The Study of Current IoT Techniques Used in Plant Disease Detection

Abstract

Plant diseases significantly affect global agricultural productivity and food security. Conventional disease detection techniques rely heavily on manual inspection, which is labor-intensive, time-consuming, and prone to subjectivity. The emergence of the Internet of Things (IoT) has enabled real-time monitoring of plant health and automated disease detection using interconnected sensors, imaging devices, and intelligent data processing frameworks. This paper presents a systematic review of current IoT techniques used in plant disease detection, focusing on sensor technologies, communication protocols, data processing platforms, and machine learning integration. A structured review methodology is adopted to analyze recent literature, and comparative tables are provided to highlight the strengths and limitations of existing systems. The study demonstrates that integrating IoT with artificial intelligence significantly improves detection accuracy, response time, and sustainability in precision agriculture.