Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
3.7
Journals
Agronomy
Special Issues
Sensing and Perception Systems for Situational Awareness of Agricultural Robotic...
share announcement

Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (30 July 2021) | Viewed by 15266

Special Issue Editors

Dr. Eugenio Cavallo

E-Mail Website
Guest Editor
Institute of Sciences and Technologies for Sustainable Energy and Mobility (STEMS) of the Italian National Research Council (CNR), Strada delle Cacce 73, 10135 Torino, Italy
Interests: agriculture; innovation; sustainability; soil management; natural resources; occupational safety and health; ergonomics
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Marani

E-Mail Website
Guest Editor
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy, Via Amendola 122D/O, 70126 Bari, Italy
Interests: computer vision; Artificial Intelligence; deep learning; hardware/software integration in complex systems for in-field agricultural monitoring; inspection; decision support
Special Issues, Collections and Topics in MDPI journals
Dr. Annalisa Milella

E-Mail Website
Guest Editor
Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council of Italy, via Amendola 122/D-I, 70126 Bari, Italy
Interests: multi-sensor systems for robot perception; 3D reconstruction and mapping; signal and image processing applied to robotics and intelligent systems; agricultural robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last few years have seen an increasing technological transfer from robotics to agriculture to develop intelligent vehicles that can significantly enhance the sustainability of agricultural systems. Robotics technology has been recently proven to be especially valuable for the development of in-field high-throughput phenotyping platforms supporting precision farming applications, to improve natural resource-saving and health and safety conditions of the workforce, as well as to increase productivity and competitiveness of agricultural production. In this respect, accurate and robust sensing and perception systems, taking advantage of the latest research advances in the field of machine learning and deep learning techniques, play a critical role in addressing unsolved issues such as safe interaction with workers and animals, controlled traffic applications, crop row guidance, surveying for variable rate applications, and situation awareness in general.

This Special Issue invites original submissions addressing the study and development of sensing and perception systems to endow an agricultural vehicle with cognitive abilities to safely navigate and interact with its operational environment, with workers or other robots, as well as articles dealing with the study and development of novel data analysis techniques for crop assessment, recognition of condition using data acquired by agricultural robotic platforms, and any other relevant application of such technologies in crops and animal husbandry. Papers providing experimental evidence in the field and involving the integration of different disciplines such as engineering, mathematics/statistics, occupational safety and health, human–machine interface and ergonomics, and environmental and computer science are particularly encouraged. Contributions focusing on the development of agricultural robotic technologies to achieve the United Nations sustainable development goals, such as securing healthy and sustainable food and safe working conditions, and overcoming and recovering from the COVID-19 pandemic are welcome.

Dr. Eugenio Cavallo
Dr. Roberto Marani
Dr. Annalisa Milella
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. Agronomy 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 2600 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

  • Sensor and robot networks in agriculture
  • Multisensor and data fusion
  • Machine learning and deep learning from sensor data in agriculture
  • Environment mapping and classification
  • Robot localization and navigation
  • In-field phenotyping sensors and vehicles
  • Safe human–robot interaction in agricultural applications
  • Sensors and vehicles for in-field pest detection and management
  • Harvesting automation
  • Cooperative agricultural robotics
  • Safety and health in agriculture
  • Food security

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 5151 KiB  
Article
Developing a Realistic Simulation Environment for Robotics Harvesting Operations in a Vegetable Greenhouse
by Brent Van De Walker, Brendan Byrne, Joshua Near, Blake Purdie, Matthew Whatman, David Weales, Cole Tarry and Medhat Moussa
Agronomy 2021, 11(9), 1848; https://doi.org/10.3390/agronomy11091848 - 15 Sep 2021
Cited by 2 | Viewed by 2253
Abstract
Vegetable greenhouse operations are labour intensive. Automating some of these operations can save growers significant costs in an industry with low-profit margins. One of the most demanding operations is harvesting. Harvesting a tomato is a complex operation due to the significant clutter inherent [...] Read more.
Vegetable greenhouse operations are labour intensive. Automating some of these operations can save growers significant costs in an industry with low-profit margins. One of the most demanding operations is harvesting. Harvesting a tomato is a complex operation due to the significant clutter inherent to a greenhouse and the fragility of the object being grasped. Improving grasp and motion planning requires setting up a realistic testbed or testing on-site, which is expensive and time-limited to the growing season and specific environment. As such, it is important to develop a simulation environment to model this operation to help test various strategies before field testing can be conducted. Using the method presented in this work, 3D images are taken from a commercial greenhouse and used to develop a physics-based realistic simulation environment. The environment is then used to simulate a picking operation using various path planning algorithms to investigate the best algorithm to use in this case. The results show that this environment can be used to explore the best approaches to automate harvesting solutions in a vegetable greenhouse environment. Full article
(This article belongs to the Special Issue Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles)
►▼ Show Figures

Figure 1

15 pages, 2401 KiB  
Article
Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi
by Peyman Ziyaee, Vahid Farzand Ahmadi, Pourya Bazyar and Eugenio Cavallo
Agronomy 2021, 11(5), 873; https://doi.org/10.3390/agronomy11050873 - 29 Apr 2021
Cited by 9 | Viewed by 2380
Abstract
Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major [...] Read more.
Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major concern to protect health, secure food and feed, and preserve their value. The effectiveness of image processing, combined with computational techniques, has been investigated to detect and segregate peanut (Arachis hypogaea L.) seeds infected with an aflatoxin producing fungus. After inoculation with Aspergillus flavus, images of peanuts seeds were taken using various lighting sources (LED, UV, and fluorescent lights) on two backgrounds (black and white) at 0, 48, and 72 h after inoculation. Images were post-processed with three different machine learning tools: the artificial neural network (ANN), the support vector machine (SVM), and the adaptive neuro-fuzzy inference system (ANFIS) to detect the Aspergillus flavus growth on peanuts. The results of the study show that the combination of LED light and a white background with ANN had 99.7% accuracy in detecting fungal growth on peanuts 72 h from infection with Aspergillus. Additionally, UV lights and a black background with ANFIS achieve 99.9% accuracy in detecting fungal growth on peanuts 48 h after their infection with Aspergillus. Full article
(This article belongs to the Special Issue Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles)
►▼ Show Figures

Figure 1

16 pages, 1173 KiB  
Article
Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System
by Vasileios Thomopoulos, Dionysios Bitas, Kyriakos-Nikos Papastavros, Dimitris Tsipianitis and Angeliki Kavga
Agronomy 2021, 11(2), 405; https://doi.org/10.3390/agronomy11020405 - 23 Feb 2021
Cited by 16 | Viewed by 4143
Abstract
The control of large greenhouse installations, especially those with hydroponics crops, is based on the analysis and use of data recorded by many sensors. At the same time, the size of such installations does not allow for their effective terrestrial surveillance, to detect [...] Read more.
The control of large greenhouse installations, especially those with hydroponics crops, is based on the analysis and use of data recorded by many sensors. At the same time, the size of such installations does not allow for their effective terrestrial surveillance, to detect problems promptly. In recent years, there has been an interest in the development of autonomous agbots equipped with agricultural sensors. Several ground-based AGV (automated guided vehicles) and UAV (unmanned aerial vehicles) systems have been developed for use in open-air plots. A key feature of all these innovative systems is spectroscopy, the development of which has been assisted by the surveillance capabilities and speed of modern-day UAVs (drones). However, there is a lag in the use of spectroscopy inside greenhouses since UAVs do not move freely indoors. In this paper, we propose as a solution a three-device (3DS) system. Full article
(This article belongs to the Special Issue Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles)
►▼ Show Figures

Figure 1

17 pages, 7684 KiB  
Article
Adaptive Ultrasound-Based Tractor Localization for Semi-Autonomous Vineyard Operations
by Matteo Corno, Sara Furioli, Paolo Cesana and Sergio M. Savaresi
Agronomy 2021, 11(2), 287; https://doi.org/10.3390/agronomy11020287 - 04 Feb 2021
Cited by 10 | Viewed by 2759
Abstract
Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards [...] Read more.
Autonomous driving is greatly impacting intensive and precise agriculture. Matter-of-factly, the first commercial applications of autonomous driving were in autonomous navigation of agricultural tractors in open fields. As the technology improves, the possibility of using autonomous or semi-autonomous tractors in orchards and vineyards is becoming commercially profitable. These scenarios offer more challenges as the vehicle needs to position itself with respect to a more cluttered environment. This paper presents an adaptive localization system for (semi-) autonomous navigation of agricultural tractors in vineyards that is based on ultrasonic automotive sensors. The system estimates the distance from the left vineyard row and the incidence angle. The paper shows that a single tuning of the localization algorithm does not provide robust performance in all vegetation scenarios. We solve this issue by implementing an Extended Kalman Filter (EKF) and by introducing an adaptive data selection stage that automatically adapts to the vegetation conditions and discards invalid measurements. An extensive experimental campaign validates the main features of the localization algorithm. In particular, we show that the Root Mean Square Error (RMSE) of the distance is 16 cm, while the angular RMSE is 2.6 degrees. Full article
(This article belongs to the Special Issue Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles)
►▼ Show Figures

Figure 1

18 pages, 2990 KiB  
Article
Evaluation of Cultivar Identification Performance Using Feature Expressions and Classification Algorithms on Optical Images of Sweet Corn Seeds
by Yu Tang, Zhishang Cheng, Aimin Miao, Jiajun Zhuang, Chaojun Hou, Yong He, Xuan Chu and Shaoming Luo
Agronomy 2020, 10(9), 1268; https://doi.org/10.3390/agronomy10091268 - 27 Aug 2020
Cited by 5 | Viewed by 2426
Abstract
Cultivar identification of seeds is important for crop yield and quality. To study the impact of different features expressions and classification methods on cultivar identification, the performance of the feature expressions and classification algorithms affecting the accuracy of cultivar identification was evaluated by [...] Read more.
Cultivar identification of seeds is important for crop yield and quality. To study the impact of different features expressions and classification methods on cultivar identification, the performance of the feature expressions and classification algorithms affecting the accuracy of cultivar identification was evaluated by image processing techniques. A total of 448 samples of seeds from seven cultivars of sweet corn, namely, Orlando, Beiyasi, Jingketian 183, Jingtian 218, Suitian 1, CT76 and Lilixiangtian, were evaluated. The color, shape and texture features of the seeds were extracted from the images, and the class separability criterion was adopted to evaluate the separability of the features of the embryo side, nonembryo side and both of them combined. The results indicate that the class separability based on the features of the embryo side was higher than that based on the nonembryo side and both of them combined. Based on the embryo-side optical feature data, dimensionality reduction was conducted by two feature selection methods (stepwise discriminant analysis (SDA) and genetic algorithm (GA)) and two feature extraction methods (principal component analysis (PCA) and kernel principal component analysis (KPCA)). Performance evaluation of the feature reductions was conducted by constructing k-nearest neighbor (K-NN), naïve Bayes (NB), linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Compared to the PCA and KPCA algorithms, the SDA and GA algorithms were more conducive to the cultivar classification of sweet corn seeds; the critical features selected specifically by the SDA, K-NN, NB, LDA and SVM classifiers achieved the best classification accuracies (81.43%, 82.86%, 90%, and 87.14%, respectively). Analysis of variance (ANOVA) revealed that the approach for optical feature selection had a more significant effect on the identification of sweet corn seed cultivars than did the classifiers. Therefore, based on the optical images of the embryo side and the key features obtained by the feature selection method, a classification model was constructed for the accurate and nondestructive classification of different sweet corn seed cultivars. Full article
(This article belongs to the Special Issue Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop