CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n
Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/128 |
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| author | Wenbin Sun Meihan Xu Kang Xu Dongquan Chen Jianhua Wang Ranbing Yang Quanquan Chen Songmei Yang |
| author_facet | Wenbin Sun Meihan Xu Kang Xu Dongquan Chen Jianhua Wang Ranbing Yang Quanquan Chen Songmei Yang |
| author_sort | Wenbin Sun |
| collection | DOAJ |
| description | Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(<i>P</i>), recall(<i>R</i>), mAP<sub>0.5</sub>, and mAP<sub>0.50:0.95</sub> achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection. |
| format | Article |
| id | doaj-art-b411403a4e5b4cce8a9175b8d7e686a6 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-b411403a4e5b4cce8a9175b8d7e686a62025-01-24T13:16:50ZengMDPI AGAgronomy2073-43952025-01-0115112810.3390/agronomy15010128CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8nWenbin Sun0Meihan Xu1Kang Xu2Dongquan Chen3Jianhua Wang4Ranbing Yang5Quanquan Chen6Songmei Yang7School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSanya Institute, China Agricultural University, Sanya 572025, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSanya Institute, China Agricultural University, Sanya 572025, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSanya Institute, China Agricultural University, Sanya 572025, ChinaKey Laboratory of Tropical Intelligent Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Haikou 570228, ChinaSeed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(<i>P</i>), recall(<i>R</i>), mAP<sub>0.5</sub>, and mAP<sub>0.50:0.95</sub> achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection.https://www.mdpi.com/2073-4395/15/1/128germination detectionobject detectioncorn seedYOLOdeep learning |
| spellingShingle | Wenbin Sun Meihan Xu Kang Xu Dongquan Chen Jianhua Wang Ranbing Yang Quanquan Chen Songmei Yang CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n Agronomy germination detection object detection corn seed YOLO deep learning |
| title | CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n |
| title_full | CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n |
| title_fullStr | CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n |
| title_full_unstemmed | CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n |
| title_short | CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n |
| title_sort | csgd yolo a corn seed germination status detection model based on yolov8n |
| topic | germination detection object detection corn seed YOLO deep learning |
| url | https://www.mdpi.com/2073-4395/15/1/128 |
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