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Robotic Systems for Future Farming

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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

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28 pages, 5802 KB

Open AccessArticle

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 648

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, 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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