Deep Learning-Based Approach for Cotton Plant Disease Identification

Abstract

Agriculture is crucial to India's economy, contributing 17% to the GDP and supporting over 60% of the population. Cotton is a key crop, essential for Indian farmers and the textile industry. However, cotton leaf diseases have long posed a challenge, affecting crop yields. Monitoring these crops manually is time-consuming and expensive.To address this, both traditional and computer-assisted methods have been used for early detection. Convolutional Neural Networks (CNNs) have shown great potential in classifying diseases but require large datasets for training. In this study, a dataset of 1,951 images of cotton leaves, affected by four major diseases, was compiled using optical sensors. These images were processed with Keras to enhance the database for more accurate disease detection.The goal was to develop a CNN-based method that identifies the health of cotton plants through user-uploaded images. The CNN architecture achieved an accuracy of 98.765%, demonstrating its ability to detect diseases early. This approach offers a valuable tool for farmers to address crop diseases promptly, potentially improving cotton yield and minimizing losses.

Plant phenotyping relevance

綿花葉の画像から病害という植物状態を推定するCNN手法の開発とデータセット構築が中心であり、植物フェノタイピング手法として適格。

Code and data availability

The paper uses a Kaggle cotton disease image dataset and a trained CNN (CNNmodel.h5), but no public URL, deposit, or availability statement for the dataset, code, or model is provided in the supplied blocks. The only URL is the article DOI itself.

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