Multi-modal expert system for automated durian ripeness classification using deep learning
Accurate classification of durian ripeness is essential for quality control and minimizing post-harvest losses. Manual inspection remains subjective and inconsistent, prompting the need for automated methods. We present a multi-modal approach that integrates Convolutional Neural Networks (CNNs) for...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000894 |
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| author | Santi Sukkasem Watchareewan Jitsakul Phayung Meesad |
| author_facet | Santi Sukkasem Watchareewan Jitsakul Phayung Meesad |
| author_sort | Santi Sukkasem |
| collection | DOAJ |
| description | Accurate classification of durian ripeness is essential for quality control and minimizing post-harvest losses. Manual inspection remains subjective and inconsistent, prompting the need for automated methods. We present a multi-modal approach that integrates Convolutional Neural Networks (CNNs) for image-based classification and Recurrent Neural Networks (RNNs) for automatic textual descriptions. Trained on 16,000 annotated images across four ripeness stages, the model achieved high classification accuracy (MobileNetV2: 95.50%) and superior captioning performance (ResNet101 + Bi-GRU: BLEU 0.9974, METEOR 0.9949, ROUGE 0.9164). While weighted summation fusion demonstrated superior performance, concatenation was ultimately chosen for its simplicity and real-world deployment feasibility. Statistical validation using one-way ANOVA (p<0.05) confirmed the significance of the findings. These results highlight the potential of the proposed multi-modal approach as a practical and interpretable framework for automated durian ripeness assessment. |
| format | Article |
| id | doaj-art-7bbb44c2eb0c47a286b4067d1a93a570 |
| institution | Kabale University |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-7bbb44c2eb0c47a286b4067d1a93a5702025-08-20T04:00:33ZengElsevierIntelligent Systems with Applications2667-30532025-09-012720056310.1016/j.iswa.2025.200563Multi-modal expert system for automated durian ripeness classification using deep learningSanti Sukkasem0Watchareewan Jitsakul1Phayung Meesad2Correspondence to: Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, 10800 Bangkok, Thailand.; King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, 10800 Bangkok, ThailandKing Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, 10800 Bangkok, ThailandKing Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Road, Wongsawang, Bangsue, 10800 Bangkok, ThailandAccurate classification of durian ripeness is essential for quality control and minimizing post-harvest losses. Manual inspection remains subjective and inconsistent, prompting the need for automated methods. We present a multi-modal approach that integrates Convolutional Neural Networks (CNNs) for image-based classification and Recurrent Neural Networks (RNNs) for automatic textual descriptions. Trained on 16,000 annotated images across four ripeness stages, the model achieved high classification accuracy (MobileNetV2: 95.50%) and superior captioning performance (ResNet101 + Bi-GRU: BLEU 0.9974, METEOR 0.9949, ROUGE 0.9164). While weighted summation fusion demonstrated superior performance, concatenation was ultimately chosen for its simplicity and real-world deployment feasibility. Statistical validation using one-way ANOVA (p<0.05) confirmed the significance of the findings. These results highlight the potential of the proposed multi-modal approach as a practical and interpretable framework for automated durian ripeness assessment.http://www.sciencedirect.com/science/article/pii/S2667305325000894Expert systemDeep learningConvolutional neural networks (CNN)Recurrent neural networks (RNN)Durian ripeness classificationHybrid AI |
| spellingShingle | Santi Sukkasem Watchareewan Jitsakul Phayung Meesad Multi-modal expert system for automated durian ripeness classification using deep learning Intelligent Systems with Applications Expert system Deep learning Convolutional neural networks (CNN) Recurrent neural networks (RNN) Durian ripeness classification Hybrid AI |
| title | Multi-modal expert system for automated durian ripeness classification using deep learning |
| title_full | Multi-modal expert system for automated durian ripeness classification using deep learning |
| title_fullStr | Multi-modal expert system for automated durian ripeness classification using deep learning |
| title_full_unstemmed | Multi-modal expert system for automated durian ripeness classification using deep learning |
| title_short | Multi-modal expert system for automated durian ripeness classification using deep learning |
| title_sort | multi modal expert system for automated durian ripeness classification using deep learning |
| topic | Expert system Deep learning Convolutional neural networks (CNN) Recurrent neural networks (RNN) Durian ripeness classification Hybrid AI |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000894 |
| work_keys_str_mv | AT santisukkasem multimodalexpertsystemforautomateddurianripenessclassificationusingdeeplearning AT watchareewanjitsakul multimodalexpertsystemforautomateddurianripenessclassificationusingdeeplearning AT phayungmeesad multimodalexpertsystemforautomateddurianripenessclassificationusingdeeplearning |