Intelligent and Cooperative Agricultural Machines: Adapting Robots and AI to Agricultural Environments

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 795

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Guest Editor
Agricultural and Biosystems Engineering, Agricultural Research Organization (ARO), The Volcani Centre, P.O. Box 15159, Rishon LeZion 7505101, Israel
Interests: robotics; robot swarms for agricultural applications
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Special Issue Information

Dear Colleagues,

Agriculture is undergoing rapid transformation driven by advances in data analytics, sensing technologies, and artificial intelligence. Precision agriculture, computer vision, and decision-support systems have reached a high level of maturity, enabling unprecedented insights into crops, soil, and farm operations. However, the practical impact of these advances remains limited by a critical bottleneck: their translation into effective physical action depends on machines. Without new agricultural machines and actuation mechanisms, even the most advanced algorithms cannot deliver real-world value.

In contrast to the rapid progress in data and AI, the development of novel agricultural machinery, mechanisms, and robotic platforms has been comparatively slow. Designing machines that can operate reliably in unstructured, harsh, and highly variable agricultural environments presents major challenges, including soil–machine interaction, plant variability, energy efficiency, robustness, safety, cost, and scalability. Furthermore, the adoption of new machines faces additional barriers related to farmer trust, maintenance complexity, interoperability with existing equipment, and regulatory constraints.

This Special Issue aims to highlight innovative machine designs and actuation concepts that enable the next generation of agricultural systems, bridging the gap between digital intelligence and physical execution.

Topics of interest include, but are not limited to, the following:

  • Novel agricultural machines and mechanisms for field operations;
  • Robotic platforms and end-effectors for crop handling and soil interaction;
  • Swarm and multi-machine agricultural systems;
  • Energy-efficient and low-cost machine designs;
  • Strong human–robot cooperation;
  • Machines enabling precision spraying, harvesting, planting, and weeding;
  • Actuation systems for soft, adaptive, or bio-inspired agricultural robots;
  • Design for robustness, maintainability, and farmer adoption;
  • Integration of AI-driven perception and decision-making with physical machines.

This Special Issue invites contributions that place machines at the center of agricultural innovation, emphasizing that sustainable and scalable agriculture ultimately depends on how intelligently we can design and deploy new machines.

Dr. Victor Bloch
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines 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 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

  • intelligent machines
  • cooperative agricultural machines
  • harsh environment
  • robustness
  • cost effectiveness

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Published Papers (1 paper)

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Research

17 pages, 3422 KB  
Article
Mechanical Design and Simulation Testing of Rope Pollination Equipment for Hybrid Rice Seed Production
by Zhide Ma, Jianbo Zhou, Jibing Chen and Yiping Wu
Machines 2026, 14(7), 821; https://doi.org/10.3390/machines14070821 - 19 Jul 2026
Viewed by 438
Abstract
In view of the urgent need for pollination equipment for existing hybrid rice and the practical problems of traditional manual pollen-driving, such as high labor intensity, low efficiency, and easy-to-break rice stems, an automatic pollen-driving system combining traditional manual and mechanical auxiliary control [...] Read more.
In view of the urgent need for pollination equipment for existing hybrid rice and the practical problems of traditional manual pollen-driving, such as high labor intensity, low efficiency, and easy-to-break rice stems, an automatic pollen-driving system combining traditional manual and mechanical auxiliary control was designed. According to the analysis of the driving process, the driving speed v and the rope height h are the main factors affecting the bending degree of rice stems (the cross-section angle θ of rice stems). Through shear and extrusion tests, the characteristic parameters of rice stems and the bonding parameters of the rice stem bonding model were obtained. The test results showed that when the bending angle of the rice stem was 47°, the maximum shear force of the rice stem was 5.42 N. A quadratic orthogonal rotation combination simulation test was carried out using the discrete element method (EDEM), and the optimal parameter combination of powder driving speed and rope height was determined. The angle error between the measured angle value and that obtained by the constraint solving tool of Design-Expert software was less than 5%. Furthermore, combined with field experiments, through the optimization of the pollen-driving operation parameters, the phenomenon of rice breakage was reduced in the process of pollen-driving. Therefore, the research in this paper can provide certain references for the design of hybrid rice powder-driven systems and the optimization of operating parameters. Full article
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