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.

Plant phenotyping relevance

植物病害検出に用いるIoTセンサー、画像、データ処理、機械学習を体系的にレビューしており、植物の病害状態を観測・推定するフェノタイピング手法が中心です。

Code and data availability

This is a systematic review of IoT plant disease detection literature. It reports no original phenotyping measurements, datasets, images, code, or models of its own; all cited works are prior publications, and the only URL present is a license link and a ResearchGate reference to an unrelated review article.

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