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

2025-03-01

Journal of Innovative Image Processing

AI Summary

Addressing the challenges of early diagnosis and poor cross-crop generalization in multi-crop leaf disease identification, this study constructs and comparatively evaluates two end-to-end deep learning architectures—CNN and LSTM—using a large-scale dataset of 72,000 leaf images spanning multiple crop species. It presents the first systematic performance assessment of both models on a fine-grained, 38-class multi-crop disease recognition task. Models are trained with data augmentation, the Adam optimizer, and categorical cross-entropy loss. Experimental results show that the CNN achieves a validation accuracy of 96.4%, significantly outperforming the LSTM (93.43%); all key metrics—including F1-score—meet stringent evaluation criteria. This work demonstrates that CNNs exhibit superior fine-grained discriminative capability and cross-crop generalization for plant disease recognition. Moreover, the optimized CNN model is lightweight and robust, exhibiting strong potential for real-world field deployment in precision agriculture applications.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Learning & Optimization for CV
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Detect and classify diseases in multi-crop leaves using deep learning
• Improve crop management by early disease identification with CNN and LSTM
• Evaluate model performance for scalable agricultural disease monitoring solutions

Innovation

• CNN and LSTM models for plant disease classification
• 70,295 training images across 38 disease classes
• CNN achieved 96.4% validation accuracy