Residual Network with Multi-scale Information Fusion for Apple Fungal Infection Classification

Abstract

Black rot, Cedar rust, and Scab are three common apple diseases caused by fungal infections. The infected areas not only impair the function of the current parts but also spread to healthy regions of the plant, necessitating early detection in production. Addressing the challenge of subtle lesion features, we have designed a deep learning model that integrates multi-scale information fusion by combining wavelet transform with a residual network, and introducing a multi-channel Collaborative Attention mechanism. In comparisons with similar models, our proposed model achieved the best results, with a classification accuracy of 98.1% for various lesions. It demonstrated excellent stability in data detection imaging under various conditions. This model provides a new perspective for apple lesion detection and may potentially be analogously applied to other types of detection in the future.