Hybridizing Long Short-Term Memory and Bi-Directional Long Short-Term Memory Models for Efficient Classification: A Study on <i>Xanthomonas axonopodis</i> pv. <i>phaseoli</i> (XaP) in Two Bean Varieties

Abstract

This study was conducted on Xanthomonas axonopodis pv, which causes significant economic losses in the agricultural sector. Here, we study a common bacterial blight disease caused by the phaseoli (XaP) bacterial pathogen on Üstün42 and Akbulut bean genera. In this study, a total of 4000 images, healthy and diseased, were used for both bean breeds. These images were classified by AlexNet, VGG16, and VGG19 models. Later, reclassification was performed by applying pre-processing to the raw images. According to the results obtained, the accuracy rates of the pre-processed images classified by the VGG19, VGG16 and AlexNet models were determined as 0.9213, 0.9125 and 0.8950, respectively. The models were then hybridized with LSTM and BiLSTM for raw and pre-processed images and new models were created. When the performance of these hybrid models was evaluated, it was found that the models hybridized with LSTM were more successful than the simple models, while the models hybridized with BiLSTM gave better results than the models hybridized with LSTM. In particular, the VGG19+BiLSTM model attracted attention by achieving 94.25% classification accuracy with pre-processed images. This study emphasizes the effectiveness of image processing techniques in agriculture in the field of disease detection and is important as a new dataset in the literature for evaluating the performance of hybridized models.

Keywords

• common bacterial blight
• LSTM
• BiLSTM
• dry bean disease
• hybrid CNN models