Optimized deep belief network for multi-disease classification and severity assessment in strawberries

R. Venkatesh; K. Vijayalakshmi; M. Geetha; A. Bhuvanesh

doi:10.36899/JAPS.2025.2.0040

Author(s)

R. Venkatesh Department of Computer Science and Engineering, Ramco Institute of Technology,Rajapalayam, Tamil Nadu,India

K. Vijayalakshmi Department of Computer Science and Engineering, Ramco Institute of Technology,Rajapalayam, Tamil Nadu,India

M. Geetha Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering,Coimbatore, Tamil Nadu,India

A. Bhuvanesh Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology,Tirunelveli, Tamil Nadu,India

Abstract

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.

Publication Details

Page(s) 482-497

DOI 10.36899/JAPS.2025.2.0040

Published Journal: Journal of Animal and Plant sciences, Volume: 35, Issue: 2, Year: 2025

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Keywords

Deep learning, Strawberry disease, segmentation, Optimal clustering, Computer Aided Design

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