Detection and Classification of Diseases in Multi-Crop Leaves using LSTM and CNN Models

TL;DR

This paper demonstrates that CNN and LSTM models can effectively classify plant leaf diseases with high accuracy, aiding early detection and crop management in agriculture.

Contribution

It introduces a combined CNN and LSTM approach for multi-crop leaf disease classification using a large dataset, achieving high accuracy and reliability.

Findings

CNN achieved 96.4% validation accuracy

LSTM reached 93.43% validation accuracy

Deep learning models are effective for plant disease detection

Abstract

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.