[{
  "type": "article-journal",
  "title": "OPTIMIZED DEEP BELIEF NETWORK FOR MULTI-DISEASE CLASSIFICATION AND SEVERITY ASSESSMENT IN STRAWBERRIES",
  "author": [
    {
      "family": "R",
      "given": ""
    },
    {
      "family": "K",
      "given": ""
    },
    {
      "family": "M",
      "given": ""
    },
    {
      "family": "A",
      "given": ""
    }
  ],
  "issued": {
    "date-parts": [[2025]]
  },
  "container-title": "Journal of Animal and Plant Sciences",
  "ISSN": "1018-7081",
  "volume": "35",
  "issue": "2",
  "page": "482-497",
  "DOI": "https://doi.org/10.36899/JAPS.2025.2.0040",
  "abstract": "<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>",
  "publisher": "Pakistan Agricultural Scientists Forum",
  "URL": "https://thejaps.org.pk/AbstractView.aspx?mid=2024-JAPS-2088"
}]