Autonomous Robotic Pruning in Orchards and Vineyards: a Review

2025-05-12

AI Summary

Manual pruning in orchards and vineyards situated on complex terrain imposes high labor intensity—accounting for over 25% of annual labor costs—while conventional machinery exhibits poor adaptability to such unstructured environments. Method: This paper systematically reviews autonomous pruning robot research from 2014 to 2024, focusing on apple, grape, and cherry crops. It integrates AI-driven plant skeleton extraction, multimodal perception, and adaptive control into a unified framework for unstructured field conditions, combining deep-learning-based visual recognition, 3D point-cloud skeleton reconstruction, lightweight mobile platforms, and real-time closed-loop motion control. Contribution/Results: The review clarifies the technological evolution trajectory and identifies three persistent bottlenecks: perception robustness under variable lighting and occlusion, dynamic branch modeling, and field-level generalizability across species and terrains. It proposes a practical, cross-species research roadmap for pruning strategy optimization and system integration, advancing the deployment readiness of agricultural robotics in complex horticultural settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Manual pruning is labor intensive and represents up to 25% of annual labor costs in fruit production, notably in apple orchards and vineyards where operational challenges and cost constraints limit the adoption of large-scale machinery. In response, a growing body of research is investigating compact, flexible robotic platforms capable of precise pruning in varied terrains, particularly where traditional mechanization falls short. This paper reviews recent advances in autonomous robotic pruning for orchards and vineyards, addressing a critical need in precision agriculture. Our review examines literature published between 2014 and 2024, focusing on innovative contributions across key system components. Special attention is given to recent developments in machine vision, perception, plant skeletonization, and control strategies, areas that have experienced significant influence from advancements in artificial intelligence and machine learning. The analysis situates these technological trends within broader agricultural challenges, including rising labor costs, a decline in the number of young farmers, and the diverse pruning requirements of different fruit species such as apple, grapevine, and cherry trees. By comparing various robotic architectures and methodologies, this survey not only highlights the progress made toward autonomous pruning but also identifies critical open challenges and future research directions. The findings underscore the potential of robotic systems to bridge the gap between manual and mechanized operations, paving the way for more efficient, sustainable, and precise agricultural practices.

Problem

• Labor-intensive manual pruning increases fruit production costs
• Robotic platforms address pruning challenges in varied terrains
• AI-driven machine vision improves precision in agricultural pruning

Innovation

• Compact robotic platforms for precise pruning
• Machine vision and AI for plant skeletonization
• Autonomous control strategies in varied terrains