17 Venkatesh R Vijayalakshmi K Geetha M Bhuvanesh A Journal of Animal and Plant Sciences JAPS 20252025/04/28 35 2 482-497 1018-7081 https://doi.org/10.36899/JAPS.2025.2.0040 <p><span lang="EN-IN">Anthracnose, powdery mildew and gray mold are among the most destructive diseases affecting strawberries (<em>Fragaria ananassa</em>&nbsp;Duchesne), capable of rapidly spreading to healthy plants and causing significant global yield losses. Early detection of these diseases is challenging due to their f ast progression, yet it is crucial for improving strawberry yield and productivity. To address this issue, an optimal clustering-based deep learning model is proposed for the segmentation and classification of strawberry diseases using computer aided design. Initially, the input images undergo pre-processing, and the diseased areas are segmented using optimal fuzzy c means clustering with optimal centers selected by the sand cat swarm algorithm</span>. Finally, features were extracted and classified using the deep belief network model. The proposed analysis on a&nbsp;<span lang="EN-IN">strawberry disease detection dataset&nbsp;</span>demonstrated a high classification accuracy of 98.8%. The results show that the integration of optimal clustering with the deep belief network model effectively enhances classification accuracy, enabling early and accurate identification of disease lesion.</p> Computer aided design, Deep learning, Optimal clustering, Segmentation, Strawberry disease Pakistan Agricultural Scientists Forum https://thejaps.org.pk/AbstractView.aspx?mid=2024-JAPS-2088