Plant Disease Detection Using CNN

Abstract

Abstract: Agriculture forms a cornerstone of the Indian economy, with food and cash crops playing a critical role in sustaining both the environment and human livelihoods. However, crop yields are significantly impacted each year by various plant diseases. The lack of efficient diagnostic methods, combined with limited awareness of disease symptoms and treatment options, often leads to widespread crop losses. This study explores the application of machine learning for plant disease detection, focusing on Convolutional Neural Networks (CNNs) to identify and classify diseases. The proposed approach employs advanced image processing techniques to analyze infected leaf regions, examining metrics such as time complexity and lesion area. The model was trained and tested on a curated dataset comprising 15 cases, including 12 disease categories such as Bell Pepper Bacterial Spot, Potato Early Blight, and Tomato Leaf Mold, alongside 3 categories of healthy leaves. The system achieved a test accuracy of 88.8%, demonstrating its potential for accurate plant disease detection. Performance evaluation was conducted using standard metrics to validate the model's reliability.

Plant phenotyping relevance

感染葉の画像から病害を分類し、病斑面積を解析するCNN手法の開発・評価が中心であり、植物の病害状態を直接推定するため含める。

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