A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion
With the continuous advancement of modern agricultural technologies, the demand for precision fruit-picking techniques has been increasing. This study addresses the challenge of accurate recognition and harvesting of winter peaches by proposing a novel recognition model based on the residual network...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1545216/full |
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| _version_ | 1849760882786041856 |
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| author | Yan Li Chunping Li Tingting Zhu Shurong Zhang Li Liu Zhanpeng Guan |
| author_facet | Yan Li Chunping Li Tingting Zhu Shurong Zhang Li Liu Zhanpeng Guan |
| author_sort | Yan Li |
| collection | DOAJ |
| description | With the continuous advancement of modern agricultural technologies, the demand for precision fruit-picking techniques has been increasing. This study addresses the challenge of accurate recognition and harvesting of winter peaches by proposing a novel recognition model based on the residual network (ResNet) architecture—WinterPeachNet—aimed at enhancing the accuracy and efficiency of winter peach detection, even in resource-constrained environments. The WinterPeachNet model achieves a comprehensive improvement in network performance by integrating depthwise separable inverted bottleneck ResNet (DIBResNet), bidirectional feature pyramid network (BiFPN) structure, GhostConv module, and the YOLOv11 detection head (v11detect). The DIBResNet module, based on the ResNet architecture, introduces an inverted bottleneck structure and depthwise separable convolution technology, enhancing the depth and quality of feature extraction while effectively reducing the model’s computational complexity. The GhostConv module further improves detection accuracy by reducing the number of convolution kernels. Additionally, the BiFPN structure strengthens the model’s ability to detect objects of different sizes by fusing multi-scale feature information. The introduction of v11detect further optimizes object localization accuracy. The results show that the WinterPeachNet model achieves excellent performance in the winter peach detection task, with P = 0.996, R = 0.996, mAP50 = 0.995, and mAP50-95 = 0.964, demonstrating the model’s efficiency and accuracy in the winter peach detection task. The high efficiency of the WinterPeachNet model makes it highly adaptable in resource-constrained environments, enabling effective object detection at a relatively low computational cost. |
| format | Article |
| id | doaj-art-0f07b559ad9f43cca0e2bfba3ff4a82f |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-0f07b559ad9f43cca0e2bfba3ff4a82f2025-08-20T03:06:13ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-04-011610.3389/fpls.2025.15452161545216A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusionYan LiChunping LiTingting ZhuShurong ZhangLi LiuZhanpeng GuanWith the continuous advancement of modern agricultural technologies, the demand for precision fruit-picking techniques has been increasing. This study addresses the challenge of accurate recognition and harvesting of winter peaches by proposing a novel recognition model based on the residual network (ResNet) architecture—WinterPeachNet—aimed at enhancing the accuracy and efficiency of winter peach detection, even in resource-constrained environments. The WinterPeachNet model achieves a comprehensive improvement in network performance by integrating depthwise separable inverted bottleneck ResNet (DIBResNet), bidirectional feature pyramid network (BiFPN) structure, GhostConv module, and the YOLOv11 detection head (v11detect). The DIBResNet module, based on the ResNet architecture, introduces an inverted bottleneck structure and depthwise separable convolution technology, enhancing the depth and quality of feature extraction while effectively reducing the model’s computational complexity. The GhostConv module further improves detection accuracy by reducing the number of convolution kernels. Additionally, the BiFPN structure strengthens the model’s ability to detect objects of different sizes by fusing multi-scale feature information. The introduction of v11detect further optimizes object localization accuracy. The results show that the WinterPeachNet model achieves excellent performance in the winter peach detection task, with P = 0.996, R = 0.996, mAP50 = 0.995, and mAP50-95 = 0.964, demonstrating the model’s efficiency and accuracy in the winter peach detection task. The high efficiency of the WinterPeachNet model makes it highly adaptable in resource-constrained environments, enabling effective object detection at a relatively low computational cost.https://www.frontiersin.org/articles/10.3389/fpls.2025.1545216/fullResNetpeachobject detectiondeep learningBiFPN |
| spellingShingle | Yan Li Chunping Li Tingting Zhu Shurong Zhang Li Liu Zhanpeng Guan A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion Frontiers in Plant Science ResNet peach object detection deep learning BiFPN |
| title | A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion |
| title_full | A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion |
| title_fullStr | A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion |
| title_full_unstemmed | A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion |
| title_short | A recognition model for winter peach fruits based on improved ResNet and multi-scale feature fusion |
| title_sort | recognition model for winter peach fruits based on improved resnet and multi scale feature fusion |
| topic | ResNet peach object detection deep learning BiFPN |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1545216/full |
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