Volume 8 • Issue 1 • PP: 13–19 • 2026
A Hybrid CNN Bi-LSTM Framework for Multi-Class Plant Disease Detection and Health Value Estimation
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Accurate and early identification of plant diseases is essential for ensuring sustainable agriculture and maximizing crop productivity. This paper presents a hybrid deep learning framework integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks for multi-class plant disease detection, classification, and Plant Health Value (PHV) estimation. The proposed framework begins with a comprehensive data preprocessing pipeline involving image resizing, normalization, and augmentation to improve model robustness. The CNN module extracts critical spatial and visual features such as lesion shape, leaf texture, and color intensity, while the BiLSTM model captures temporal and sequential feature correlations to accurately learn disease progression patterns. A Decision Support System (DSS) is incorporated to compute the Plant Health Value (PHV), where PHV ranges from 0% (Healthy) to 100% (Severely Unhealthy), indicating the severity of disease infection. Additionally, the DSS generates actionable recommendations to assist in early intervention and treatment planning. Experimental results on a multi-species plant dataset demonstrate that the proposed CNN–BiLSTM hybrid model significantly improves accuracy, interpretability, and early disease prediction compared to conventional CNN based methods, offering a robust and intelligent framework for automated plant health monitoring.
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References
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