Recent Developments and Future Prospects in the Integration of Machine Learning in Mechanized Systems for Autonomous Spraying: A Brief Review

Abstract

The integration of Machine Learning (ML) into autonomous spraying systems (1) is one of the major developments in digital precision agriculture (2) that is significantly improving resource efficiency, sustainability, and production. This study looks at current advancements in machine learning applications for automated spraying in agricultural mechanization (3), emphasising new innovations, difficulties, and prospects. The study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics (4), sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection (5) as well as accurate application of plant protection products. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations' efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advancements, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods.