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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 83449

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Special Issue Editors

Prof. Dr. Sigfredo Fuentes

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Guest Editor

Digital Agriculture Food and Wine, School of Agriculture and Food, Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, VIC 3010, Australia
Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Special Issues, Collections and Topics in MDPI journals

Dr. Carlos Poblete-Echeverria

E-Mail Website
Guest Editor

Department of Viticulture and Oenology, Faculty of AgriScicences, Stellenbosch University, Stellenbosch, South Africa
Interests: remote sensing; viticulture; plant physiology; unmanned aerial vehicles

Special Issue Information

Dear Colleagues,

Challenges imposed by climate change have caused significant interest and investments, from different countries, into research areas related to smart digital agriculture. This has been notably triggered by an impending population increase to 9.2 billion in 2050, and the requirement of producing 70% more food by 2050 in half of arable land available today (FAO).

In order to be successful in overcoming the effects of climate change, and to remain competitive and sustainable as a country in the agricultural sector, there is a need to acknowledge these challenges and support research and applications in the development of new and emerging sensor technologies and their applications in agriculture. The development of new and emerging technologies applied to sensor networks will help to overcome these issues by basing decision making on more accurate, meaningful data with high spatial and temporal resolutions.

Sensor technology and sensor networks using telemetry systems and the Internet of Things (IoT) are becoming important for research areas that can be applied to digital agriculture. The key challenge in the production of accurate agricultural models relies critically on timely provision of high-quality, geospatially-distributed data. This requires the development of complex workflows of real-time sensor calibration, data transfer, image processing and interpretation, as well as integration in optimal and high-performing computational nodes and networks. An example is imaging sensor data, where image sensors need to be radiometrically and geometrically calibrated so that each pixel value can be reliably converted to an at-surface reflectance value. Conventional sensing systems deploy time-consuming post-processing, which depends on specialized skills and specific software, which significantly delays the delivery of the final information to users. The aim of this particular call is focused on systems that provide automated integrated set of tools that can standardize the key components of aerial and ground sensor data processing to empowering industry and academics to focus on innovation. The proposed system will enable near-real-time distribution of monitored aspects of soil–plants and atmospheric factors that allows data mapping and delivery via mobile devices.

The technology proposed can include also a cloud computing framework for sensor calibration, processing, fusion, and classification to reduce the complexity and time required to develop workflows.

Papers submitted based on the following aspects will be highly considered:

Research based on the framework to process and combine ground based sensor networks, meteorological information and remotely sensed data from proximal and UAVs based technology to rapidly produce high quality geospatial products that help visualize our environment in extreme detail. Satellite based research will be excluded from this call.
Modelling papers using sensor or remote sensing technology coupled with machine learning modelling that targets important factors in the agricultural decision making process, such as irrigation scheduling, canopy and fertilizer management, pest and disease management, among others.
Research that has used cloud computing on High-Performance Computing (HPC) platforms that enables rapid and automated processing of aerial imagery and ground-based sensor network data, streamlining the process, from data acquisition to data analysis.
Papers showing the shared knowledge and experience gained through collaboration between industry and academics with centralized development efforts for sensor data processing and visualization algorithms, leading to a higher quality of geospatial products, will be also considered.

Potential topics include, but are not limited to, the following:

New sensor development and applications for agriculture and forestry trials.
Sensor network development, data transmission, self-healing and redundancy considerations.
Machine learning modelling for geospatial information targeting agricultural decision making criteria such as plant water status, canopy growth, nutritional level, early pest and disease management, among others.
Remote sensing using unmanned aerial vehicles (UAV) integrated with sensor network technology.
Visualization systems and software platforms developed to integrate sensor networks for decision-making processes.
Low-cost smart sensors applicable to agriculture.
Development of integrated models with sensor networks and applications in agriculture and forestry environments.

Dr. Sigfredo Fuentes
Dr. Carlos Poblete-Echeverria
Guest Editors

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Keywords

Sensor Networks
Internet of Things
Machine Learning
Unmanned Aerial Vehicles
Remote Sensing
Agriculture

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Published Papers (14 papers)

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Editorial

Jump to: Research

3 pages, 179 KiB

Open AccessEditorial

Editorial: Special Issue “Emerging Sensor Technology in Agriculture”

by Carlos Poblete-Echeverría and Sigfredo Fuentes

Sensors 2020, 20(14), 3827; https://doi.org/10.3390/s20143827 - 9 Jul 2020

Cited by 4 | Viewed by 2961

Abstract

Research and innovation activities in the area of sensor technology can accelerate the adoption of new and emerging digital tools in the agricultural sector by the implementation of precision farming practices such as remote sensing, operations, and real-time monitoring [...] Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

Research

Jump to: Editorial

15 pages, 655 KiB

Open AccessArticle

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

by Gurjinder S. Baath, Harpinder K. Baath, Prasanna H. Gowda, Johnson P. Thomas, Brian K. Northup, Srinivas C. Rao and Hardeep Singh

Sensors 2020, 20(3), 867; https://doi.org/10.3390/s20030867 - 6 Feb 2020

Cited by 26 | Viewed by 5082

Abstract

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R²_c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R²_cv/R²_v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R²_cv/R²_v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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19 pages, 4947 KiB

Open AccessArticle

A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions

by Fernando Palacios, Maria P. Diago and Javier Tardaguila

Sensors 2019, 19(17), 3799; https://doi.org/10.3390/s19173799 - 2 Sep 2019

Cited by 22 | Viewed by 4558

Abstract

Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a new, non-invasive method based on the combination of computer vision and machine learning technology for cluster compactness assessment under field conditions from on-the-go red, green, blue (RGB) image acquisition. A mobile sensing platform was used to automatically capture RGB images of grapevine canopies and fruiting zones at night using artificial illumination. Likewise, a set of 195 clusters of four red grapevine varieties of three commercial vineyards were photographed during several years one week prior to harvest. After image acquisition, cluster compactness was evaluated by a group of 15 experts in the laboratory following the International Organization of Vine and Wine (OIV) 204 standard as a reference method. The developed algorithm comprises several steps, including an initial, semi-supervised image segmentation, followed by automated cluster detection and automated compactness estimation using a Gaussian process regression model. Calibration (95 clusters were used as a training set and 100 clusters as the test set) and leave-one-out cross-validation models (LOOCV; performed on the whole 195 clusters set) were elaborated. For these, determination coefficient (R²) of 0.68 and a root mean squared error (RMSE) of 0.96 were obtained on the test set between the image-based compactness estimated values and the average of the evaluators’ ratings (in the range from 1–9). Additionally, the leave-one-out cross-validation yielded a R² of 0.70 and an RMSE of 1.11. The results show that the newly developed computer vision based method could be commercially applied by the wine industry for efficient cluster compactness estimation from RGB on-the-go image acquisition platforms in commercial vineyards. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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20 pages, 10620 KiB

Open AccessArticle

Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation

by Chris Hacking, Nitesh Poona, Nicola Manzan and Carlos Poblete-Echeverría

Sensors 2019, 19(17), 3652; https://doi.org/10.3390/s19173652 - 22 Aug 2019

Cited by 29 | Viewed by 3605

Abstract

Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r² = 0.950), which outperformed RGB imagery (r² = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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16 pages, 3490 KiB

Open AccessArticle

Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach

by Sigfredo Fuentes, Eden Jane Tongson, Roberta De Bei, Claudia Gonzalez Viejo, Renata Ristic, Stephen Tyerman and Kerry Wilkinson

Sensors 2019, 19(15), 3335; https://doi.org/10.3390/s19153335 - 30 Jul 2019

Cited by 28 | Viewed by 6336

Abstract

Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS). Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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18 pages, 2965 KiB

Open AccessArticle

Spatial Variability of Aroma Profiles of Cocoa Trees Obtained through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application

by Sigfredo Fuentes, Gabriela Chacon, Damir D. Torrico, Andrea Zarate and Claudia Gonzalez Viejo

Sensors 2019, 19(14), 3054; https://doi.org/10.3390/s19143054 - 11 Jul 2019

Cited by 16 | Viewed by 4678

Abstract

Cocoa is an important commodity crop, not only to produce chocolate, one of the most complex products from the sensory perspective, but one that commonly grows in developing countries close to the tropics. This paper presents novel techniques applied using cover photography and a novel computer application (VitiCanopy) to assess the canopy architecture of cocoa trees in a commercial plantation in Queensland, Australia. From the cocoa trees monitored, pod samples were collected, fermented, dried, and ground to obtain the aroma profile per tree using gas chromatography. The canopy architecture data were used as inputs in an artificial neural network (ANN) algorithm, with the aroma profile, considering six main aromas, as targets. The ANN model rendered high accuracy (correlation coefficient (R) = 0.82; mean squared error (MSE) = 0.09) with no overfitting. The model was then applied to an aerial image of the whole cocoa field studied to produce canopy vigor, and aroma profile maps up to the tree-by-tree scale. The tool developed could significantly aid the canopy management practices in cocoa trees, which have a direct effect on cocoa quality. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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16 pages, 1966 KiB

Open AccessArticle

Thermal Imaging Reliability for Estimating Grain Yield and Carbon Isotope Discrimination in Wheat Genotypes: Importance of the Environmental Conditions

by Sebastián Romero-Bravo, Ana María Méndez-Espinoza, Miguel Garriga, Félix Estrada, Alejandro Escobar, Luis González-Martinez, Carlos Poblete-Echeverría, Daniel Sepulveda, Ivan Matus, Dalma Castillo, Alejandro del Pozo and Gustavo A. Lobos

Sensors 2019, 19(12), 2676; https://doi.org/10.3390/s19122676 - 13 Jun 2019

Cited by 9 | Viewed by 3538

Abstract

Canopy temperature (Tc) by thermal imaging is a useful tool to study plant water status and estimate other crop traits. This work seeks to estimate grain yield (GY) and carbon discrimination (Δ¹³C) from stress degree day (SDD = Tc − air temperature, Ta), considering the effect of a number of environmental variables such as the averages of the maximum vapor pressure deficit (VPDmax) and the ambient temperature (Tmax), and the soil water content (SWC). For this, a set of 384 and a subset of 16 genotypes of spring bread wheat were evaluated in two Mediterranean-climate sites under water stress (WS) and full irrigation (FI) conditions, in 2011 and 2012, and 2014 and 2015, respectively. The relationship between the GY of the 384 wheat genotypes and SDD was negative and highly significant in 2011 (r² = 0.52 to 0.68), but not significant in 2012 (r² = 0.03 to 0.12). Under WS, the average GY, Δ¹³C, and SDD of wheat genotypes growing in ten environments were more associated with changes in VPDmax and Tmax than with the SWC. Therefore, the amount of water available to the plant is not enough information to assume that a particular genotype is experiencing a stress condition. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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21 pages, 2521 KiB

Open AccessArticle

Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection

by Elie Zemmour, Polina Kurtser and Yael Edan

Sensors 2019, 19(9), 2130; https://doi.org/10.3390/s19092130 - 8 May 2019

Cited by 27 | Viewed by 5410

Abstract

This paper presents an automatic parameter tuning procedure specially developed for a dynamic adaptive thresholding algorithm for fruit detection. One of the major algorithm strengths is its high detection performances using a small set of training images. The algorithm enables robust detection in highly-variable lighting conditions. The image is dynamically split into variably-sized regions, where each region has approximately homogeneous lighting conditions. Nine thresholds were selected to accommodate three different illumination levels for three different dimensions in four color spaces: RGB, HSI, LAB, and NDI. Each color space uses a different method to represent a pixel in an image: RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), LAB (Lightness, Green to Red and Blue to Yellow) and NDI (Normalized Difference Index, which represents the normal difference between the RGB color dimensions). The thresholds were selected by quantifying the required relation between the true positive rate and false positive rate. A tuning process was developed to determine the best fit values of the algorithm parameters to enable easy adaption to different kinds of fruits (shapes, colors) and environments (illumination conditions). Extensive analyses were conducted on three different databases acquired in natural growing conditions: red apples (nine images with 113 apples), green grape clusters (129 images with 1078 grape clusters), and yellow peppers (30 images with 73 peppers). These databases are provided as part of this paper for future developments. The algorithm was evaluated using cross-validation with 70% images for training and 30% images for testing. The algorithm successfully detected apples and peppers in variable lighting conditions resulting with an F-score of 93.17% and 99.31% respectively. Results show the importance of the tuning process for the generalization of the algorithm to different kinds of fruits and environments. In addition, this research revealed the importance of evaluating different color spaces since for each kind of fruit, a different color space might be superior over the others. The LAB color space is most robust to noise. The algorithm is robust to changes in the threshold learned by the training process and to noise effects in images. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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10 pages, 3027 KiB

Open AccessArticle

Vibration Monitoring of the Mechanical Harvesting of Citrus to Improve Fruit Detachment Efficiency

by Sergio Castro-Garcia, Fernando Aragon-Rodriguez, Rafael R. Sola-Guirado, Antonio J. Serrano, Emilio Soria-Olivas and Jesús A. Gil-Ribes

Sensors 2019, 19(8), 1760; https://doi.org/10.3390/s19081760 - 12 Apr 2019

Cited by 16 | Viewed by 4235

Abstract

The introduction of a mechanical harvesting process for oranges can contribute to enhancing farm profitability and reducing labour dependency. The objective of this work is to determine the spread of the vibration in citrus tree canopies to establish recommendations to reach high values of fruit detachment efficiency and eliminate the need for subsequent hand-harvesting processes. Field tests were carried out with a lateral tractor-drawn canopy shaker on four commercial plots of sweet oranges. Canopy vibration during the harvesting process was measured with a set of triaxial accelerometer sensors with a datalogger placed on 90 bearing branches. Monitoring of the vibration process, fruit production, and branch properties were analysed. The improvement of fruit detachment efficiency was possible if both the hedge tree and the machinery were mutually adjusted. The hedge should be trained to facilitate access of the rods and to encourage external fructification since the internal canopy branches showed 43% of the acceleration vibration level of the external branches. The machine should be adjusted to vibrate the branches at a vibration time of at least 5.8 s, after the interaction of the rod with the branch, together with a root mean square acceleration value of 23.9 m/s² to a complete process of fruit detachment. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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24 pages, 10218 KiB

Open AccessArticle

Multi-Pig Part Detection and Association with a Fully-Convolutional Network

by Eric T. Psota, Mateusz Mittek, Lance C. Pérez, Ty Schmidt and Benny Mote

Sensors 2019, 19(4), 852; https://doi.org/10.3390/s19040852 - 19 Feb 2019

Cited by 87 | Viewed by 10225

Abstract

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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19 pages, 4673 KiB

Open AccessArticle

UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status

by Lili Yao, Qing Wang, Jinbo Yang, Yu Zhang, Yan Zhu, Weixing Cao and Jun Ni

Sensors 2019, 19(4), 816; https://doi.org/10.3390/s19040816 - 17 Feb 2019

Cited by 19 | Viewed by 4193

Abstract

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R²) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R² was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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8 pages, 1352 KiB

Open AccessArticle

Monitoring of the Pesticide Droplet Deposition with a Novel Capacitance Sensor

by Pei Wang, Wei Yu, Mingxiong Ou, Chen Gong and Weidong Jia

Sensors 2019, 19(3), 537; https://doi.org/10.3390/s19030537 - 28 Jan 2019

Cited by 22 | Viewed by 4622

Abstract

Rapid detection of spraying deposit can contribute to the precision application of plant protection products. In this study, a novel capacitor sensor system was implemented for measuring the spray deposit immediately after herbicide application. Herbicides with different formulations and nozzles in different mode types were included to test the impact on the capacitance of this system. The results showed that there was a linear relationship between the deposit mass and the digital voltage signals of the capacitance on the sensor surface with spray droplets. The linear models were similar for water and the spray mixtures with non-ionized herbicides usually in formulations of emulsifiable concentrates and suspension concentrates. However, the ionized herbicides in formulation of aqueous solutions presented a unique linear model. With this novel sensor, it is possible to monitor the deposit mass in real-time shortly after the pesticide application. This will contribute to the precision application of plant protection chemicals in the fields. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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17 pages, 32838 KiB

Open AccessEditor’s ChoiceArticle

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

by Victor P. Rueda-Ayala, José M. Peña, Mats Höglind, José M. Bengochea-Guevara and Dionisio Andújar

Sensors 2019, 19(3), 535; https://doi.org/10.3390/s19030535 - 28 Jan 2019

Cited by 73 | Viewed by 9828

Abstract

Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m² were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height (

R^{2} > 0.80

) for both fields, and with dry biomass (

R^{2} = 0.88

), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement (

R^{2} = 0.87

). The UAV-based system showed a weaker estimation capability for plant height and dry biomass (

R^{2} < 0.6

). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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17 pages, 13223 KiB

Open AccessArticle

A Comprehensive Study of the Potential Application of Flying Ethylene-Sensitive Sensors for Ripeness Detection in Apple Orchards

by João Valente, Rodrigo Almeida and Lammert Kooistra

Sensors 2019, 19(2), 372; https://doi.org/10.3390/s19020372 - 17 Jan 2019

Cited by 38 | Viewed by 12311

Abstract

The right moment to harvest apples in fruit orchards is still decided after persistent monitoring of the fruit orchards via local inspection and using manual instrumentation. However, this task is tedious, time consuming, and requires costly human effort because of the manual work that is necessary to sample large orchard parcels. The sensor miniaturization and the advances in gas detection technology have increased the usage of gas sensors and detectors in many industrial applications. This work explores the combination of small-sized sensors under Unmanned Aerial Vehicles (UAV) to understand its suitability for ethylene sensing in an apple orchard. To accomplish this goal, a simulated environment built from field data was used to understand the spatial distribution of ethylene when subject to the orchard environment and the wind of the UAV rotors. The simulation results indicate the main driving variables of the ethylene emission. Additionally, preliminary field tests are also reported. It was demonstrated that the minimum sensing wind speed cut-off is 2 ms⁻¹ and that a small commercial UAV (like Phantom 3 Professional) can sense volatile ethylene at less than six meters from the ground with a detection probability of a maximum of

10 %

. This work is a step forward in the usage of aerial remote sensing technology to detect the optimal harvest time. Full article

(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)

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