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In this study, three methodologies were explored for papaya disease classification: (i) Transfer learning using a convolutional neural network (CNN) model, (ii) deep feature extraction followed by traditional machine learning (ML) models, and (iii) the application of principal component analysis (PCA) to retrieve deep features, using a best-performing model, ResNet101. Seven CNN architectures achieved over 90% classification accuracy, demonstrating the effectiveness of CNNs in fruit disease detection. The retrieved deep features combined with PCA further improved accuracy, with support vector machine achieving 99.87%, random forest classifier 99.54%, multilayer perceptron 99.08%, and k-nearest neighbors 91.63%. These outcomes highlight the advantages of integrating deep learning and traditional ML approaches. However, limitations include the exclusion of disease severity in classification and the lack of testing on large, diverse samples. Future research should concentrate on these by applying broader samples, considering environmental conditions, and exploring more advanced CNN architectures to enhance model robustness and generalizability for practical agricultural applications.<\/jats:p>","DOI":"10.1515\/jisys-2024-0523","type":"journal-article","created":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T14:21:24Z","timestamp":1758464484000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimizing papaya disease classification: A hybrid approach using deep features and PCA-enhanced machine learning"],"prefix":"10.1515","volume":"34","author":[{"given":"Ashoka Kumar","family":"Ratha","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Sambalpur University , Sambalpur , Odisha, 768 019 , India"}]},{"given":"Nalini Kanta","family":"Barpanda","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sambalpur University , Sambalpur , Odisha, 768 019 , India"}]},{"given":"Prabira Kumar","family":"Sethy","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Sambalpur University , Sambalpur , Odisha, 768 019 , India"}]},{"given":"Santi Kumari","family":"Behera","sequence":"additional","affiliation":[{"name":"Department of CSE, VSSUT, Burla , Sambalpur , Odisha, 768 019 , India"}]},{"given":"Aziz","family":"Nanthaamornphong","sequence":"additional","affiliation":[{"name":"College of Computing, Prince of Songkla University , Phuket, 83120 , Thailand"}]}],"member":"374","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"2025122009032218492_j_jisys-2024-0523_ref_001","unstructured":"Food and Agriculture Organization of the United Nations. 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