SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments
The accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. This study proposes SWMD-YOLO, a lightweight multi-scale detection network optimized for greenhouse sc...
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1593 |
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| author | Quan Wang Ye Hua Qiongdan Lou Xi Kan |
| author_facet | Quan Wang Ye Hua Qiongdan Lou Xi Kan |
| author_sort | Quan Wang |
| collection | DOAJ |
| description | The accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. This study proposes SWMD-YOLO, a lightweight multi-scale detection network optimized for greenhouse scenarios. The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. Traditional down-sampling is replaced with a dynamic sample (DySample) operator to minimize information loss during resolution transitions, while a multi-scale convolutional attention (MSCA) mechanism prioritizes discriminative regions under varying illumination. Additionally, we introduce Focaler-IoU, a novel loss function that addresses sample imbalance by dynamically re-weighting gradients for partially occluded and multi-scale targets. Experiments on greenhouse tomato data sets demonstrate that SWMD-YOLO achieves 93.47% mAP50 with a detection speed of 75.68 FPS, outperforming baseline models in accuracy while reducing parameters by 18.9%. Cross-data set validation confirms the model’s robustness to complex backgrounds and lighting variations. Overall, the proposed model provides a computationally efficient solution for real-time crop monitoring in resource-constrained precision agriculture systems. |
| format | Article |
| id | doaj-art-f0c8784a078d4e85b96e5437fa90af45 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-f0c8784a078d4e85b96e5437fa90af452025-08-20T03:55:48ZengMDPI AGAgronomy2073-43952025-06-01157159310.3390/agronomy15071593SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse EnvironmentsQuan Wang0Ye Hua1Qiongdan Lou2Xi Kan3School of Internet of Things Engineering, Wuxi University, Wuxi 214105, ChinaSchool of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Internet of Things Engineering, Wuxi University, Wuxi 214105, ChinaSchool of Internet of Things Engineering, Wuxi University, Wuxi 214105, ChinaThe accurate detection of occluded tomatoes in complex greenhouse environments remains challenging due to the limited feature representation ability and high computational costs of existing models. This study proposes SWMD-YOLO, a lightweight multi-scale detection network optimized for greenhouse scenarios. The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. Traditional down-sampling is replaced with a dynamic sample (DySample) operator to minimize information loss during resolution transitions, while a multi-scale convolutional attention (MSCA) mechanism prioritizes discriminative regions under varying illumination. Additionally, we introduce Focaler-IoU, a novel loss function that addresses sample imbalance by dynamically re-weighting gradients for partially occluded and multi-scale targets. Experiments on greenhouse tomato data sets demonstrate that SWMD-YOLO achieves 93.47% mAP50 with a detection speed of 75.68 FPS, outperforming baseline models in accuracy while reducing parameters by 18.9%. Cross-data set validation confirms the model’s robustness to complex backgrounds and lighting variations. Overall, the proposed model provides a computationally efficient solution for real-time crop monitoring in resource-constrained precision agriculture systems.https://www.mdpi.com/2073-4395/15/7/1593tomato detectionlightweight modelwavelet transformattention mechanismmulti-scale feature fusion |
| spellingShingle | Quan Wang Ye Hua Qiongdan Lou Xi Kan SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments Agronomy tomato detection lightweight model wavelet transform attention mechanism multi-scale feature fusion |
| title | SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments |
| title_full | SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments |
| title_fullStr | SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments |
| title_full_unstemmed | SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments |
| title_short | SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments |
| title_sort | swmd yolo a lightweight model for tomato detection in greenhouse environments |
| topic | tomato detection lightweight model wavelet transform attention mechanism multi-scale feature fusion |
| url | https://www.mdpi.com/2073-4395/15/7/1593 |
| work_keys_str_mv | AT quanwang swmdyoloalightweightmodelfortomatodetectioningreenhouseenvironments AT yehua swmdyoloalightweightmodelfortomatodetectioningreenhouseenvironments AT qiongdanlou swmdyoloalightweightmodelfortomatodetectioningreenhouseenvironments AT xikan swmdyoloalightweightmodelfortomatodetectioningreenhouseenvironments |