Sustainable AI-Driven Precision Agriculture: Real-Time Crop Health Monitoring and Weed Detection for Targeted Spraying

Abstract

The Study of Artificial Intelligence integrated with Unmanned Arieal vehicles has Changed precision agriculture by providing novel solutions for crop health monitoring, weed detection, and targeted spraying.  This research helps in designing and development of AI integrated agriculture drones that are capable of real time crop surveillance, more accurate weed detection in the farm and pesticide spraying without using GPS technology. The system utilizes Deep Learning models like region-based Convolutional Neural Networks (R-CNNs) and Convolutional Neural Networks (CNNs) for multispectral and high-resolution image detection and analysis taken by onboarding cameras. Precision spraying system is connected to detection modules to enable precise application of agrochemicals and pesticides that will reduce wastage of chemicals, decrease environmental load and encourage sustainable agriculture. The process is implemented with system design, hardware integration, AI model design, real-time processing and testing. The result of deployment demonstrates the efficacy of the system in maximizing the crop yield and reducing the chemical consumption, providing a scalable solution for various crops and farm sizes.