A CNN-BiLSTM Hybrid for Plant Leaf Disease Classification: Comparative Performance Evaluation of Deep Learning Algorithms

Abstract

—The existence of plant leaf diseases is a big problem for farmers all over the world because they make crops less healthy and less plentiful, which puts global food security at risk. The most common problems with diagnosing plant leaf diseases are a lack of experience, different ways of undertaking visual assessments, and image overlaps, all of which can lead to wrong diagnoses. This study introduces a hybrid deep learning architecture that integrates squeeze-and-excitation residual blocks, capsule networks, bidirectional long short-term memory, and attention mechanisms. The design utilizes convolutional operators for effective feature extraction, Squeeze-and-Excitation (SE) block for channel reweighting, capsule networks for spatial relationship capture Bidirectional Long Short-Term Memory (BiLSTM) for sequential dependencies, and attention mechanisms for emphasizing prominent features. Experiments were performed on 2 empirical datasets: the Corn Leaf Disease Dataset (CLDD) and the Rice Leaf Disease Dataset (RLDD). The data were divided into 60% for training, 20% for validation, and 20% for testing. The proposed method attained 99.88% training accuracy on CLDD and 100% on RLDD. During testing, the class-wise accuracies were 99.29% for blight and 100% for the other CLDD categories. In the case of RLDD, the accuracies attained were 78.95% for bacterial leaf blight, 85.53% for brown spot, 89.77% for healthy samples, 77.27% for leaf blast, 100% for leaf scald, and 97.73% for narrow brown spot. This work highlights practical potential for deployment in terms of mobile applications, enabling farmers to obtain rapid, reliable, and cost-effective field diagnoses, thereby improving agricultural productivity and sustainability.