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Special Issue "Agriculture and Forestry: Sensors, Technologies and Procedures"

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

Deadline for manuscript submissions: closed (31 May 2015)

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. 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,

Agriculture and forestry are two areas where advances in sensors and technologies play an important role. Used individually or integrated into devices and machinery, sensors and technologies allow a more efficient use of resources while facilitating the realization of harsh, and occasionally dangerous tasks by the use of agrochemicals.

Methods and procedures, designed to make operational and profitable devices, allow the processing of relevant information oriented toward the efficiency of agricultural tasks.

The following is a list of the main topics covered by this special issue. The special issue will, however, not be limited to these issues:

  • Sensors and technologies based on physical designs, including mechanical tools.
  • Sensors for chemical analysis or applications: soil nutrients and properties, herbicides, pesticides, fertilizers.
  • Sensors for monitorization: humidity, solar radiation, weeds densities, canopy analysis, soil compaction, energy consumption, diameter of trees, crown height, bark thickness, 3D structures.
  • Sensors for quality: fruit identification and ripeness, yield estimation, storage monitoring.
  • Sensors for autonomous agricultural vehicles: navigation, localization, crop rows detection, plants alignments, obstacle avoidance, communications, path following.

Prof. Dr. Gonzalo Pajares Martinsanz
Prof. Dr. Pablo González-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).


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Published Papers (38 papers)

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Research

Open AccessArticle Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera
Sensors 2015, 15(8), 18427-18442; doi:10.3390/s150818427
Received: 31 May 2015 / Revised: 6 July 2015 / Accepted: 20 July 2015 / Published: 28 July 2015
PDF Full-text (3407 KB) | HTML Full-text | XML Full-text
Abstract
This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms
[...] Read more.
This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification
Sensors 2015, 15(7), 15578-15594; doi:10.3390/s150715578
Received: 5 April 2015 / Revised: 25 June 2015 / Accepted: 26 June 2015 / Published: 1 July 2015
Cited by 6 | PDF Full-text (3063 KB) | HTML Full-text | XML Full-text
Abstract
The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images.
[...] Read more.
The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares–discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity
Sensors 2015, 15(6), 13994-14015; doi:10.3390/s150613994
Received: 17 April 2015 / Revised: 28 May 2015 / Accepted: 9 June 2015 / Published: 15 June 2015
Cited by 2 | PDF Full-text (14298 KB) | HTML Full-text | XML Full-text
Abstract
Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming.
[...] Read more.
Ground bearing capacity has become a relevant concept for site-specific management that aims to protect soil from the compaction and the rutting produced by the indiscriminate use of agricultural and forestry machines. Nevertheless, commonly known techniques for its estimation are cumbersome and time-consuming. In order to alleviate these difficulties, this paper introduces an innovative sensory system based on Visible-Near InfraRed (VIS-NIR), Short-Wave InfraRed (SWIR) and Long-Wave InfraRed (LWIR) imagery and a sequential algorithm that combines a registration procedure, a multi-class SVM classifier, a K-means clustering and a linear regression for estimating the ground bearing capacity. To evaluate the feasibility and capabilities of the presented approach, several experimental tests were carried out in a sandy-loam terrain. The proposed solution offers notable benefits such as its non-invasiveness to the soil, its spatial coverage without the need for exhaustive manual measurements and its real time operation. Therefore, it can be very useful in decision making processes that tend to reduce ground damage during agricultural and forestry operations. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Matching the Best Viewing Angle in Depth Cameras for Biomass Estimation Based on Poplar Seedling Geometry
Sensors 2015, 15(6), 12999-13011; doi:10.3390/s150612999
Received: 9 February 2015 / Revised: 29 May 2015 / Accepted: 29 May 2015 / Published: 4 June 2015
Cited by 4 | PDF Full-text (3004 KB) | HTML Full-text | XML Full-text
Abstract
In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows
[...] Read more.
In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (−45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and −45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Figures

Open AccessArticle Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night
Sensors 2015, 15(4), 8284-8301; doi:10.3390/s150408284
Received: 5 December 2014 / Revised: 12 March 2015 / Accepted: 3 April 2015 / Published: 9 April 2015
Cited by 5 | PDF Full-text (1447 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear
[...] Read more.
This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV). The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1) the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2) the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle A Non-Chemical System for Online Weed Control
Sensors 2015, 15(4), 7691-7707; doi:10.3390/s150407691
Received: 22 December 2014 / Revised: 12 March 2015 / Accepted: 20 March 2015 / Published: 30 March 2015
PDF Full-text (723 KB) | HTML Full-text | XML Full-text
Abstract
Non-chemical weed control methods need to be directed towards a site-specific weeding approach, in order to be able to compete the conventional herbicide equivalents. A system for online weed control was developed. It automatically adjusts the tine angle of a harrow and creates
[...] Read more.
Non-chemical weed control methods need to be directed towards a site-specific weeding approach, in order to be able to compete the conventional herbicide equivalents. A system for online weed control was developed. It automatically adjusts the tine angle of a harrow and creates different levels of intensity: from gentle to aggressive. Two experimental plots in a maize field were harrowed with two consecutive passes. The plots presented from low to high weed infestation levels. Discriminant capabilities of an ultrasonic sensor were used to determine the crop and weed variability of the field. A controlling unit used ultrasonic readings to adjust the tine angle, producing an appropriate harrowing intensity. Thus, areas with high crop and weed densities were more aggressively harrowed, while areas with lower densities were cultivated with a gentler treatment; areas with very low densities or without weeds were not treated. Although the weed development was relatively advanced and the soil surface was hard, the weed control achieved by the system reached an average of 51% (20%–91%), without causing significant crop damage as a result of harrowing. This system is proposed as a relatively low cost, online, and real-time automatic harrow that improves the weed control efficacy, reduces energy consumption, and avoids the usage of herbicide. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle The Design and Implementation of the Leaf Area Index Sensor
Sensors 2015, 15(3), 6250-6269; doi:10.3390/s150306250
Received: 14 November 2014 / Revised: 28 February 2015 / Accepted: 4 March 2015 / Published: 13 March 2015
Cited by 2 | PDF Full-text (2662 KB) | HTML Full-text | XML Full-text
Abstract
The quick and accurate acquisition of crop growth parameters on a large scale is important for agricultural management and food security. The combination of photographic and wireless sensor network (WSN) techniques can be used to collect agricultural information, such as leaf area index
[...] Read more.
The quick and accurate acquisition of crop growth parameters on a large scale is important for agricultural management and food security. The combination of photographic and wireless sensor network (WSN) techniques can be used to collect agricultural information, such as leaf area index (LAI), over long distances and in real time. Such acquisition not only provides farmers with photographs of crops and suggestions for farmland management, but also the collected quantitative parameters, such as LAI, can be used to support large scale research in ecology, hydrology, remote sensing, etc. The present research developed a Leaf Area Index Sensor (LAIS) to continuously monitor the growth of crops in several sampling points, and applied 3G/WIFI communication technology to remotely collect (and remotely setup and upgrade) crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The research also constructed a database of images and other information relating to crop management. The leaf length and width method (LAILLW) can accurately measure LAI through direct field harvest. The LAIS has been tested in several exemplary applications, and validation with LAI from LAILLW. The LAI acquired by LAIS had been proved reliable. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle An Approach to Precise Nitrogen Management Using Hand-Held Crop Sensor Measurements and Winter Wheat Yield Mapping in a Mediterranean Environment
Sensors 2015, 15(3), 5504-5517; doi:10.3390/s150305504
Received: 29 December 2014 / Revised: 26 February 2015 / Accepted: 27 February 2015 / Published: 6 March 2015
PDF Full-text (5272 KB) | HTML Full-text | XML Full-text
Abstract
Regardless of the crop production system, nutrients inputs must be controlled at or below a certain economic threshold to achieve an acceptable level of profitability. The use of management zones and variable-rate fertilizer applications is gaining popularity in precision agriculture. Many researchers have
[...] Read more.
Regardless of the crop production system, nutrients inputs must be controlled at or below a certain economic threshold to achieve an acceptable level of profitability. The use of management zones and variable-rate fertilizer applications is gaining popularity in precision agriculture. Many researchers have evaluated the application of final yield maps and geo-referenced geophysical measurements (e.g., apparent soil electrical conductivity-ECa) as a method of establishing relatively homogeneous management zones within the same plot. Yield estimation models based on crop conditions at certain growth stages, soil nutrient statuses, agronomic factors, moisture statuses, and weed/pest pressures are a primary goal in precision agriculture. This study attempted to achieve the following objectives: (1) to investigate the potential for predicting winter wheat yields using vegetation measurements (the Normalized Difference Vegetation Index—NDVI) at the beginning of the season, thereby allowing for a yield response to nitrogen (N) fertilizer; and (2) evaluate the feasibility of using inexpensive optical sensor measurements in a Mediterranean environment. A field experiment was conducted in two commercial wheat fields near Seville, in southwestern Spain. Yield data were collected at harvest using a yield monitoring system (RDS Ceres II-volumetric meter) installed on a combine. Wheat yield and NDVI values of 3498 ± 481 kg ha−1 and 0.67 ± 0.04 nm nm−1 (field 1) and 3221 ± 531 kg ha−1 and 0.68 ± 0.05 nm nm−1 (field 2) were obtained. In both fields, the yield and NDVI exhibited a strong Pearson correlation, with rxy = 0.64 and p < 10−4 in field 1 and rxy = 0.78 and p < 10−4 in field 2. The preliminary results indicate that hand-held crop sensor-based N management can be applied to wheat production in Spain and has the potential to increase agronomic N-use efficiency on a long-term basis. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Distributed Multi-Level Supervision to Effectively Monitor the Operations of a Fleet of Autonomous Vehicles in Agricultural Tasks
Sensors 2015, 15(3), 5402-5428; doi:10.3390/s150305402
Received: 4 January 2015 / Revised: 13 February 2015 / Accepted: 27 February 2015 / Published: 5 March 2015
Cited by 9 | PDF Full-text (1801 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous
[...] Read more.
This paper describes a supervisor system for monitoring the operation of automated agricultural vehicles. The system analyses all of the information provided by the sensors and subsystems on the vehicles in real time and notifies the user when a failure or potentially dangerous situation is detected. In some situations, it is even able to execute a neutralising protocol to remedy the failure. The system is based on a distributed and multi-level architecture that divides the supervision into different subsystems, allowing for better management of the detection and repair of failures. The proposed supervision system was developed to perform well in several scenarios, such as spraying canopy treatments against insects and diseases and selective weed treatments, by either spraying herbicide or burning pests with a mechanical-thermal actuator. Results are presented for selective weed treatment by the spraying of herbicide. The system successfully supervised the task; it detected failures such as service disruptions, incorrect working speeds, incorrect implement states, and potential collisions. Moreover, the system was able to prevent collisions between vehicles by taking action to avoid intersecting trajectories. The results show that the proposed system is a highly useful tool for managing fleets of autonomous vehicles. In particular, it can be used to manage agricultural vehicles during treatment operations. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Figures

Open AccessArticle Detection of Bird Nests during Mechanical Weeding by Incremental Background Modeling and Visual Saliency
Sensors 2015, 15(3), 5096-5111; doi:10.3390/s150305096
Received: 29 December 2014 / Revised: 16 February 2015 / Accepted: 17 February 2015 / Published: 2 March 2015
Cited by 2 | PDF Full-text (8100 KB) | HTML Full-text | XML Full-text
Abstract
Mechanical weeding is an important tool in organic farming. However, the use of mechanical weeding in conventional agriculture is increasing, due to public demands to lower the use of pesticides and an increased number of pesticide-resistant weeds. Ground nesting birds are highly susceptible
[...] Read more.
Mechanical weeding is an important tool in organic farming. However, the use of mechanical weeding in conventional agriculture is increasing, due to public demands to lower the use of pesticides and an increased number of pesticide-resistant weeds. Ground nesting birds are highly susceptible to farming operations, like mechanical weeding, which may destroy the nests and reduce the survival of chicks and incubating females. This problem has limited focus within agricultural engineering. However, when the number of machines increases, destruction of nests will have an impact on various species. It is therefore necessary to explore and develop new technology in order to avoid these negative ethical consequences. This paper presents a vision-based approach to automated ground nest detection. The algorithm is based on the fusion of visual saliency, which mimics human attention, and incremental background modeling, which enables foreground detection with moving cameras. The algorithm achieves a good detection rate, as it detects 28 of 30 nests at an average distance of 3.8 m, with a true positive rate of 0.75. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging
Sensors 2015, 15(2), 4592-4604; doi:10.3390/s150204592
Received: 22 December 2014 / Revised: 5 February 2015 / Accepted: 9 February 2015 / Published: 17 February 2015
Cited by 3 | PDF Full-text (1894 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm
[...] Read more.
The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Figures

Open AccessArticle Use of Multispectral Imaging in Varietal Identification of Tomato
Sensors 2015, 15(2), 4496-4512; doi:10.3390/s150204496
Received: 27 December 2014 / Revised: 8 February 2015 / Accepted: 9 February 2015 / Published: 16 February 2015
Cited by 4 | PDF Full-text (563 KB) | HTML Full-text | XML Full-text
Abstract
Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven
[...] Read more.
Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Development of a Telemetry and Yield-Mapping System of Olive Harvester
Sensors 2015, 15(2), 4001-4018; doi:10.3390/s150204001
Received: 11 December 2014 / Revised: 19 January 2015 / Accepted: 28 January 2015 / Published: 10 February 2015
Cited by 2 | PDF Full-text (5592 KB) | HTML Full-text | XML Full-text
Abstract
Sensors, communication systems and geo-reference units are required to achieve an optimized management of agricultural inputs with respect to the economic and environmental aspects of olive groves. In this study, three commercial olive harvesters were tracked during two harvesting seasons in Spain and
[...] Read more.
Sensors, communication systems and geo-reference units are required to achieve an optimized management of agricultural inputs with respect to the economic and environmental aspects of olive groves. In this study, three commercial olive harvesters were tracked during two harvesting seasons in Spain and Chile using remote and autonomous equipment that was developed to determine their time efficiency and effective based on canopy shaking for fruit detachment. These harvesters work in intensive/high-density (HD) and super-high-density (SHD) olive orchards. A GNSS (Global Navigation Satellite System) and GSM (Global System for Mobile Communications) device was installed to track these harvesters. The GNSS receiver did not affect the driver’s work schedule. Time elements methodology was adapted to the remote data acquisition system. The effective field capacity and field efficiency were investigated. In addition, the field shape, row length, angle between headland alley and row, and row alley width were measured to determinate the optimum orchard design parameters value. The SHD olive harvester showed significant lower effective field capacity values when alley width was less than 4 m. In addition, a yield monitor was developed and installed on a traditional olive harvester to obtain a yield map from the harvested area. The hedge straddle harvester stood out for its highly effective field capacity; nevertheless, a higher field efficiency was provided by a non-integral lateral canopy shaker. All of the measured orchard parameters have influenced machinery yields, whether effective field capacity or field efficiency. A saving of 40% in effective field capacity was achieved with a reduction from 4 m or higher to 3.5 m in alley width for SHD olive harvester. A yield map was plotted using data that were acquired by a yield monitor, reflecting the yield gradient in spite of the larger differences between tree yields. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Eye-Safe Lidar System for Pesticide Spray Drift Measurement
Sensors 2015, 15(2), 3650-3670; doi:10.3390/s150203650
Received: 29 September 2014 / Revised: 19 January 2015 / Accepted: 28 January 2015 / Published: 4 February 2015
Cited by 2 | PDF Full-text (2319 KB) | HTML Full-text | XML Full-text
Abstract
Spray drift is one of the main sources of pesticide contamination. For this reason, an accurate understanding of this phenomenon is necessary in order to limit its effects. Nowadays, spray drift is usually studied by using in situ collectors which only allow time-integrated
[...] Read more.
Spray drift is one of the main sources of pesticide contamination. For this reason, an accurate understanding of this phenomenon is necessary in order to limit its effects. Nowadays, spray drift is usually studied by using in situ collectors which only allow time-integrated sampling of specific points of the pesticide clouds. Previous research has demonstrated that the light detection and ranging (lidar) technique can be an alternative for spray drift monitoring. This technique enables remote measurement of pesticide clouds with high temporal and distance resolution. Despite these advantages, the fact that no lidar instrument suitable for such an application is presently available has appreciably limited its practical use. This work presents the first eye-safe lidar system specifically designed for the monitoring of pesticide clouds. Parameter design of this system is carried out via signal-to-noise ratio simulations. The instrument is based on a 3-mJ pulse-energy erbium-doped glass laser, an 80-mm diameter telescope, an APD optoelectronic receiver and optomechanically adjustable components. In first test measurements, the lidar system has been able to measure a topographic target located over 2 km away. The instrument has also been used in spray drift studies, demonstrating its capability to monitor the temporal and distance evolution of several pesticide clouds emitted by air-assisted sprayers at distances between 50 and 100 m. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Towards an Optimized Method of Olive Tree Crown Volume Measurement
Sensors 2015, 15(2), 3671-3687; doi:10.3390/s150203671
Received: 12 December 2014 / Revised: 26 January 2015 / Accepted: 30 January 2015 / Published: 4 February 2015
Cited by 5 | PDF Full-text (3786 KB) | HTML Full-text | XML Full-text
Abstract
Accurate crown characterization of large isolated olive trees is vital for adjusting spray doses in three-dimensional crop agriculture. Among the many methodologies available, laser sensors have proved to be the most reliable and accurate. However, their operation is time consuming and requires specialist
[...] Read more.
Accurate crown characterization of large isolated olive trees is vital for adjusting spray doses in three-dimensional crop agriculture. Among the many methodologies available, laser sensors have proved to be the most reliable and accurate. However, their operation is time consuming and requires specialist knowledge and so a simpler crown characterization method is required. To this end, three methods were evaluated and compared with LiDAR measurements to determine their accuracy: Vertical Crown Projected Area method (VCPA), Ellipsoid Volume method (VE) and Tree Silhouette Volume method (VTS). Trials were performed in three different kinds of olive tree plantations: intensive, adapted one-trunked traditional and traditional. In total, 55 trees were characterized. Results show that all three methods are appropriate to estimate the crown volume, reaching high coefficients of determination: R2 = 0.783, 0.843 and 0.824 for VCPA, VE and VTS, respectively. However, discrepancies arise when evaluating tree plantations separately, especially for traditional trees. Here, correlations between LiDAR volume and other parameters showed that the Mean Vector calculated for VCPA method showed the highest correlation for traditional trees, thus its use in traditional plantations is highly recommended. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient
Sensors 2015, 15(2), 2860-2872; doi:10.3390/s150202860
Received: 7 August 2014 / Accepted: 10 December 2014 / Published: 28 January 2015
Cited by 4 | PDF Full-text (1919 KB) | HTML Full-text | XML Full-text
Abstract
Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to
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Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to ground-truth or destructive measurements, requiring tedious site-specific calibrations. The objective of this study was to test the performance of a modified digital cover photography method to estimate LAI in apple trees using conventional digital photography and instantaneous measurements of incident radiation (Io) and transmitted radiation (I) through the canopy. Leaf area of 40 single apple trees were measured destructively to obtain real leaf area index (LAID), which was compared with LAI estimated by the proposed digital photography method (LAIM). Results showed that the LAIM was able to estimate LAID with an error of 25% using a constant light extinction coefficient (k = 0.68). However, when k was estimated using an exponential function based on the fraction of foliage cover (ff) derived from images, the error was reduced to 18%. Furthermore, when measurements of light intercepted by the canopy (Ic) were used as a proxy value for k, the method presented an error of only 9%. These results have shown that by using a proxy k value, estimated by Ic, helped to increase accuracy of LAI estimates using digital cover images for apple trees with different canopy sizes and under field conditions. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Open AccessArticle Development of FT-NIR Models for the Simultaneous Estimation of Chlorophyll and Nitrogen Content in Fresh Apple (Malus Domestica) Leaves
Sensors 2015, 15(2), 2662-2679; doi:10.3390/s150202662
Received: 14 October 2014 / Accepted: 14 January 2015 / Published: 26 January 2015
Cited by 2 | PDF Full-text (979 KB) | HTML Full-text | XML Full-text
Abstract
Agricultural practices determine the level of food production and, to great extent, the state of the global environment. During the last decades, the indiscriminate recourse to fertilizers as well as the nitrogen losses from land application have been recognized as serious issues of
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Agricultural practices determine the level of food production and, to great extent, the state of the global environment. During the last decades, the indiscriminate recourse to fertilizers as well as the nitrogen losses from land application have been recognized as serious issues of modern agriculture, globally contributing to nitrate pollution. The development of a reliable Near-Infra-Red Spectroscopy (NIRS)-based method, for the simultaneous monitoring of nitrogen and chlorophyll in fresh apple (Malus domestica) leaves, was investigated on a set of 133 samples, with the aim of estimating the nutritional and physiological status of trees, in real time, cheaply and non-destructively. By means of a FT (Fourier Transform)-NIR instrument, Partial Least Squares (PLS) regression models were developed, spanning a concentration range of 0.577%–0.817% for the total Kjeldahl nitrogen (TKN) content (R2 = 0.983; SEC = 0.012; SEP = 0.028), and of 1.534–2.372 mg/g for the total chlorophyll content (R2 = 0.941; SEC = 0.132; SEP = 0.162). Chlorophyll-a and chlorophyll-b contents were also evaluated (R2 = 0.913; SEC = 0.076; SEP = 0.101 and R2 = 0.899; SEC = 0.059; SEP = 0.101, respectively). All calibration models were validated by means of 47 independent samples. The NIR approach allows a rapid evaluation of the nitrogen and chlorophyll contents, and may represent a useful tool for determining nutritional and physiological status of plants, in order to allow a correction of nutrition programs during the season. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture
Sensors 2014, 14(12), 24212-24230; doi:10.3390/s141224212
Received: 14 October 2014 / Revised: 12 November 2014 / Accepted: 8 December 2014 / Published: 16 December 2014
Cited by 7 | PDF Full-text (3478 KB) | HTML Full-text | XML Full-text
Abstract
3D geodata play an increasingly important role in precision agriculture, e.g., for modeling in-field variations of grain crop features such as height or biomass. A common data capturing method is LiDAR, which often requires expensive equipment and produces large datasets. This study contributes
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3D geodata play an increasingly important role in precision agriculture, e.g., for modeling in-field variations of grain crop features such as height or biomass. A common data capturing method is LiDAR, which often requires expensive equipment and produces large datasets. This study contributes to the improvement of 3D geodata capturing efficiency by assessing the effect of reduced scanning resolution on crop surface models (CSMs). The analysis is based on high-end LiDAR point clouds of grain crop fields of different varieties (rye and wheat) and nitrogen fertilization stages (100%, 50%, 10%). Lower scanning resolutions are simulated by keeping every n-th laser beam with increasing step widths n. For each iteration step, high-resolution CSMs (0.01 m2 cells) are derived and assessed regarding their coverage relative to a seamless CSM derived from the original point cloud, standard deviation of elevation and mean elevation. Reducing the resolution to, e.g., 25% still leads to a coverage of >90% and a mean CSM elevation of >96% of measured crop height. CSM types (maximum elevation or 90th-percentile elevation) react differently to reduced scanning resolutions in different crops (variety, density). The results can help to assess the trade-off between CSM quality and minimum requirements regarding equipment and capturing set-up. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Multisensory System for Fruit Harvesting Robots. Experimental Testing in Natural Scenarios and with Different Kinds of Crops
Sensors 2014, 14(12), 23885-23904; doi:10.3390/s141223885
Received: 5 October 2014 / Revised: 27 November 2014 / Accepted: 4 December 2014 / Published: 11 December 2014
Cited by 9 | PDF Full-text (9083 KB) | HTML Full-text | XML Full-text
Abstract
The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed.
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The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Open AccessArticle The Electronic McPhail Trap
Sensors 2014, 14(12), 22285-22299; doi:10.3390/s141222285
Received: 29 July 2014 / Revised: 15 November 2014 / Accepted: 19 November 2014 / Published: 25 November 2014
Cited by 4 | PDF Full-text (4043 KB) | HTML Full-text | XML Full-text
Abstract
Certain insects affect cultivations in a detrimental way. A notable case is the olive fruit fly (Bactrocera oleae (Rossi)), that in Europe alone causes billions of euros in crop-loss/per year. Pests can be controlled with aerial and ground bait pesticide
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Certain insects affect cultivations in a detrimental way. A notable case is the olive fruit fly (Bactrocera oleae (Rossi)), that in Europe alone causes billions of euros in crop-loss/per year. Pests can be controlled with aerial and ground bait pesticide sprays, the efficiency of which depends on knowing the time and location of insect infestations as early as possible. The inspection of traps is currently carried out manually. Automatic monitoring traps can enhance efficient monitoring of flying pests by identifying and counting targeted pests as they enter the trap. This work deals with the hardware setup of an insect trap with an embedded optoelectronic sensor that automatically records insects as they fly in the trap. The sensor responsible for detecting the insect is an array of phototransistors receiving light from an infrared LED. The wing-beat recording is based on the interruption of the emitted light due to the partial occlusion from insect’s wings as they fly in the trap. We show that the recordings are of high quality paving the way for automatic recognition and transmission of insect detections from the field to a smartphone. This work emphasizes the hardware implementation of the sensor and the detection/counting module giving all necessary implementation details needed to construct it. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle High Speed Stereovision Setup for Position and Motion Estimation of Fertilizer Particles Leaving a Centrifugal Spreader
Sensors 2014, 14(11), 21466-21482; doi:10.3390/s141121466
Received: 22 September 2014 / Revised: 28 October 2014 / Accepted: 31 October 2014 / Published: 13 November 2014
Cited by 5 | PDF Full-text (657 KB) | HTML Full-text | XML Full-text
Abstract
A 3D imaging technique using a high speed binocular stereovision system was developed in combination with corresponding image processing algorithms for accurate determination of the parameters of particles leaving the spinning disks of centrifugal fertilizer spreaders. Validation of the stereo-matching algorithm using a
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A 3D imaging technique using a high speed binocular stereovision system was developed in combination with corresponding image processing algorithms for accurate determination of the parameters of particles leaving the spinning disks of centrifugal fertilizer spreaders. Validation of the stereo-matching algorithm using a virtual 3D stereovision simulator indicated an error of less than 2 pixels for 90% of the particles. The setup was validated using the cylindrical spread pattern of an experimental spreader. A 2D correlation coefficient of 90% and a Relative Error of 27% was found between the experimental results and the (simulated) spread pattern obtained with the developed setup. In combination with a ballistic flight model, the developed image acquisition and processing algorithms can enable fast determination and evaluation of the spread pattern which can be used as a tool for spreader design and precise machine calibration. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis
Sensors 2014, 14(11), 20713-20735; doi:10.3390/s141120713
Received: 9 September 2014 / Revised: 20 October 2014 / Accepted: 23 October 2014 / Published: 3 November 2014
Cited by 5 | PDF Full-text (1100 KB) | HTML Full-text | XML Full-text
Abstract
The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine
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The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Open AccessArticle The Influence of Tractor-Seat Height above the Ground on Lateral Vibrations
Sensors 2014, 14(10), 19713-19730; doi:10.3390/s141019713
Received: 6 July 2014 / Revised: 8 September 2014 / Accepted: 16 October 2014 / Published: 22 October 2014
PDF Full-text (2086 KB) | HTML Full-text | XML Full-text
Abstract
Farmers experience whole-body vibrations when they drive tractors. Among the various factors that influence the vibrations to which the driver is exposed are terrain roughness, tractor speed, tire type and pressure, rear axle width, and tractor seat height above the ground. In this
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Farmers experience whole-body vibrations when they drive tractors. Among the various factors that influence the vibrations to which the driver is exposed are terrain roughness, tractor speed, tire type and pressure, rear axle width, and tractor seat height above the ground. In this paper the influence of tractor seat height above the ground on the lateral vibrations to which the tractor driver is exposed is studied by means of a geometrical and an experimental analysis. Both analyses show that: (i) lateral vibrations experienced by a tractor driver increase linearly with tractor-seat height above the ground; (ii) lateral vibrations to which the tractor driver is exposed can equal or exceed vertical vibrations; (iii) in medium-size tractors, a feasible 30 cm reduction in the height of the tractor seat, which represents only 15% of its current height, will reduce the lateral vibrations by around 20%; and (iv) vertical vibrations are scarcely influenced by tractor-seat height above the ground. The results suggest that manufacturers could increase the comfort of tractors by lowering tractor-seat height above the ground, which will reduce lateral vibrations. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Open AccessArticle Wi-Fi and Satellite-Based Location Techniques for Intelligent Agricultural Machinery Controlled by a Human Operator
Sensors 2014, 14(10), 19767-19784; doi:10.3390/s141019767
Received: 14 July 2014 / Revised: 29 August 2014 / Accepted: 16 October 2014 / Published: 22 October 2014
Cited by 2 | PDF Full-text (2353 KB) | HTML Full-text | XML Full-text
Abstract
In the new agricultural scenarios, the interaction between autonomous tractors and a human operator is important when they jointly perform a task. Obtaining and exchanging accurate localization information between autonomous tractors and the human operator, working as a team, is a critical to
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In the new agricultural scenarios, the interaction between autonomous tractors and a human operator is important when they jointly perform a task. Obtaining and exchanging accurate localization information between autonomous tractors and the human operator, working as a team, is a critical to maintaining safety, synchronization, and efficiency during the execution of a mission. An advanced localization system for both entities involved in the joint work, i.e., the autonomous tractors and the human operator, provides a basis for meeting the task requirements. In this paper, different localization techniques for a human operator and an autonomous tractor in a field environment were tested. First, we compared the localization performances of two global navigation satellite systems’ (GNSS) receivers carried by the human operator: (1) an internal GNSS receiver built into a handheld device; and (2) an external DGNSS receiver with centimeter-level accuracy. To investigate autonomous tractor localization, a real-time kinematic (RTK)-based localization system installed on autonomous tractor developed for agricultural applications was evaluated. Finally, a hybrid localization approach, which combines distance estimates obtained using a wireless scheme with the position of an autonomous tractor obtained using an RTK-GNSS system, is proposed. The hybrid solution is intended for user localization in unstructured environments in which the GNSS signal is obstructed. The hybrid localization approach has two components: (1) a localization algorithm based on the received signal strength indication (RSSI) from the wireless environment; and (2) the acquisition of the tractor RTK coordinates when the human operator is near the tractor. In five RSSI tests, the best result achieved was an average localization error of 4 m. In tests of real-time position correction between rows, RMS error of 2.4 cm demonstrated that the passes were straight, as was desired for the autonomous tractor. From these preliminary results, future work will address the use of autonomous tractor localization in the hybrid localization approach. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle A Novel Low-Cost Open-Hardware Platform for Monitoring Soil Water Content and Multiple Soil-Air-Vegetation Parameters
Sensors 2014, 14(10), 19639-19659; doi:10.3390/s141019639
Received: 6 August 2014 / Revised: 14 September 2014 / Accepted: 26 September 2014 / Published: 21 October 2014
Cited by 7 | PDF Full-text (950 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring soil water content at high spatio-temporal resolution and coupled to other sensor data is crucial for applications oriented towards water sustainability in agriculture, such as precision irrigation or phenotyping root traits for drought tolerance. The cost of instrumentation, however, limits measurement frequency
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Monitoring soil water content at high spatio-temporal resolution and coupled to other sensor data is crucial for applications oriented towards water sustainability in agriculture, such as precision irrigation or phenotyping root traits for drought tolerance. The cost of instrumentation, however, limits measurement frequency and number of sensors. The objective of this work was to design a low cost “open hardware” platform for multi-sensor measurements including water content at different depths, air and soil temperatures. The system is based on an open-source ARDUINO microcontroller-board, programmed in a simple integrated development environment (IDE). Low cost high-frequency dielectric probes were used in the platform and lab tested on three non-saline soils (ECe1: 2.5 < 0.1 mS/cm). Empirical calibration curves were subjected to cross-validation (leave-one-out method), and normalized root mean square error (NRMSE) were respectively 0.09 for the overall model, 0.09 for the sandy soil, 0.07 for the clay loam and 0.08 for the sandy loam. The overall model (pooled soil data) fitted the data very well (R2 = 0.89) showing a high stability, being able to generate very similar RMSEs during training and validation (RMSEtraining = 2.63; RMSEvalidation = 2.61). Data recorded on the card were automatically sent to a remote server allowing repeated field-data quality checks. This work provides a framework for the replication and upgrading of a customized low cost platform, consistent with the open source approach whereby sharing information on equipment design and software facilitates the adoption and continuous improvement of existing technologies. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Detection of Cracks on Tomatoes Using a Hyperspectral Near-Infrared Reflectance Imaging System
Sensors 2014, 14(10), 18837-18850; doi:10.3390/s141018837
Received: 7 August 2014 / Revised: 10 September 2014 / Accepted: 24 September 2014 / Published: 10 October 2014
Cited by 3 | PDF Full-text (3586 KB) | HTML Full-text | XML Full-text
Abstract
The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance
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The objective of this study was to evaluate the use of hyperspectral near-infrared (NIR) reflectance imaging techniques for detecting cuticle cracks on tomatoes. A hyperspectral NIR reflectance imaging system that analyzed the spectral region of 1000–1700 nm was used to obtain hyperspectral reflectance images of 224 tomatoes: 112 with and 112 without cracks along the stem-scar region. The hyperspectral images were subjected to partial least square discriminant analysis (PLS-DA) to classify and detect cracks on the tomatoes. Two morphological features, roundness (R) and minimum-maximum distance (D), were calculated from the PLS-DA images to quantify the shape of the stem scar. Linear discriminant analysis (LDA) and a support vector machine (SVM) were then used to classify R and D. The results revealed 94.6% and 96.4% accuracy for classifications made using LDA and SVM, respectively, for tomatoes with and without crack defects. These data suggest that the hyperspectral near-infrared reflectance imaging system, in addition to traditional NIR spectroscopy-based methods, could potentially be used to detect crack defects on tomatoes and perform quality assessments. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle FPGA-Based Smart Sensor for Drought Stress Detection in Tomato Plants Using Novel Physiological Variables and Discrete Wavelet Transform
Sensors 2014, 14(10), 18650-18669; doi:10.3390/s141018650
Received: 18 June 2014 / Revised: 9 September 2014 / Accepted: 10 September 2014 / Published: 9 October 2014
PDF Full-text (6701 KB) | HTML Full-text | XML Full-text
Abstract
Soil drought represents one of the most dangerous stresses for plants. It impacts the yield and quality of crops, and if it remains undetected for a long time, the entire crop could be lost. However, for some plants a certain amount of drought
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Soil drought represents one of the most dangerous stresses for plants. It impacts the yield and quality of crops, and if it remains undetected for a long time, the entire crop could be lost. However, for some plants a certain amount of drought stress improves specific characteristics. In such cases, a device capable of detecting and quantifying the impact of drought stress in plants is desirable. This article focuses on testing if the monitoring of physiological process through a gas exchange methodology provides enough information to detect drought stress conditions in plants. The experiment consists of using a set of smart sensors based on Field Programmable Gate Arrays (FPGAs) to monitor a group of plants under controlled drought conditions. The main objective was to use different digital signal processing techniques such as the Discrete Wavelet Transform (DWT) to explore the response of plant physiological processes to drought. Also, an index-based methodology was utilized to compensate the spatial variation inside the greenhouse. As a result, differences between treatments were determined to be independent of climate variations inside the greenhouse. Finally, after using the DWT as digital filter, results demonstrated that the proposed system is capable to reject high frequency noise and to detect drought conditions. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Open AccessArticle Estimation of the Age and Amount of Brown Rice Plant Hoppers Based on Bionic Electronic Nose Use
Sensors 2014, 14(10), 18114-18130; doi:10.3390/s141018114
Received: 6 August 2014 / Revised: 17 September 2014 / Accepted: 23 September 2014 / Published: 29 September 2014
Cited by 1 | PDF Full-text (2413 KB) | HTML Full-text | XML Full-text
Abstract
The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH
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The brown rice plant hopper (BRPH), Nilaparvata lugens (Stal), is one of the most important insect pests affecting rice and causes serious damage to the yield and quality of rice plants in Asia. This study used bionic electronic nose technology to sample BRPH volatiles, which vary in age and amount. Principal component analysis (PCA), linear discrimination analysis (LDA), probabilistic neural network (PNN), BP neural network (BPNN) and loading analysis (Loadings) techniques were used to analyze the sampling data. The results indicate that the PCA and LDA classification ability is poor, but the LDA classification displays superior performance relative to PCA. When a PNN was used to evaluate the BRPH age and amount, the classification rates of the training set were 100% and 96.67%, respectively, and the classification rates of the test set were 90.67% and 64.67%, respectively. When BPNN was used for the evaluation of the BRPH age and amount, the classification accuracies of the training set were 100% and 48.93%, respectively, and the classification accuracies of the test set were 96.67% and 47.33%, respectively. Loadings for BRPH volatiles indicate that the main elements of BRPHs’ volatiles are sulfur-containing organics, aromatics, sulfur-and chlorine-containing organics and nitrogen oxides, which provide a reference for sensors chosen when exploited in specialized BRPH identification devices. This research proves the feasibility and broad application prospects of bionic electronic noses for BRPH recognition. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat
Sensors 2014, 14(9), 17753-17769; doi:10.3390/s140917753
Received: 21 June 2014 / Revised: 9 September 2014 / Accepted: 15 September 2014 / Published: 23 September 2014
Cited by 4 | PDF Full-text (1517 KB) | HTML Full-text | XML Full-text
Abstract
Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress
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Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
Sensors 2014, 14(9), 15939-15952; doi:10.3390/s140915939
Received: 16 June 2014 / Revised: 21 August 2014 / Accepted: 22 August 2014 / Published: 28 August 2014
Cited by 4 | PDF Full-text (595 KB) | HTML Full-text | XML Full-text
Abstract
Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as
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Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation). Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method
Sensors 2014, 14(8), 15304-15324; doi:10.3390/s140815304
Received: 5 March 2014 / Revised: 7 July 2014 / Accepted: 8 August 2014 / Published: 19 August 2014
Cited by 2 | PDF Full-text (1403 KB) | HTML Full-text | XML Full-text
Abstract
An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation
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An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation instead of using a broadcast herbicide on the whole surface. This work proposes a strategy where weeds are characterised by a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). Weeds appear in outdoor field images which display real situations obtained from a RGB camera. Thus, images present a mixture of both weed species under varying conditions of lighting. In the presented approach, four decision-making methods were adapted to use the best shape descriptors as attributes and a choice was taken. This proposal establishes a novel methodology with a high success rate in weed species discrimination. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Automated In-Situ Laser Scanner for Monitoring Forest Leaf Area Index
Sensors 2014, 14(8), 14994-15008; doi:10.3390/s140814994
Received: 9 June 2014 / Revised: 7 August 2014 / Accepted: 7 August 2014 / Published: 14 August 2014
Cited by 9 | PDF Full-text (1734 KB) | HTML Full-text | XML Full-text
Abstract
An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed
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An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instrument’s scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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Open AccessArticle Automated Analysis of Barley Organs Using 3D Laser Scanning: An Approach for High Throughput Phenotyping
Sensors 2014, 14(7), 12670-12686; doi:10.3390/s140712670
Received: 8 April 2014 / Revised: 23 June 2014 / Accepted: 24 June 2014 / Published: 15 July 2014
Cited by 18 | PDF Full-text (3789 KB) | HTML Full-text | XML Full-text
Abstract
Due to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and
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Due to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and the organs were separated by applying a histogram-based classification algorithm. The leaf organs were represented by meshing algorithms, while the stem organs were parameterized by a least-squares cylinder approximation. We introduced surface feature histograms with an accuracy of 96% for the separation of the barley organs, leaf and stem. This enables growth monitoring in a time course for barley plants. Its reliability was demonstrated by a comparison with manually fitted parameters with a correlation R2 = 0:99 for the leaf area and R2 = 0:98 for the cumulated stem height. A proof of concept has been given for its applicability for the detection of water stress in barley, where the extension growth of an irrigated and a non-irrigated plant has been monitored. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods
Sensors 2014, 14(7), 12191-12206; doi:10.3390/s140712191
Received: 4 May 2014 / Revised: 16 June 2014 / Accepted: 24 June 2014 / Published: 9 July 2014
Cited by 11 | PDF Full-text (2182 KB) | HTML Full-text | XML Full-text
Abstract
Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and
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Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle A Proposal for Automatic Fruit Harvesting by Combining a Low Cost Stereovision Camera and a Robotic Arm
Sensors 2014, 14(7), 11557-11579; doi:10.3390/s140711557
Received: 1 April 2014 / Revised: 16 June 2014 / Accepted: 25 June 2014 / Published: 30 June 2014
Cited by 8 | PDF Full-text (2443 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes the development of an automatic fruit harvesting system by combining a low cost stereovision camera and a robotic arm placed in the gripper tool. The stereovision camera is used to estimate the size, distance and position of the fruits whereas
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This paper proposes the development of an automatic fruit harvesting system by combining a low cost stereovision camera and a robotic arm placed in the gripper tool. The stereovision camera is used to estimate the size, distance and position of the fruits whereas the robotic arm is used to mechanically pickup the fruits. The low cost stereovision system has been tested in laboratory conditions with a reference small object, an apple and a pear at 10 different intermediate distances from the camera. The average distance error was from 4% to 5%, and the average diameter error was up to 30% in the case of a small object and in a range from 2% to 6% in the case of a pear and an apple. The stereovision system has been attached to the gripper tool in order to obtain relative distance, orientation and size of the fruit. The harvesting stage requires the initial fruit location, the computation of the inverse kinematics of the robotic arm in order to place the gripper tool in front of the fruit, and a final pickup approach by iteratively adjusting the vertical and horizontal position of the gripper tool in a closed visual loop. The complete system has been tested in controlled laboratory conditions with uniform illumination applied to the fruits. As a future work, this system will be tested and improved in conventional outdoor farming conditions. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle An Analysis of Electrical Impedance Measurements Applied for Plant N Status Estimation in Lettuce (Lactuca sativa)
Sensors 2014, 14(7), 11492-11503; doi:10.3390/s140711492
Received: 16 May 2014 / Revised: 19 June 2014 / Accepted: 19 June 2014 / Published: 27 June 2014
Cited by 4 | PDF Full-text (370 KB) | HTML Full-text | XML Full-text
Abstract
Nitrogen plays a key role in crop yields. Hence, farmers may apply excessive N fertilizers to crop fields, inducing environmental pollution. Crop N monitoring methods have been developed to improve N fertilizer management, most of them based on leaf or canopy optical-property measurements.
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Nitrogen plays a key role in crop yields. Hence, farmers may apply excessive N fertilizers to crop fields, inducing environmental pollution. Crop N monitoring methods have been developed to improve N fertilizer management, most of them based on leaf or canopy optical-property measurements. However, sensitivity to environmental interference remains an important drawback. Electrical impedance has been applied to determine the physiological and nutritional status of plant tissue, but no studies related to plant-N contents are reported. The objective of this article is to analyze how the electrical impedance response of plants is affected by their N status. Four sets of lettuce (Lactuca sativa L.) with a different N-source concentrations per set were used. Total nitrogen and electrical impedance spectra (in a 1 to 100 kHz frequency range) were measured five times per set, three times every other day. Minimum phase angles of impedance spectra were detected and analyzed, together with the frequency value in which they occurred, and their magnitude at that frequency. High and positive correlation was observed between plant N content and frequency values at minimum phase angle with no significant variations detected between days of measurement. These results suggest that electrical impedance can be sensitive to plant N status. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
Sensors 2014, 14(6), 10783-10803; doi:10.3390/s140610783
Received: 9 April 2014 / Revised: 6 June 2014 / Accepted: 6 June 2014 / Published: 19 June 2014
Cited by 6 | PDF Full-text (997 KB) | HTML Full-text | XML Full-text
Abstract
Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a
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Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
Open AccessArticle Spatial and Temporal Patterns of Apparent Electrical Conductivity: DUALEM vs. Veris Sensors for Monitoring Soil Properties
Sensors 2014, 14(6), 10024-10041; doi:10.3390/s140610024
Received: 6 February 2014 / Revised: 3 June 2014 / Accepted: 3 June 2014 / Published: 6 June 2014
Cited by 6 | PDF Full-text (1093 KB) | HTML Full-text | XML Full-text
Abstract
The main objective of this study was to compare two apparent soil electrical conductivity (ECa) sensors (Veris 2000 XA and DUALEM 1S) for mapping variability of soil properties in a Mediterranean shallow soil. This study also aims at studying the effect
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The main objective of this study was to compare two apparent soil electrical conductivity (ECa) sensors (Veris 2000 XA and DUALEM 1S) for mapping variability of soil properties in a Mediterranean shallow soil. This study also aims at studying the effect of soil cover vegetation on the ECa measurement by the two types of sensors. The study was based on two surveys carried out under two very different situations: in February of 2012, with low soil moisture content (SMC) and with high and differentiated vegetation development (non grazed pasture), and in February of 2013, with high SMC and with short and relatively homogeneous vegetation development (grazed pasture). The greater temporal stability of Veris sensor, despite the wide variation in the SMC and vegetation ground cover indicates the suitability of using this sensor for monitoring soil properties in permanent pastures. The survey carried out with the DUALEM sensor in 2012 might have been affected by the presence of a 0.20 m vegetation layer at the soil surface, masking the soil properties. These differences should be considered in the selection of ECa sensing systems for a particular application. Full article
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
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