Hybrid CNN and Transformer-Based Sequential Learning Techniques for Plant Disease Classification

Plant diseases have important consequences for livelihoods and economies, both on local and global scales, whereby the spread of plant pathogens can lead to high levels of damage to agricultural productivity. In this regard, deep learning (DL) has evolved as a promising remedy. Nevertheless, the level of diversity of plant species still presents a constant challenge to effective plant disease classification. This research proposed a two-stream convolution operator combining bidirectional long short-term memory (BiLSTM) for effective feature mapping and learning. Moreover, a transformer network (TransNet) is constructed based on sequential learning techniques (SLT) of long short-term memory (LSTM), bidirectional LSTM (BiLSTM), sequence-to-sequence, and gated recurrent unit to highlight important plant disease features. The network is trained on the PlantVillage dataset, which includes 38 species of plant diseases. The experimental results indicate that the proposed model achieves scores of 97.88%, 97.93%, and 97.62% for Accuracy, Precision, and Recall, respectively, along with a low training loss of 0.0696 and a minimal training time of 83.17 minutes. The proposed approach represents a significant improvement over these previous models and demonstrates the potential for the efficiency and accuracy of plant disease classification to be further improved, contributing to the realization of modern technology in the agricultural industry.