of the air velocity vector was not affected by the number of activated fans. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Figures

Open AccessArticle Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination
Sensors 2012, 12(6), 7701-7718; doi:10.3390/s120607701
Received: 19 April 2012 / Revised: 28 May 2012 / Accepted: 1 June 2012 / Published: 7 June 2012
Cited by 15 | PDF Full-text (1251 KB) | HTML Full-text | XML Full-text
Abstract
This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color
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This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Figures

Open AccessArticle Estimating Sugarcane Yield Potential Using an In-Season Determination of Normalized Difference Vegetative Index
Sensors 2012, 12(6), 7529-7547; doi:10.3390/s120607529
Received: 13 April 2012 / Revised: 24 May 2012 / Accepted: 27 May 2012 / Published: 4 June 2012
Cited by 17 | PDF Full-text (532 KB) | HTML Full-text | XML Full-text
Abstract
Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be
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Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI). Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY) values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601–750 GDD. In-season estimated yield values improved the yield potential (YP) model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r2 values 0.48 and 0.42 for cane tonnage and sugar yield, respectively). When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r2 0.53 and 0.47 for cane tonnage and sugar yield, respectively); however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)

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Open AccessReview Diverse Applications of Electronic-Nose Technologies in Agriculture and Forestry
Sensors 2013, 13(2), 2295-2348; doi:10.3390/s130202295
Received: 1 December 2012 / Revised: 30 January 2013 / Accepted: 30 January 2013 / Published: 8 February 2013
Cited by 50 | PDF Full-text (561 KB) | HTML Full-text | XML Full-text
Abstract
Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data
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Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)
Open AccessReview Instrumentation in Developing Chlorophyll Fluorescence Biosensing: A Review
Sensors 2012, 12(9), 11853-11869; doi:10.3390/s120911853
Received: 25 June 2012 / Revised: 9 August 2012 / Accepted: 13 August 2012 / Published: 29 August 2012
Cited by 13 | PDF Full-text (364 KB) | HTML Full-text | XML Full-text
Abstract
Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic
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Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic sample. This article reviews the current state of the art knowledge regarding the design of new chlorophyll fluorescence sensing systems, providing appropriate information about processes, instrumentation and electronic devices. These types of systems and applications can be created to determine both comfort conditions and current problems within a given subject. The procedure to measure chlorophyll fluorescence is commonly split into two main parts; the first involves chlorophyll excitation, for which there are passive or active methods. The second part of the procedure is to closely measure the chlorophyll fluorescence response with specialized instrumentation systems. Such systems utilize several methods, each with different characteristics regarding to cost, resolution, ease of processing or portability. These methods for the most part include cameras, photodiodes and satellite images. Full article
(This article belongs to the Special Issue Sensor-Based Technologies and Processes in Agriculture and Forestry)

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