DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s
The quality inspection of potato seed tubers is pivotal for their effective segregation and a critical step in the cultivation process of potatoes. Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detectio...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/14/11/2558 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846154746254065664 |
|---|---|
| author | Zhaomei Qiu Weili Wang Xin Jin Fei Wang Zhitao He Jiangtao Ji Shanshan Jin |
| author_facet | Zhaomei Qiu Weili Wang Xin Jin Fei Wang Zhitao He Jiangtao Ji Shanshan Jin |
| author_sort | Zhaomei Qiu |
| collection | DOAJ |
| description | The quality inspection of potato seed tubers is pivotal for their effective segregation and a critical step in the cultivation process of potatoes. Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. This study initiates with data augmentation of the seed tuber images obtained via the image acquisition system, employing strategies such as translation, noise injection, luminance modulation, cropping, mirroring, and the Cutout technique to amplify the dataset and fortify the model’s resilience. Subsequently, the original YOLOv5s model undergoes a series of enhancements, including the substitution of the conventional convolutional modules in the backbone network with the depth-wise separable convolution DP_Conv module to curtail the model’s parameter count and computational load; the replacement of the original C3 module’s Bottleneck with the GhostBottleneck to render the model more compact; and the integration of the SimAM attention mechanism module to augment the model’s proficiency in capturing features of potato tuber buds and defects, culminating in the DCS-YOLOv5s lightweight model. The research findings indicate that the DCS-YOLOv5s model outperforms the YOLOv5s model in detection precision and velocity, exhibiting superior detection efficacy and model compactness. The model’s detection metrics, including Precision, Recall, and mean Average Precision at Intersection over Union thresholds of 0.5 (mAP1) and 0.75 (mAP2), have improved to 95.8%, 93.2%, 97.1%, and 66.2%, respectively, signifying increments of 4.2%, 5.7%, 5.4%, and 9.8%. The detection velocity has also been augmented by 12.07%, achieving a rate of 65 FPS. The DCS-YOLOv5s target detection model, by attaining model compactness, has substantially heightened the detection precision, presenting a beneficial reference for dynamic sample target detection in the context of potato-cutting machinery. |
| format | Article |
| id | doaj-art-1ebfc3c4d8b64e058eaed4a8ec681d4c |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-1ebfc3c4d8b64e058eaed4a8ec681d4c2024-11-26T17:44:26ZengMDPI AGAgronomy2073-43952024-10-011411255810.3390/agronomy14112558DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5sZhaomei Qiu0Weili Wang1Xin Jin2Fei Wang3Zhitao He4Jiangtao Ji5Shanshan Jin6College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaCollege of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471000, ChinaThe quality inspection of potato seed tubers is pivotal for their effective segregation and a critical step in the cultivation process of potatoes. Given the dearth of research on intelligent tuber-cutting machinery in China, particularly concerning the identification of bud eyes and defect detection, this study has developed a multi-target recognition approach for potato seed tubers utilizing deep learning techniques. By refining the YOLOv5s algorithm, a novel, lightweight model termed DCS-YOLOv5s has been introduced for the simultaneous identification of tuber buds and defects. This study initiates with data augmentation of the seed tuber images obtained via the image acquisition system, employing strategies such as translation, noise injection, luminance modulation, cropping, mirroring, and the Cutout technique to amplify the dataset and fortify the model’s resilience. Subsequently, the original YOLOv5s model undergoes a series of enhancements, including the substitution of the conventional convolutional modules in the backbone network with the depth-wise separable convolution DP_Conv module to curtail the model’s parameter count and computational load; the replacement of the original C3 module’s Bottleneck with the GhostBottleneck to render the model more compact; and the integration of the SimAM attention mechanism module to augment the model’s proficiency in capturing features of potato tuber buds and defects, culminating in the DCS-YOLOv5s lightweight model. The research findings indicate that the DCS-YOLOv5s model outperforms the YOLOv5s model in detection precision and velocity, exhibiting superior detection efficacy and model compactness. The model’s detection metrics, including Precision, Recall, and mean Average Precision at Intersection over Union thresholds of 0.5 (mAP1) and 0.75 (mAP2), have improved to 95.8%, 93.2%, 97.1%, and 66.2%, respectively, signifying increments of 4.2%, 5.7%, 5.4%, and 9.8%. The detection velocity has also been augmented by 12.07%, achieving a rate of 65 FPS. The DCS-YOLOv5s target detection model, by attaining model compactness, has substantially heightened the detection precision, presenting a beneficial reference for dynamic sample target detection in the context of potato-cutting machinery.https://www.mdpi.com/2073-4395/14/11/2558DCS-YOLOv5sdeep learninglightweightingseed potatoquality inspection |
| spellingShingle | Zhaomei Qiu Weili Wang Xin Jin Fei Wang Zhitao He Jiangtao Ji Shanshan Jin DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s Agronomy DCS-YOLOv5s deep learning lightweighting seed potato quality inspection |
| title | DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s |
| title_full | DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s |
| title_fullStr | DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s |
| title_full_unstemmed | DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s |
| title_short | DCS-YOLOv5s: A Lightweight Algorithm for Multi-Target Recognition of Potato Seed Potatoes Based on YOLOv5s |
| title_sort | dcs yolov5s a lightweight algorithm for multi target recognition of potato seed potatoes based on yolov5s |
| topic | DCS-YOLOv5s deep learning lightweighting seed potato quality inspection |
| url | https://www.mdpi.com/2073-4395/14/11/2558 |
| work_keys_str_mv | AT zhaomeiqiu dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s AT weiliwang dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s AT xinjin dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s AT feiwang dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s AT zhitaohe dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s AT jiangtaoji dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s AT shanshanjin dcsyolov5salightweightalgorithmformultitargetrecognitionofpotatoseedpotatoesbasedonyolov5s |