A Hybrid CNN Bi-LSTM Framework for Multi-Class Plant Disease

Detection and Health Value

Estimation

Janani J.1,* Gautham Praveen R.2 Suguna C.2 Ponni Vinothini K.3

1 Student, Department of Computer Science and Engineering, Syed Ammal Engineering College, Ramanathapuram-623501, India

2 Assistant Professor, Department of Computer Science and Business System, Syed Ammal Engineering College,

Ramanathapuram-623501, India

3 Assistant Professor, Department of Computer Science Engineering, Syed Ammal Engineering College, Ramanathapuram-623501,

India

Received: January 30, 2026 Revised: February 27 2026 Accepted: March 29, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Plant Disease Detection CNN BiLSTM Deep Learning Decision Support System Plant Health

Value Precision Agriculture

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