Harvesting the Future: Transforming Agricultural Practices Through AI Application

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1224

Special Issue Editors


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Guest Editor
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Interests: soil remediation and protection; electric agricultural machinery; intelligent agricultural machinery; intelligent agriculture; agricultural low-carbon technology
Special Issues, Collections and Topics in MDPI journals
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Interests: harvesting machinery; agricultural robots; man-machine coordination; machine learning; image identification; navigation
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Interests: machine vision; precision agriculture; dynamic path planning; algorithm optimization; agricultural sensors

Special Issue Information

Dear Colleagues,

How to enhance agricultural production efficiency, ensure food security and achieve sustainable development under limited resource conditions have become urgent questions that need addressing in the future of agriculture. The rapid development of artificial intelligence (AI) has brought unprecedented opportunities to the agricultural sector, and AI is gradually becoming the core driving force for transforming traditional agricultural practices and promoting agricultural modernization.

With its powerful capabilities in data processing, pattern recognition and predictive analysis, artificial intelligence is penetrating into every aspect of agricultural production, management and sales. There are myriad agricultural applications for AI, including genetic analysis and variety optimization in intelligent breeding, intelligent irrigation and fertilization decision-making in precision agriculture, early monitoring and early warning for disease and pest diagnosis, intelligent agricultural machinery and agricultural robots for smart production, intelligent detection and grading of agricultural product quality, intelligent management of agricultural supply chain, and prediction and risk assessment of agricultural markets. The application of AI is profoundly transforming the face of agriculture, significantly enhancing the precision, efficiency and sustainability of agricultural production.

This Special Issue focuses on the frontier topic "Harvesting the Future: Transforming Agricultural Practices Through AI Application", aiming to bring together researchers, scholars, engineers and industry practitioners in the field of agriculture from around the world to jointly discuss the latest research progress, innovative application cases and future development trends of AI technology in agriculture. We hope that through this Special Issue, we can establish a cross-disciplinary and cross-field communication platform, promoting intellectual exchange and cooperation among professionals with different disciplinary backgrounds, accelerating the innovative application and promotion of AI technology in agriculture and providing new ideas and solutions to address the challenges faced by agriculture globally.

We look forward to receiving your contributions.

Dr. Zhaoyang Yu
Dr. Song Mei
Dr. Hongbo Xu
Guest Editors

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Keywords

  • intelligent breeding
  • intelligent rearing
  • intelligent planting
  • precision agriculture
  • intelligent pest and disease diagnosis
  • intelligent agricultural machinery
  • agricultural robots
  • agricultural sensors
  • intelligent management of agricultural production
  • digital village

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Published Papers (1 paper)

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Research

24 pages, 5084 KB  
Article
Real-Time Constrained Visual Servoing for Agricultural Harvesting Robots via MPC-Guided Reinforcement Learning
by Liangzheng Gao, Qingchun Feng, Shiqi Chen, Zhijie Yang, Fengcui Fan, Lin Chen and Chunjiang Zhao
AI 2026, 7(4), 124; https://doi.org/10.3390/ai7040124 - 1 Apr 2026
Viewed by 822
Abstract
With the intensification of global agricultural labor shortage and scaled development of facility agriculture, autonomous precision harvesting robots for unstructured greenhouse environments have become an urgent need. For cluster-picking crops such as tomatoes, visual servoing enables real-time closed-loop control of the end-effector pose, [...] Read more.
With the intensification of global agricultural labor shortage and scaled development of facility agriculture, autonomous precision harvesting robots for unstructured greenhouse environments have become an urgent need. For cluster-picking crops such as tomatoes, visual servoing enables real-time closed-loop control of the end-effector pose, addressing challenges of random fruit distribution and variable stem orientations. However, existing methods struggle to balance constraint handling with real-time efficiency. This paper proposes an MPC-Guided Reinforcement Learning visual servoing framework, innovatively combining the planning capability of optimal control with the adaptive learning ability and real-time inference advantages of reinforcement learning. The approach adopts a teacher–student paradigm: expert trajectories from the MPC controller warm-start the reinforcement learning policy through behavior cloning, followed by PPO-based fine-tuning with adaptive gain regulation and stagnation-enhanced exploration mechanisms. Simulation experiments demonstrate a 95% success rate with average positioning and orientation errors of 13.6 mm and 0.009 rad respectively. Compared to MPC baseline, task steps are reduced by 53.4%; compared to Standard PPO, success rate improves by 6%. Greenhouse field validation achieves 85.3% picking success rate and 5.63 s per fruit operation time, confirming the framework’s excellent balance among control precision, robustness, and efficiency for high-precision robotic harvesting in unstructured agricultural environments. Full article
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