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AgriEngineering
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Selected Papers from The Ag Robotic Forum—World FIRA 2021
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Selected Papers from The Ag Robotic Forum—World FIRA 2021

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: closed (1 August 2022) | Viewed by 19966

Special Issue Editors

Dr. Roland Lenain

E-Mail Website
Guest Editor
INRAE, National Research Institute for Agriculture, Food and Environment, 59650 Villeneuve-d'Ascq, France
Interests: mobile robots; field robotics; agricultural robotics; predictive control; adaptive control; numeric terrain model; obstacle avoidance; traversability evlaution
Dr. Eric Lucet

E-Mail Website
Assistant Guest Editor
Nano INNOV, The French Alternative Energies and Atomic Energy Commission (CEA), 91120 Palaiseau, France
Interests: mobile robotics

Special Issue Information

Dear Colleagues,

Agricultural robots are able to perform much more tasks than ever and are more and more efficient in working in the agricultural context, allowing farmers to free themselves from harsh or hazardous works. As a result, agriculture robots are marketed and promoted intensively,  promising to be the new tools for agriculture. Nevertheless, the popularity of such autonomous machines relies on their ability to be used, supervised and understood by farmers in their practical situations. The human and robot interactions, whether physically or remotely, will be a key challenge to integrate these (new) robots into farmer’s everyday life. Such interactions are also important to investigate new opportunities for designing and achieving farm operations such as assistance, cooperation or even mimicking and reproducing manual gestures. This supposes to understand human behavior and adapt robotic systems to the expected work. This also implies ensuring the safety of the humans and the integrity of the robots, which is not trivial in dynamic and variable environments. 

The design of agriculture robots that really can be used by farmers and that will be flexible to adjust to different circumstances poses also several scientific challenges in numerous topics such as perception, coordinated control, use of artificial intelligence, interface, or cobotics. This Special Issue aims at sharing the latest scientific advances for agricultural robotics on the following topics.

  • Human–robot(s)–environment interactions
  • Remote control and supervision of (collaborative) robots used in agriculture
  • Agricultural environment awareness and adaptation to farming situation
  • Robot safety and security in the agriculture framework
  • Advanced hardware and software design methods

Dr. Roland Lenain
Dr. Eric Lucet
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • off-road mobile robots
  • robots adaptation
  • motion control
  • human machine interface
  • mobile manipulator
  • soft object manipulation
  • decision making for robot navigation

Published Papers (6 papers)

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Research

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11 pages, 1597 KiB  
Article
Autonomous Navigation of a Forestry Robot Equipped with a Scanning Laser
by Fida Ben Abdallah, Anis Bouali and Pierre-Jean Meausoone
AgriEngineering 2023, 5(1), 1-11; https://doi.org/10.3390/agriengineering5010001 - 20 Dec 2022
Cited by 1 | Viewed by 2573
Abstract
This abstract is an overview of our research project entitled “Innovative Forest Plantation”, currently in progress. The aim of this project is to automate traditionally manual tasks for poplar plantations in the first years after planting, in particular mechanical weeding without the use [...] Read more.
This abstract is an overview of our research project entitled “Innovative Forest Plantation”, currently in progress. The aim of this project is to automate traditionally manual tasks for poplar plantations in the first years after planting, in particular mechanical weeding without the use of herbicides. The poplar forest is considered as a semi-structured environment where the dense canopy prevents the use of GPS signals and laser sensors are often preferred to localize the vehicle. In this paper, we focus on one of the main functionalities: autonomous navigation, which consists in detecting and locating trees to move safely in such complex environment. Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is very well-suited solution. The constructed map can be reliably used to plan semantic paths of the mobile robot in order treat specifically each tree. Simulations conducted on Gazebo and Robot Operation System (ROS) have proven that the robot could navigate autonomously in a poplar forest. Full article
(This article belongs to the Special Issue Selected Papers from The Ag Robotic Forum—World FIRA 2021)
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12 pages, 982 KiB  
Article
Online Gain Tuning Using Neural Networks: A Comparative Study
by Ashley Hill, Jean Laneurit, Roland Lenain and Eric Lucet
AgriEngineering 2022, 4(4), 1200-1211; https://doi.org/10.3390/agriengineering4040075 - 14 Dec 2022
Viewed by 1824
Abstract
This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method [...] Read more.
This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method to define the steering control of the system. The second strategy uses an end-to-end reinforcement learning method, allowing for the training of a policy for the steering of the robot. The third strategy uses a hybrid gain tuning method, allowing for the adaptation of the settling distance with respect to the robot’s capabilities according to the perception, in order to optimize the robot’s behavior with respect to an objective function. The three methods are described and compared to the results obtained using constant parameters in order to identify their respective strengths and weaknesses. They have been implemented and tested in real conditions on an off-road mobile robot with variable terrain and trajectories. The hybrid method allowing for an overall reduction of 53.2% when compared with a predictive control law. A thorough analysis of the methods are then performed, and further insights are obtained in the context of gain tuning for steering controllers in dynamic environments. The performance and transferability of these methods are demonstrated, as well as their robustness to changes in the terrain properties. As a result, tracking errors are reduced while preserving the stability and the explainability of the control architecture. Full article
(This article belongs to the Special Issue Selected Papers from The Ag Robotic Forum—World FIRA 2021)
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19 pages, 3508 KiB  
Article
Pesticide-Free Robotic Control of Aphids as Crop Pests
by Virginie Lacotte, Toan NGuyen, Javier Diaz Sempere, Vivien Novales, Vincent Dufour, Richard Moreau, Minh Tu Pham, Kanty Rabenorosoa, Sergio Peignier, François G. Feugier, Robin Gaetani, Thomas Grenier, Bruno Masenelli, Pedro da Silva, Abdelaziz Heddi and Arnaud Lelevé
AgriEngineering 2022, 4(4), 903-921; https://doi.org/10.3390/agriengineering4040058 - 7 Oct 2022
Cited by 8 | Viewed by 3548
Abstract
Because our civilization has relied on pesticides to fight weeds, insects, and diseases since antiquity, the use of these chemicals has become natural and exclusive. Unfortunately, the use of pesticides has progressively had alarming effects on water quality, biodiversity, and human health. This [...] Read more.
Because our civilization has relied on pesticides to fight weeds, insects, and diseases since antiquity, the use of these chemicals has become natural and exclusive. Unfortunately, the use of pesticides has progressively had alarming effects on water quality, biodiversity, and human health. This paper proposes to improve farming practices by replacing pesticides with a laser-based robotic approach. This study focused on the neutralization of aphids, as they are among the most harmful pests for crops and complex to control. With the help of deep learning, we developed a mobile robot that spans crop rows, locates aphids, and neutralizes them with laser beams. We have built a prototype with the sole purpose of validating the localization-neutralization loop on a single seedling row. The experiments performed in our laboratory demonstrate the feasibility of detecting different lines of aphids (50% detected at 3 cm/s) and of neutralizing them (90% mortality) without impacting the growth of their host plants. The results are encouraging since aphids are one of the most challenging crop pests to eradicate. However, enhancements in detection and mainly in targeting are necessary to be useful in a real farming context. Moreover, robustness regarding field conditions should be evaluated. Full article
(This article belongs to the Special Issue Selected Papers from The Ag Robotic Forum—World FIRA 2021)
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21 pages, 20200 KiB  
Article
Autonomous Vineyard Tracking Using a Four-Wheel-Steering Mobile Robot and a 2D LiDAR
by Dimia Iberraken, Florian Gaurier, Jean-Christophe Roux, Colin Chaballier and Roland Lenain
AgriEngineering 2022, 4(4), 826-846; https://doi.org/10.3390/agriengineering4040053 - 22 Sep 2022
Cited by 7 | Viewed by 3037
Abstract
The intensive advances in robotics have deeply facilitated the accomplishment of tedious and repetitive tasks in our daily lives. If robots are now well established in the manufacturing industry, thanks to the knowledge of the environment, this is still not fully the case [...] Read more.
The intensive advances in robotics have deeply facilitated the accomplishment of tedious and repetitive tasks in our daily lives. If robots are now well established in the manufacturing industry, thanks to the knowledge of the environment, this is still not fully the case for outdoor applications such as in agriculture, as many parameters are varying (kind of vegetation, perception conditions, wheel–soil interaction, etc.) The use of robots in such a context is nevertheless important since the reduction of environmental impacts requires the use of alternative practices (such as agroecological production or organic production), which require highly accurate work and frequent operations. As a result, the design of robots for agroecology implies notably the availability of highly accurate autonomous navigation processes related to crop and adapting to their variability. This paper proposes several contributions to the problem of crop row tracking using a four-wheel-steering mobile robot, which straddles the crops. It uses a 2D LiDAR allowing the detection of crop rows in 3D thanks to the robot motion. This permits the definition of a reference trajectory that is followed using two different control approaches. The main targeted application is navigation in vineyard fields, to achieve several kinds of operation, such as monitoring, cropping, or accurate spraying. In the first part, a row detection strategy based on a 2D LiDAR inclined in front of the robot to match a predefined shape of the vineyard row in the robot framework is described. The successive detected regions of interest are aggregated along the local robot motion, through the system odometry. This permits the computation of a local trajectory to be followed by a robot. In a second part, a control architecture that allows the control of a four-wheel-steering mobile robot is proposed. Two different strategies are investigated, one is based on a backstepping approach, while the second considers independently the regulation of front and rear steering axle position. The results of these control laws are then compared in an extended simulation framework, using a 3D reconstruction of actual vineyards in different seasons. Full article
(This article belongs to the Special Issue Selected Papers from The Ag Robotic Forum—World FIRA 2021)
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19 pages, 3037 KiB  
Article
A Multi-Control Strategy to Achieve Autonomous Field Operation
by Cyrille Pierre, Roland Lenain, Jean Laneurit and Vincent Rousseau
AgriEngineering 2022, 4(3), 770-788; https://doi.org/10.3390/agriengineering4030050 - 31 Aug 2022
Viewed by 2449
Abstract
Nowadays, there are several methods of controlling a robot depending on the type of agricultural environment in which it operates. In order to perform a complete agricultural task, this paper proposes a switching strategy between several perception/control approaches, allowing us to select the [...] Read more.
Nowadays, there are several methods of controlling a robot depending on the type of agricultural environment in which it operates. In order to perform a complete agricultural task, this paper proposes a switching strategy between several perception/control approaches, allowing us to select the most appropriate one at any given time. This strategy is presented using an electrical tractor and three control approaches we have developed: path tracking, edge following and furrow pursuing. The effectiveness of the proposed development is tested through full-scale experiments in realistic field environments, performing autonomous navigation and weeding operations in an orchard and an open field. The commutation strategy allows us to select behavior depending on the context, with a good robustness with respect to different sizes of crops (maize and bean). The accuracy stays within ten centimeters, allowing us to expect the use of robots to help with the development of agroecological principles. Full article
(This article belongs to the Special Issue Selected Papers from The Ag Robotic Forum—World FIRA 2021)
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Review

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21 pages, 1624 KiB  
Review
AI-Assisted Vision for Agricultural Robots
by Spyros Fountas, Ioannis Malounas, Loukas Athanasakos, Ioannis Avgoustakis and Borja Espejo-Garcia
AgriEngineering 2022, 4(3), 674-694; https://doi.org/10.3390/agriengineering4030043 - 1 Aug 2022
Cited by 14 | Viewed by 5497
Abstract
Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where [...] Read more.
Robotics has been increasingly relevant over the years. The ever-increasing demand for productivity, the reduction of tedious labor, and safety for the operator and the environment have brought robotics to the forefront of technological innovation. The same principle applies to agricultural robots, where such solutions can aid in making farming easier for the farmers, safer, and with greater margins for profit, while at the same time offering higher quality products with minimal environmental impact. This paper focuses on reviewing the existing state of the art for vision-based perception in agricultural robots across a variety of field operations; specifically: weed detection, crop scouting, phenotyping, disease detection, vision-based navigation, harvesting, and spraying. The review revealed a large interest in the uptake of vision-based solutions in agricultural robotics, with RGB cameras being the most popular sensor of choice. It also outlined that AI can achieve promising results and that there is not a single algorithm that outperforms all others; instead, different artificial intelligence techniques offer their unique advantages to address specific agronomic problems. Full article
(This article belongs to the Special Issue Selected Papers from The Ag Robotic Forum—World FIRA 2021)
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