Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment
Growing consumer demand for high-quality strawberries has highlighted the need for accurate, efficient, and non-destructive methods to assess key postharvest quality traits, such as weight, size uniformity, and quantity. This study proposes a multi-objective learning algorithm that leverages RGB-D m...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1564301/full |
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| author | Zhenzhen Cheng Yifan Cheng Bailing Miao Tingting Fang Shoufu Gong |
| author_facet | Zhenzhen Cheng Yifan Cheng Bailing Miao Tingting Fang Shoufu Gong |
| author_sort | Zhenzhen Cheng |
| collection | DOAJ |
| description | Growing consumer demand for high-quality strawberries has highlighted the need for accurate, efficient, and non-destructive methods to assess key postharvest quality traits, such as weight, size uniformity, and quantity. This study proposes a multi-objective learning algorithm that leverages RGB-D multimodal information to estimate these quality metrics. The algorithm develops a fusion expert network architecture that maximizes the use of multimodal features while preserving the distinct details of each modality. Additionally, a novel Heritable Loss function is implemented to reduce redundancy and enhance model performance. Experimental results show that the coefficient of determination (R²) values for weight, size uniformity and number are 0.94, 0.90 and 0.95 respectively. Ablation studies demonstrate the advantage of the architecture in multimodal, multi-task prediction accuracy. Compared to single-modality models, non-fusion branch networks, and attention-enhanced fusion models, our approach achieves enhanced performance across multi-task learning scenarios, providing more precise data for trait assessment and precision strawberry applications. |
| format | Article |
| id | doaj-art-eb77999a2fcc48868f57071a1e2249c4 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-eb77999a2fcc48868f57071a1e2249c42025-08-20T02:47:52ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-03-011610.3389/fpls.2025.15643011564301Multi-objective RGB-D fusion network for non-destructive strawberry trait assessmentZhenzhen Cheng0Yifan Cheng1Bailing Miao2Tingting Fang3Shoufu Gong4Department of Horticulture, Xinyang Agriculture and Forestry University, Xinyang, ChinaDepartment of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Horticulture, Xinyang Agriculture and Forestry University, Xinyang, ChinaDepartment of Horticulture, Xinyang Agriculture and Forestry University, Xinyang, ChinaDepartment of Horticulture, Xinyang Agriculture and Forestry University, Xinyang, ChinaGrowing consumer demand for high-quality strawberries has highlighted the need for accurate, efficient, and non-destructive methods to assess key postharvest quality traits, such as weight, size uniformity, and quantity. This study proposes a multi-objective learning algorithm that leverages RGB-D multimodal information to estimate these quality metrics. The algorithm develops a fusion expert network architecture that maximizes the use of multimodal features while preserving the distinct details of each modality. Additionally, a novel Heritable Loss function is implemented to reduce redundancy and enhance model performance. Experimental results show that the coefficient of determination (R²) values for weight, size uniformity and number are 0.94, 0.90 and 0.95 respectively. Ablation studies demonstrate the advantage of the architecture in multimodal, multi-task prediction accuracy. Compared to single-modality models, non-fusion branch networks, and attention-enhanced fusion models, our approach achieves enhanced performance across multi-task learning scenarios, providing more precise data for trait assessment and precision strawberry applications.https://www.frontiersin.org/articles/10.3389/fpls.2025.1564301/fullstrawberry qualityfruit traits estimationcomputer visiondeep learningRGB-D modality fusion |
| spellingShingle | Zhenzhen Cheng Yifan Cheng Bailing Miao Tingting Fang Shoufu Gong Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment Frontiers in Plant Science strawberry quality fruit traits estimation computer vision deep learning RGB-D modality fusion |
| title | Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment |
| title_full | Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment |
| title_fullStr | Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment |
| title_full_unstemmed | Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment |
| title_short | Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment |
| title_sort | multi objective rgb d fusion network for non destructive strawberry trait assessment |
| topic | strawberry quality fruit traits estimation computer vision deep learning RGB-D modality fusion |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1564301/full |
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