Autonomous and Automated Agricultural Machinery: Safety Issues, Focused Applications, and Regulatory Aspects

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (25 October 2024) | Viewed by 1927

Special Issue Editors


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Guest Editor
CREA—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari (CREA-IT), Treviglio, LM, Italy
Interests: agriculture engineering; manure management; livestock farming automation; one-health approach; modeling

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Guest Editor
CREA—Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari (CREA-IT), Treviglio, LM, Italy
Interests: agriculture engineering; agricultural health and safety; agricultural tractors and tires; energy in agriculture; precision agriculture; digital agriculture
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Special Issue Information

Dear Colleagues,

Research on autonomous vehicles and machinery in agriculture is rapidly evolving, and various legislative and technical approaches are being taken. However, the definition of autonomous or automated vehicles is yet to be established worldwide, as the current standards and regulations do not provide for it. Within this heterogeneous framework, the forthcoming update of the ISO 18497/2018 standard adds to the uncertainty in the field.

Among the factors distinguishing between autonomous and automated vehicles is the way artificial intelligence is deployed, which has a non-uniform definition across the globe. For instance, the EU Artificial Intelligence Act and OCSE working groups have varying definitions; countries have varying approaches and regulatory assessments for autonomous vehicles.

This Special Issue aims to present innovative scientific articles, reviews, communications, brief reports, case reports, short notes, and technical notes to share the scientific, planning, engineering, regulatory, and legislative trends that are uncertain globally. The developed solutions mainly focus on mechanical weeding and spraying along the crops’ rows. Therefore, cutting-edge research is necessary on further autonomous and automated agricultural machinery applications, technical aspects, and regulatory and safety concerns.

This Special Issue aims to serve as an environment to encourage the exchange of ideas and facilitate discussions on experimental and regulatory research involving various countries worldwide. We welcome contributions from all stakeholders to engage in this scientific and constructive dialogue. We give specific attention to regulatory and safety issues related to the operation of autonomous and automated vehicles and the technical approaches taken to expand the use of autonomous and automated machinery.

Dr. Massimo Brambilla
Dr. Maurizio Cutini
Guest Editors

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Keywords

  • occupational safety
  • regulatory issues
  • governmental policies
  • sensors
  • artificial intelligence
  • farming automation
  • precision agriculture
  • precision livestock production and management

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Published Papers (2 papers)

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Research

21 pages, 10251 KiB  
Article
Autonomous Self-Propelled Napa Cabbage Harvester: Cutting, Attitude Control, and Loading Modules
by Yonghyun Park, Myeong-Sin Kim, Juwon Shin, Yongjin Cho, Hyuck-Joo Kim and Hyoung Il Son
Agriculture 2024, 14(11), 1869; https://doi.org/10.3390/agriculture14111869 - 23 Oct 2024
Viewed by 439
Abstract
This paper introduces an autonomous self-propelled Napa cabbage harvester, designed to significantly improve the efficiency and effectiveness of the traditionally labor-intensive harvesting process. The harvester integrates three key modules: a cutting, an attitude control, and a loading module. The cutting module is equipped [...] Read more.
This paper introduces an autonomous self-propelled Napa cabbage harvester, designed to significantly improve the efficiency and effectiveness of the traditionally labor-intensive harvesting process. The harvester integrates three key modules: a cutting, an attitude control, and a loading module. The cutting module is equipped with an attitude control module that ensures precise severance of the Napa cabbage stems, minimizing damage to the crop and maintaining product quality. The attitude control module employs a backstepping-based force control that continuously adjusts the cutting angle and height to ensure consistent cutting precision, even on uneven terrain, thereby optimizing the quality of the Napa cabbages. The loading module automates the collection and transfer of harvested Napa cabbages into storage, significantly reducing the physical burden on workers and improving operational efficiency. Field experiments demonstrated improvements, including a 42–66% reduction in task time compared to manual harvesting, as well as a 37% increase in cutting accuracy through the use of autonomous control. The proposed system presents a comprehensive solution for enhancing productivity, reducing labor demands, and maintaining high crop quality in Napa cabbage harvesting, offering a practical approach to modernizing agricultural practices. Full article
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16 pages, 4655 KiB  
Article
Rolling Bearing Fault Diagnosis in Agricultural Machinery Based on Multi-Source Locally Adaptive Graph Convolution
by Fengyun Xie, Enguang Sun, Linglan Wang, Gan Wang and Qian Xiao
Agriculture 2024, 14(8), 1333; https://doi.org/10.3390/agriculture14081333 - 9 Aug 2024
Cited by 1 | Viewed by 992
Abstract
Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can [...] Read more.
Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method’s effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value. Full article
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