Accurate and efficient detection of dragon fruit ripeness is crucial for optimizing harvesting schedules, reducing post-harvest losses, and ensuring fruit quality. This research investigates applying optimized hybrid deep learning (DL) models for intelligent dragon fruit ripeness classification using a dataset of 2,563 images. The feature extraction using pre-trained CNNs, specifically DenseNet-50 and ResNet-50, followed by dimensionality reduction using Principal Component Analysis (PCA). The reduced feature sets are then fed into various classifiers, including Support Vector Machines (SVM) with linear and RBF kernels, a Voting ensemble of SVMs, and a Multi-Layer Perceptron (MLP). The performance of models is evaluated using key metrics such as accuracy, AUC, etc. The experimental findings indicate that the DenseNet-50 features combined with PCA and an SVM Voting ensemble achieve the highest classification accuracy of 97.71%, along with a balanced recall, precision, and F1-score of 0.96. The ResNet-50 features coupled with an MLP also exhibit competitiveperformance.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Angajala Guna Sai Abhishek, Ravi Kumar T, Panduranga Vital Terlapu, Chalapathi Rao Tippana and Ramkishor Pondreti;
Methodology: Angajala Guna Sai Abhishek and Ravi Kumar T;
Visualization: Panduranga Vital Terlapu, Chalapathi Rao Tippana and Ramkishor Pondreti;
Investigation: Angajala Guna Sai Abhishek and Ravi Kumar T;
Writing- Reviewing and Editing: Angajala Guna Sai Abhishek, Ravi Kumar T, Panduranga Vital Terlapu, Chalapathi Rao Tippana and Ramkishor Pondreti;
All authors reviewed the results and approved the final version of the manuscript.
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We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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Ravi Kumar T
Department of Computer Science and Engineering, Aditya Institute of Technology and Management, Tekkali, Srikakulam, Andhra Pradesh, India.
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Cite this article
Angajala Guna Sai Abhishek, Ravi Kumar T, Panduranga Vital Terlapu, Chalapathi Rao Tippana and Ramkishor Pondreti, “Intelligent Fruit Detection System Using Optimized Hybrid Deep Learning Models”, Journal of Machine and Computing, vol.5, no.3, pp. 1386-1395, July 2025, doi: 10.53759/7669/jmc202505109.
