A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS
The development of resistant cucumber varieties is of a great importance for reducing the production loss caused by root-knot nematodes. After cucumber plants are infected with root-knot nematodes, their roots will swell into spherical bumps. Rapid and accurate detection of the infected sites and as...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/10/2555 |
| _version_ | 1797476054597632000 |
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| author | Chunshan Wang Shedong Sun Chunjiang Zhao Zhenchuan Mao Huarui Wu Guifa Teng |
| author_facet | Chunshan Wang Shedong Sun Chunjiang Zhao Zhenchuan Mao Huarui Wu Guifa Teng |
| author_sort | Chunshan Wang |
| collection | DOAJ |
| description | The development of resistant cucumber varieties is of a great importance for reducing the production loss caused by root-knot nematodes. After cucumber plants are infected with root-knot nematodes, their roots will swell into spherical bumps. Rapid and accurate detection of the infected sites and assessment of the disease severity play a key role in selecting resistant cucumber varieties. Because the locations and sizes of the spherical bumps formed after different degrees of infection are random, the currently available detection and counting methods based on manual operation are extremely time-consuming and labor-intensive, and are prone to human error. In response to these problems, this paper proposes a cucumber root-knot nematode detection model based on the modified YOLOv5s model (i.e., YOLOv5-CMS) in order to support the breeding of resistant cucumber varieties. In the proposed model, the dual attention module (CBAM-CA) was adopted to enhance the model’s ability of extracting key features, the K-means++ clustering algorithm was applied to optimize the selection of the initial cluster center, which effectively improved the model’s performance, and a novel bounding box regression loss function (SIoU) was used to fuse the direction information between the ground-truth box and the predicted box so as to improve the detection precision. The experiment results show that the recall (R) and mAP of the YOLOv5s-CMS model were improved by 3% and 3.1%, respectively, compared to the original YOLOv5s model, which means it can achieve a better performance in cucumber root-knot nematode detection. This study provides an effective method for obtaining more intuitive and accurate data sources during the breeding of cucumber varieties resistant to root-knot nematode. |
| first_indexed | 2024-03-09T20:52:31Z |
| format | Article |
| id | doaj.art-579e0156d14b4aa79023cf8193fd504f |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:52:31Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-579e0156d14b4aa79023cf8193fd504f2023-11-23T22:29:13ZengMDPI AGAgronomy2073-43952022-10-011210255510.3390/agronomy12102555A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMSChunshan Wang0Shedong Sun1Chunjiang Zhao2Zhenchuan Mao3Huarui Wu4Guifa Teng5School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, ChinaSchool of Information Science and Technology, Hebei Agricultural University, Baoding 071001, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaInstitute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaNational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaSchool of Information Science and Technology, Hebei Agricultural University, Baoding 071001, ChinaThe development of resistant cucumber varieties is of a great importance for reducing the production loss caused by root-knot nematodes. After cucumber plants are infected with root-knot nematodes, their roots will swell into spherical bumps. Rapid and accurate detection of the infected sites and assessment of the disease severity play a key role in selecting resistant cucumber varieties. Because the locations and sizes of the spherical bumps formed after different degrees of infection are random, the currently available detection and counting methods based on manual operation are extremely time-consuming and labor-intensive, and are prone to human error. In response to these problems, this paper proposes a cucumber root-knot nematode detection model based on the modified YOLOv5s model (i.e., YOLOv5-CMS) in order to support the breeding of resistant cucumber varieties. In the proposed model, the dual attention module (CBAM-CA) was adopted to enhance the model’s ability of extracting key features, the K-means++ clustering algorithm was applied to optimize the selection of the initial cluster center, which effectively improved the model’s performance, and a novel bounding box regression loss function (SIoU) was used to fuse the direction information between the ground-truth box and the predicted box so as to improve the detection precision. The experiment results show that the recall (R) and mAP of the YOLOv5s-CMS model were improved by 3% and 3.1%, respectively, compared to the original YOLOv5s model, which means it can achieve a better performance in cucumber root-knot nematode detection. This study provides an effective method for obtaining more intuitive and accurate data sources during the breeding of cucumber varieties resistant to root-knot nematode.https://www.mdpi.com/2073-4395/12/10/2555root-knot nematodecucumbertarget detectionYOLOv5 |
| spellingShingle | Chunshan Wang Shedong Sun Chunjiang Zhao Zhenchuan Mao Huarui Wu Guifa Teng A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS Agronomy root-knot nematode cucumber target detection YOLOv5 |
| title | A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS |
| title_full | A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS |
| title_fullStr | A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS |
| title_full_unstemmed | A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS |
| title_short | A Detection Model for Cucumber Root-Knot Nematodes Based on Modified YOLOv5-CMS |
| title_sort | detection model for cucumber root knot nematodes based on modified yolov5 cms |
| topic | root-knot nematode cucumber target detection YOLOv5 |
| url | https://www.mdpi.com/2073-4395/12/10/2555 |
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