Anthracnose, powdery mildew and gray mold are among the most destructive diseases affecting strawberries (Fragaria ananassa 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. Finally, features were extracted and classified using the deep belief network model. The proposed analysis on a strawberry disease detection dataset 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.