Plant Disease Detection & Autonomous Spraying Robot

Abstract

Agricultural productivity faces significant challenges from plant diseases and inefficient pesticide use. This paper presents an autonomous robotic system that integrates real-time machine vision with precision spraying technology. The platform navigates crop fields using a four-wheel drive system while capturing plant images through an onboard camera. These images are processed by a cloud-based Convolutional Neural Network that identifies specific plant diseases with 93% accuracy. Upon detection, the system activates targeted spraying mechanisms that deliver disease-specific pesticides only to infected plants, utilizing multiple chemical reservoirs. Experimental results demonstrate a 70% reduction in pesticide consumption compared to conventional methods while maintaining effective disease control. The system operates on battery power and incorporates real-time monitoring through a web interface and mobile alerts. This research represents a significant advancement in agricultural technology, offering an eco-friendly solution that enhances crop productivity while minimizing environmental impact. The integration of robotics, artificial intelligence, and precision agriculture techniques provides a sustainable approach to plant disease management that can be scaled for various farming applications. The system's practical implementation shows promising results for reducing chemical usage and operational costs while maintaining crop health and yield quality.