Detection and Classification of Diseases in Multi-Crop Leaves using LSTM and CNN Models
It addresses crop yield and food quality issues for agriculture, but is incremental as it applies existing deep learning methods to a specific domain.
This study tackled the problem of early detection and classification of plant leaf diseases to reduce agricultural losses, achieving a validation accuracy of 96.4% with a CNN model on a dataset of 70,295 training images across 38 disease classes.
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.