用CNN和LSTM识别38类作物病害，准确率超96%。
Detection and Classification of Diseases in Multi-Crop Leaves using LSTM and CNN Models
- 结合CNN与LSTM模型分析叶片图像，提升疾病分类精度。
- CNN在38类病害上验证准确率达96.4%，训练准确率99.1%。
- 适合农业监测、智能种植系统研发人员参考。
植物病害严重威胁农业，导致产量下降和食品质量受损。早期检测与分类对减少损失、改善管理至关重要。本研究采用卷积神经网络（CNN）和长短期记忆（LSTM）模型，基于包含70,295张训练图像和17,572张验证图像的38类病害数据集进行分类。CNN使用Adam优化器，学习率为0.0001，以分类交叉熵为损失函数，经10轮训练后，训练准确率达99.1%，验证准确率为96.4%。LSTM模型验证准确率为93.43%。通过精确率、召回率、F1分数及混淆矩阵评估性能，证实了基于CNN方法的可靠性。结果表明，深度学习模型尤其是CNN，可实现高效且可扩展的植物病害分类，支持农业监测的实际应用。
原文摘要 · Abstract (English)
Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality. Early detection and classification of these diseases are essential for minimising losses and improving crop management practices. This study applies Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to classify plant leaf diseases using a dataset containing 70,295 training images and 17,572 validation images across 38 disease classes. The CNN model was trained using the Adam optimiser with a learning rate of 0.0001 and categorical cross-entropy as the loss function. After 10 training epochs, the model achieved a training accuracy of 99.1% and a validation accuracy of 96.4%. The LSTM model reached a validation accuracy of 93.43%. Performance was evaluated using precision, recall, F1-score, and confusion matrix, confirming the reliability of the CNN-based approach. The results suggest that deep learning models, particularly CNN, enable an effective solution for accurate and scalable plant disease classification, supporting practical applications in agricultural monitoring.
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