Machine Learning Integrated IoT System for Crop Disease Detection

Abstract

Agriculture remains the backbone of global food security, yet crop diseases significantly reduce yield and quality, posing challenges for farmers worldwide. Traditional disease detection methods are often labor-intensive, time-consuming, and prone to error. Integrating Internet of Things (IoT) technology with machine learning (ML) algorithms offers a revolutionary solution for early disease detection and precision farming. IoT devices such as sensors, drones, and smart cameras continuously collect environmental and crop data—including temperature, humidity, soil moisture, and leaf imagery—while machine learning models analyze this data to identify disease patterns accurately. This paper reviews current advances in ML-integrated IoT systems for crop disease detection, presents a conceptual architecture for Indian farming contexts, and highlights challenges, practical applications, and potential benefits. Case studies demonstrate that such systems enhance detection accuracy, optimize pesticide usage, reduce crop losses, and promote sustainable agriculture. By combining real-time monitoring with predictive analytics, ML-IoT systems represent a crucial tool for modernizing agriculture and supporting food security in India and globally.