Automated Pesticide Spraying Systems for Sustainable Agriculture: A Systematic Review and an Integrated Framework for Precision Crop Protection

Abstract

Conventional pesticide application is associated with high overuse rates, non-uniform distribution, and exposure of farm workers to harmful substances. Automated spraying systems with built-in sensing, artificial intelligence (AI), embedded control, robotics, and internet of things (IoT) enable precise and localized treatment of crops. This paper provides a PRISMA-based systematic review of 68 articles on automated pesticide spraying found between 2015 and 2025. The initial search yielded 512 records in seven databases. After the first screening, 68 articles were included in the analysis. The review discusses pesticide spraying automation through the lens of sensing technologies, control platforms, and communication channels used in the studies, pesticide-use reduction, and environments where the technology was validated. Sensor fusion and vision-based variable-rate technologies were found to reduce pesticide use by 44-70% compared to conventional approaches, while 72% of the analyzed articles were validated in laboratory or small-scale field settings. Cost, complexity, and limited interoperability were identified as key barriers to large-scale commercialization. Finally, based on the review, a six-layer framework comprising sensing, processing, decision-making, communication, spraying, and monitoring is proposed.