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Robotic Systems for Future Farming

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 784

Special Issue Editors


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Guest Editor
National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence; machine vision; robotics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Interests: agricultural robotics; soft robotics; smart agriculture

Special Issue Information

Dear Colleagues,

Agriculture faces significant challenges globally, such as labor shortages, environmental sustainability, food security, and resource efficiency. Robotic systems integrated with advanced sensor technologies offer transformative solutions, reshaping traditional farming practices into precision-driven, sustainable, and highly productive systems. The synergy between robotics and sensors enables enhanced crop monitoring, automated pest and disease detection, precise application of inputs, and improved decision-making capabilities through data-driven insights.

This Special Issue invites original research and comprehensive reviews highlighting recent developments, innovative technologies, and practical applications of robotic systems specifically designed for modern and future agricultural practices. Submissions should emphasize sensor integration, sensing methodologies, data processing, and practical implementation of robotic solutions in farming.

Potential topics include, but are not limited to, the following:

  • Robotics for precision agriculture;
  • Sensor-integrated autonomous agricultural vehicles;
  • AI-enabled robotic sensing systems;
  • Multi-sensor data fusion for farming robots;
  • Vision-based robotic systems for plant monitoring;
  • Robotic harvesting systems;
  • Agricultural robot navigation and localization;
  • Environmental sensing for automated farming;
  • Smart sensor networks in robotic agriculture;
  • IoT-enabled robotic management of farms.

Dr. Xiaojun Jin
Dr. Dong Chen
Guest Editors

Manuscript Submission Information

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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 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

  • robotic farming
  • precision agriculture
  • agricultural robotics
  • AI-enabled sensors
  • sensor fusion
  • autonomous systems
  • smart farming
  • environmental sensing
  • IoT in agriculture
  • agricultural automation

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Published Papers (1 paper)

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Research

28 pages, 5802 KB  
Article
An Autonomous Operation Path Planning Method for Wheat Planter Based on Improved Particle Swarm Algorithm
by Shuangshuang Du, Yunjie Zhao, Yongqiang Tian and Taihong Zhang
Sensors 2025, 25(17), 5468; https://doi.org/10.3390/s25175468 - 3 Sep 2025
Viewed by 553
Abstract
To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, [...] Read more.
To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, the proposed method introduces a Tent chaotic mapping initialization mechanism, a Logistic-based dynamic inertia weight adjustment strategy, and adaptive Gaussian perturbation optimization to achieve precise control of the agricultural machinery’s driving orientation angle. A comprehensive path planning model is constructed with the objectives of minimizing the effective operation path length, reducing turning frequency, and maximizing coverage rate. Furthermore, cubic Bézier curves are employed for path smoothing, effectively controlling path curvature and ensuring the safety and stability of agricultural operations. The simulation experiment results demonstrate that the TLG-PSO algorithm achieved exceptional full-coverage operation performance across four categories of typical test fields. Compared to conventional fixed-direction path planning strategies, the algorithm reduced average total path length by 6228 m, improved coverage rate by 1.31%, achieved average labor savings of 96.32%, and decreased energy consumption by 6.45%. In large-scale comprehensive testing encompassing 1–27 field plots, the proposed algorithm reduced average total path length by 8472 m (a 5.45% decrease) and achieved average energy savings of 44.21 kW (a 5.48% reduction rate). Comparative experiments with mainstream intelligent optimization algorithms, including GA, ACO, PSO, BreedPSO, and SecPSO, revealed that TLG-PSO reduced path length by 0.16%–0.74% and decreased energy consumption by 0.53%–2.47%. It is worth noting that for large-scale field operations spanning hundreds of acres, even an approximately 1% path reduction translates to substantial fuel and operational time savings, which holds significant practical implications for large-scale agricultural production. Furthermore, TLG-PSO demonstrated exceptional performance in terms of algorithm convergence speed and computational efficiency. The improved TLG-PSO algorithm provides a feasible and efficient solution for autonomous operation of large-scale agricultural machinery. Full article
(This article belongs to the Special Issue Robotic Systems for Future Farming)
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