An Intelligent Hybrid Deep Learning Model Integrating CNN, Transformer, and LSTM for Precision Cotton Disease Diagnosis

Abstract

Cotton leaf diseases significantly affect crop yield and quality, leading to substantial economic losses in agricultural regions. Early and accurate detection of these diseases is essential for sustainable crop management and precision agriculture. Existing studies primarily rely on conventional Convolutional Neural Networks (CNNs) or object detection models that focus on static image classification, often lacking global contextual learning and temporal disease progression modeling. To address these limitations, this study proposes a novel hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Transformer-based attention mechanisms, and Long Short-Term Memory (LSTM) networks for spatio-temporal cotton leaf disease detection and classification.In the proposed architecture, a pre-trained EfficientNet backbone is employed for spatial feature extraction, followed by a multi-head self-attention Transformer block to capture global contextual relationships within leaf patterns. An LSTM layer is incorporated to enable temporal sequence modeling, allowing the framework to be extended toward disease progression analysis and forecasting. The model is trained and evaluated on a structured cotton leaf dataset with data augmentation and fine-tuning strategies to enhance generalization performance.Experimental results demonstrate that the proposed hybrid model achieves superior classification accuracy, improved robustness, and enhanced feature representation compared to conventional CNN-based approaches. Confusion matrix analysis and ROC curve evaluation confirm high discriminative capability across multiple disease classes. The integration of spatial, global attention, and temporal learning components makes the proposed framework suitable for intelligent precision agriculture systems and real-time disease monitoring applications.The developed model provides a scalable foundation for future integration with IoT-based environmental sensing systems, enabling predictive crop health management and early warning advisory mechanisms in smart farming environments.