Autonomous Robotic Pruning in Orchards and Vineyards: a Review

TL;DR

This review surveys recent advances in autonomous robotic pruning systems for orchards and vineyards, emphasizing technological progress, challenges, and future directions to improve efficiency and sustainability in precision agriculture.

Contribution

It provides a comprehensive analysis of technological developments in robotic pruning from 2014 to 2024, highlighting innovations in perception, control, and AI integration.

Findings

Significant progress in machine vision and perception technologies.

Identification of key open challenges in robotic pruning systems.

Future research directions for improving autonomy and adaptability.

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.