XAI-FruitNet: An explainable deep model for accurate fruit classification
In agricultural technology, precise fruit classification is essential yet challenging due to inherent interclass similarities and intra-class variabilities among fruit species. Despite their impressive performance, traditional deep learning models suffer from a lack of interpretability, which hamper...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666154324005118 |
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| author | Shirin Sultana Md All Moon Tasir S.M. Nuruzzaman Nobel Md Mohsin Kabir M.F. Mridha |
| author_facet | Shirin Sultana Md All Moon Tasir S.M. Nuruzzaman Nobel Md Mohsin Kabir M.F. Mridha |
| author_sort | Shirin Sultana |
| collection | DOAJ |
| description | In agricultural technology, precise fruit classification is essential yet challenging due to inherent interclass similarities and intra-class variabilities among fruit species. Despite their impressive performance, traditional deep learning models suffer from a lack of interpretability, which hampers their transparency and trustworthiness in practical applications. To address these issues, we present XAI-FruitNet, a novel hybrid deep learning architecture designed to enhance feature discrimination by integrating average and max pooling techniques. XAI-FruitNet, an optimized architecture for efficiency evaluated across the Fruits-360, Fruit Recognition, Fruit and Vegetables Image Recognition, and Dry Fruit datasets, consistently achieves over 97 % accuracy, surpassing existing state-of-the-art models and underscoring its remarkable generalization capability. A significant advancement of XAI-FruitNet is its built-in interpretability, which enhances the model's transparency and fosters trust among endusers. Through rigorous experimentation, we demonstrate that XAI-FruitNet advances state-of-the-art fruit classification accuracy and sets a new standard for explainable artificial intelligence (XAI) in agricultural applications. This hybrid approach ensures that stakeholders can rely on the classification outcomes' high performance and comprehensible nature, thereby offering a robust and trustworthy solution for modern agricultural needs. |
| format | Article |
| id | doaj-art-69ea3d590909469ea88cfaece4daf4bf |
| institution | OA Journals |
| issn | 2666-1543 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-69ea3d590909469ea88cfaece4daf4bf2025-08-20T02:35:40ZengElsevierJournal of Agriculture and Food Research2666-15432024-12-011810147410.1016/j.jafr.2024.101474XAI-FruitNet: An explainable deep model for accurate fruit classificationShirin Sultana0Md All Moon Tasir1S.M. Nuruzzaman Nobel2Md Mohsin Kabir3M.F. Mridha4Bangladesh University of Business and Technology, Dhaka, BangladeshBangladesh University of Business and Technology, Dhaka, BangladeshBangladesh University of Business and Technology, Dhaka, BangladeshEötvös Loránd University, Budapest, 1117, Hungary; Corresponding author.American International University - Bangladesh, Dhaka, 1229, Bangladesh; Corresponding author.In agricultural technology, precise fruit classification is essential yet challenging due to inherent interclass similarities and intra-class variabilities among fruit species. Despite their impressive performance, traditional deep learning models suffer from a lack of interpretability, which hampers their transparency and trustworthiness in practical applications. To address these issues, we present XAI-FruitNet, a novel hybrid deep learning architecture designed to enhance feature discrimination by integrating average and max pooling techniques. XAI-FruitNet, an optimized architecture for efficiency evaluated across the Fruits-360, Fruit Recognition, Fruit and Vegetables Image Recognition, and Dry Fruit datasets, consistently achieves over 97 % accuracy, surpassing existing state-of-the-art models and underscoring its remarkable generalization capability. A significant advancement of XAI-FruitNet is its built-in interpretability, which enhances the model's transparency and fosters trust among endusers. Through rigorous experimentation, we demonstrate that XAI-FruitNet advances state-of-the-art fruit classification accuracy and sets a new standard for explainable artificial intelligence (XAI) in agricultural applications. This hybrid approach ensures that stakeholders can rely on the classification outcomes' high performance and comprehensible nature, thereby offering a robust and trustworthy solution for modern agricultural needs.http://www.sciencedirect.com/science/article/pii/S2666154324005118CNNFruit classification XAI modelInterpretabilityHybrid poolingDeep learning |
| spellingShingle | Shirin Sultana Md All Moon Tasir S.M. Nuruzzaman Nobel Md Mohsin Kabir M.F. Mridha XAI-FruitNet: An explainable deep model for accurate fruit classification Journal of Agriculture and Food Research CNN Fruit classification XAI model Interpretability Hybrid pooling Deep learning |
| title | XAI-FruitNet: An explainable deep model for accurate fruit classification |
| title_full | XAI-FruitNet: An explainable deep model for accurate fruit classification |
| title_fullStr | XAI-FruitNet: An explainable deep model for accurate fruit classification |
| title_full_unstemmed | XAI-FruitNet: An explainable deep model for accurate fruit classification |
| title_short | XAI-FruitNet: An explainable deep model for accurate fruit classification |
| title_sort | xai fruitnet an explainable deep model for accurate fruit classification |
| topic | CNN Fruit classification XAI model Interpretability Hybrid pooling Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666154324005118 |
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