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Special Issue "Robotics and Sensors Technology in Agriculture"

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

Deadline for manuscript submissions: 1 October 2022 | Viewed by 4459

Special Issue Editors

Prof. Melanie Ooi
E-Mail Website
Guest Editor
School of Engineering, University of Waikato, Hamilton 3240, New Zealand
Interests: standardising the measurement of blue LED; measurement technique for determining harvest-readiness of Cannabis; analytical measurement uncertainty evaluation; proximal spectral measurement of common new zealand weeds for pasture
Prof. Serge Demidenko
E-Mail Website
Guest Editor
School of Engineering and Technology, Sunway University, 47500 Petaling Jaya, Malaysia
Interests: electronics design and testing; signal generation and processing; instrumentation and measurement
Prof. Dr. Eric Matson
E-Mail Website
Guest Editor
Department of Computer and Information Technology, Purdue University, 401 North Grant Street, West Lafayette, IN 47907-2121, USA
Interests: multiagent systems and agent organizations; autonomous robotics and intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotics, automation and sensing technologies are slowly but surely revolutionizing the agricultural and horticultural industries across the globe. The rapid development of these technologies comes at the same time as some of the industry’s most urgent challenges in labor shortages and border restrictions as a result of an on-going global pandemic. This has accelerated the deployment of technologies for seeding, pest control, crop assessment, disease detection, environmental monitoring and optimization, harvesting, pruning, deleafing, etc. Such technologies include new sensor and measurement systems, advanced machine learning techniques, signal processing, automation and control of application-specific end-effectors, and autonomous aerial and surface vehicles with specific navigation technologies for orchards and glasshouses.

Prof. Melanie Ooi
Prof. Serge Demidenko
Prof. Dr. Eric Matson
Guest Editors

Manuscript Submission Information

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. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind 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 semimonthly 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 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensors
  • unmanned
  • agricultural robotics
  • precision agriculture
  • machine learning
  • computer vision
  • imaging
  • hyperspectral
  • unmanned agricultural vehicle

Published Papers (5 papers)

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Research

Article
Anomaly Detection for Agricultural Vehicles Using Autoencoders
Sensors 2022, 22(10), 3608; https://doi.org/10.3390/s22103608 - 10 May 2022
Viewed by 542
Abstract
The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly [...] Read more.
The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases. Full article
(This article belongs to the Special Issue Robotics and Sensors Technology in Agriculture)
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Article
Early Detection of Grapevine (Vitis vinifera) Downy Mildew (Peronospora) and Diurnal Variations Using Thermal Imaging
Sensors 2022, 22(9), 3585; https://doi.org/10.3390/s22093585 - 08 May 2022
Cited by 1 | Viewed by 720
Abstract
Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew (Peronospora) on grapevine (Vitis [...] Read more.
Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew (Peronospora) on grapevine (Vitis vinifera) leaves at early stages of development using thermal imaging technology and to determine the best time during the day for image acquisition. In controlled experiments, 1587 thermal images of grapevines grown in a greenhouse were acquired around midday, before inoculation, 1, 2, 4, 5, 6, and 7 days after an inoculation. In addition, images of healthy and infected leaves were acquired at seven different times during the day between 7:00 a.m. and 4:30 p.m. Leaves were segmented using the active contour algorithm. Twelve features were derived from the leaf mask and from meteorological measurements. Stepwise logistic regression revealed five significant features used in five classification models. Performance was evaluated using K-folds cross-validation. The support vector machine model produced the best classification accuracy of 81.6%, F1 score of 77.5% and area under the curve (AUC) of 0.874. Acquiring images in the morning between 10:40 a.m. and 11:30 a.m. resulted in 80.7% accuracy, 80.5% F1 score, and 0.895 AUC. Full article
(This article belongs to the Special Issue Robotics and Sensors Technology in Agriculture)
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Article
Portable Deep Learning-Driven Ion-Sensitive Field-Effect Transistor Scheme for Measurement of Carbaryl Pesticide
Sensors 2022, 22(9), 3543; https://doi.org/10.3390/s22093543 - 06 May 2022
Viewed by 412
Abstract
This research proposes a multiple-input deep learning-driven ion-sensitive field-effect transistor (ISFET) scheme to predict the concentrations of carbaryl pesticide. In the study, the carbaryl concentrations are varied between 1 × 10−7–1 × 10−3 M, and the temperatures of solutions between [...] Read more.
This research proposes a multiple-input deep learning-driven ion-sensitive field-effect transistor (ISFET) scheme to predict the concentrations of carbaryl pesticide. In the study, the carbaryl concentrations are varied between 1 × 10−7–1 × 10−3 M, and the temperatures of solutions between 20–35 °C. To validate the multiple-input deep learning regression model, the proposed ISFET scheme is deployed onsite (a field test) to measure pesticide concentrations in the carbaryl-spiked vegetable extract. The advantage of this research lies in the use of a deep learning algorithm with an ISFET sensor to effectively predict the pesticide concentrations, in addition to improving the prediction accuracy. The results demonstrate the very high predictive ability of the proposed ISFET scheme, given an MSE, MAE, and R2 of 0.007%, 0.016%, and 0.992, respectively. The proposed multiple-input deep learning regression model with signal compensation is applicable to a wide range of solution temperatures which is convenient for onsite measurement. Essentially, the proposed multiple-input deep learning regression model could be adopted as an effective alternative to the conventional statistics-based regression to predict pesticide concentrations. Full article
(This article belongs to the Special Issue Robotics and Sensors Technology in Agriculture)
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Article
Effects of Apple Vinegar Addition on Aerobic Deterioration of Fermented High Moisture Maize Using Infrared Thermography as an Indicator
Sensors 2022, 22(3), 771; https://doi.org/10.3390/s22030771 - 20 Jan 2022
Viewed by 467
Abstract
This study was carried out to determine the effects of apple vinegar and sodium diacetate addition on the aerobic stability of fermented high moisture maize grain (HMM) silage after opening. In the study, the effect of three different levels (0%, 0.5% and 1%) [...] Read more.
This study was carried out to determine the effects of apple vinegar and sodium diacetate addition on the aerobic stability of fermented high moisture maize grain (HMM) silage after opening. In the study, the effect of three different levels (0%, 0.5% and 1%) of apple vinegar (AV) and sodium diacetate (SDA) supplementation to fermented HMM at two different storage conditions (27–29 °C, 48% Humidity; 35–37 °C, 26% Humidity) were investigated. The material of the study was fermented rolled maize grain with 62% moisture content stored for about 120 days. Silage samples were subjected to aerobic stability test with three replicates for each treatment group. Wendee and microbiological analyses were made at 0, 2, 4, 7, and 12 days. Meanwhile, samples were displayed in the T200 IR brand thermal camera. According to the thermogram results, 1% SDA addition positively affected HMM silages at the second and fourth days of aerobic stability at both storage conditions (p < 0.05). Aerobic stability and infrared thermography analysis indicated that 1% AV, 0.5%, and 1% SDA additions to HMM silages had promising effects. Due to our results, we concluded that thermal camera images might be used as an alternative quality indicator for silages in laboratory conditions. Full article
(This article belongs to the Special Issue Robotics and Sensors Technology in Agriculture)
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Article
Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment
Sensors 2022, 22(1), 114; https://doi.org/10.3390/s22010114 - 24 Dec 2021
Viewed by 918
Abstract
To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, [...] Read more.
To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A prototype of the agricultural vehicle was created using a rubber-tracked vehicle equipped with two AC motors. A prototype of the autonomous driving hardware consisted of a GNSS module, a motion sensor, an embedded board, and an LTE module, and it was made for less than $1000. Additional software, including a sensor fusion algorithm for positioning and a path-tracking algorithm for autonomous driving, were implemented. Then, the performance of the autonomous driving agricultural vehicle was evaluated based on two trajectories in an apple farm. The results of the field test determined the RMS, and the maximums of the path-following errors were 0.10 m, 0.34 m, respectively. Full article
(This article belongs to the Special Issue Robotics and Sensors Technology in Agriculture)
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