Revolutionizing Agriculture With Automated Plant Disease Detection: Techniques, Applications, Challenges, Future Directions, and Sustainability Impacts

Abstract

Automated plant disease detection using computer vision has transformed agriculture by addressing challenges in plant health management, productivity, and sustainability. This chapter explores advancements from traditional methods to AI-enhanced deep learning and multi-modal imaging, enabling early disease detection, real-time processing, and precise interventions. Applications like precision agriculture, IoT integration, and data-driven decision-making foster eco-friendly practices and resource efficiency. Despite challenges such as data quality, scalability, and accessibility, future innovations in data collection, sustainable hardware, and collaboration promise to shape resilient agricultural systems. By aligning technology with sustainability, automated plant disease detection supports food security, environmental conservation, and the evolution of modern farming practices.