PLANT HEALTH MONITORING WITH AI-POWERED CAMERAS

Abstract

Efficient and accurate plant health monitoring is essential for optimizing agricultural productivity and ensuring sustainable farming practices. Traditional methods of assessing plant health, which often involve manual inspections, are time-consuming, labor-intensive, and prone to error, especially over large crop areas. Recent advancements in artificial intelligence (AI) and imaging technologies have led to the development of AI-powered cameras capable of automating plant health monitoring through real-time image analysis. This study presents a framework that integrates AI-powered cameras with machine learning algorithms to detect early signs of plant stress, disease, and nutrient deficiencies. By analyzing visual indicators such as leaf color, texture, and structural anomalies, the AI system can identify issues like chlorosis, fungal infections, and pest infestations. The captured images are processed using convolutional neural networks (CNNs) to classify health conditions and provide actionable insights for targeted intervention. The proposed approach not only reduces the need for manual inspections but also enhances early detection and precision in agricultural practices. Field experiments demonstrate that the AI-powered system achieves high accuracy in detecting common plant health issues, paving the way for a scalable, data-driven solution that supports farmers in making timely, informed decisions to maintain crop health and improve yields.