Tomato Disease Recognition: Advancing Accuracy Through Xception and Bilinear Pooling Fusion

Abstract

Accurate detection and classification of tomato diseases are essential for effective disease management and maintaining agricultural productivity. This paper presents a novel approach to tomato disease recognition that combines Xception, a pre-trained convolutional neural network (CNN), with bilinear pooling to advance accuracy. The proposed model consists of two parallel Xception-based CNNs that independently process input tomato images. Bilinear pooling is applied to combine the feature maps generated by the two CNNs, capturing intricate interactions between different image regions. This fusion of Xception and bilinear pooling results in a comprehensive representation of tomato diseases, leading to improved recognition performance. Extensive experiments were conducted on a diverse dataset of annotated tomato disease images to evaluate the effectiveness of the suggested approach. The model achieved a remarkable test accuracy of 98.7%, surpassing conventional CNN approaches. This high accuracy demonstrates the efficacy of the integrated Xception and bilinear pooling model in accurately identifying and classifying tomato diseases. The implications of this research are significant for automated tomato disease recognition systems, enabling timely and precise disease diagnosis. The model’s exceptional accuracy empowers farmers and agricultural practitioners to implement targeted disease management strategies, minimizing crop losses and optimizing yields.