Multi leaf disease identification and classification using efficient capsule convolutional shuffle attention network

Plant diseases establish a serious risk to worldwide food safety and agronomic sustainability, leading to substantial losses in harvest and quality. Precise and appropriate experience of these viruses is necessary for employing effective control procedures, diminishing economic victims and confirming food accessibility. However, current detection methods are often hindered by limitations such as insufficient generalizability across various crops, difficulty in handling complex, and noisy backgrounds, and suboptimal performance when addressing diverse and overlapping disease symptoms. This research is driven by the persistent necessity to tackle these issues and delivers a novel approach to improve disease identification and categorization performance. For this need, propose the Efficient Capsule convolutional Shuffle Attention Network (ECSAN), a comprehensive framework specifically designed to classify diseases across five diverse crops: Apple Leaves, Cassava Leaves, Hibiscus, Hyacinth Bean, and Okra Leaves. The framework integrates a fast gradient-domain weighted guided image filter to denoise and improve image superiority and segmentation is done over a dense swin transformer combined with Unet. Statistical features are extracted using general kernel joint non-negative matrix factorization, and the classification progression is optimized through the Enhanced Osprey Optimization Algorithm (EOOA). Implementation outcomes on five standard datasets prove the superiority of ECSAN, achieving 99.9 % accuracy, 99.98 % recall, 99.97 % precision, 99.98 % F1-score, and 99.99 % specificity. Additionally, ECSAN significantly reduces execution time compared to existing methods, emphasizing its efficiency and applicability in agricultural scenarios. This research underscores the urgency of addressing current detection challenges and establishes a robust foundation for advancements in plant pathology.