CNN Based Plant Leaf Disease Detection Using Raw Leaf Images for Efficient Plant Health Monitoring in Agricultural IoT

Abstract

Agriculture is a major economic driver of both the high and low income countries across the globe. Hence it is important to modernize agricultural practices by incorporating modern technological developments as a continuous process. Among the various challenges, plant leaf diseases pose a significant threat, leading to reduced agricultural yields and economic losses. Early detection of these diseases is crucial for timely intervention, yet traditional methods relying on human visual inspection are often delayed, incomplete, and unreliable. To address this, our study employs a Convolutional Neural Network (CNN) to analyze plant leaf images taken from New plant dataset, resulting in a highly accurate model for detecting various leaf diseases of Apple, Potato, Strawberry and Tomato. The developed CNN model achieved an accuracy of 90.91%, offering a promising tool for improving agricultural productivity by minimizing disease-induced crop damage.

Plant phenotyping relevance

植物葉画像から病害状態をCNNで推定する手法が研究の中心であり、植物の病徴に基づくフェノタイピング手法の開発・評価に該当する。

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