P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s
Passion fruit pests are characterized by their high species diversity, small physical size, and dense populations. Traditional algorithms often face challenges in achieving high detection accuracy and efficiency when addressing the complex task of detecting densely distributed small objects. To addr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-06-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1612642/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850115968721747968 |
|---|---|
| author | Zhiping Tan Zhiping Tan Dapeng Ye Jiancong Wang Wenxiang Wang |
| author_facet | Zhiping Tan Zhiping Tan Dapeng Ye Jiancong Wang Wenxiang Wang |
| author_sort | Zhiping Tan |
| collection | DOAJ |
| description | Passion fruit pests are characterized by their high species diversity, small physical size, and dense populations. Traditional algorithms often face challenges in achieving high detection accuracy and efficiency when addressing the complex task of detecting densely distributed small objects. To address this issue, this paper proposed an enhanced lightweight and efficient deep learning model, which is developed based on YOLOv5s, consists of the PLDIoU, four CBAM modules, and one newAnchors, termed P4CN-YOLOv5s, for detecting passion fruit pests. In P4CN-YOLOv5s, the Mosaic-9 and Mixup algorithms are initially used for data augmentation to augment the training dataset and enhance data complexity. Secondly, after analyzing the image set characteristics to be detected in this research, the point-line distance bounding box loss function is utilized to calculate the coordinate distance of the prediction box and target box, and aimed at improving detection speed. Subsequently, a convolutional block attention module (CBAM) and optimized anchor boxes are employed to reduce the false detection rate of the model. Finally, a dataset consisting of 6,000 images of passion fruit pests is used to evaluate the performance of the proposed model. The experimental data analysis reveals that the proposed P4CN-YOLOv5s model achieves superior performance, with an accuracy of 96.99%, an F1-score of 93.99%, and a mean detection time of 7.2 milliseconds. When compared to other widely used target detection models, including SSD, Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, P4C-YOLOv5s, and YOLOv7 on the same dataset, the P4CN-YOLOv5s model demonstrates distinct advantages, such as a low false positive rate and high detection efficiency. Therefore, the proposed model proves to be more effective for detecting passion fruit pests in natural orchard environments. |
| format | Article |
| id | doaj-art-17aca2b77db3455397b5e0c9e646bdec |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-17aca2b77db3455397b5e0c9e646bdec2025-08-20T02:36:27ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-06-011610.3389/fpls.2025.16126421612642P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5sZhiping Tan0Zhiping Tan1Dapeng Ye2Jiancong Wang3Wenxiang Wang4College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, ChinaCollege of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, ChinaCollege of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou, ChinaCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, ChinaCollege of Information Engineering, Gannan University of Science and Technology, Ganzhou, ChinaPassion fruit pests are characterized by their high species diversity, small physical size, and dense populations. Traditional algorithms often face challenges in achieving high detection accuracy and efficiency when addressing the complex task of detecting densely distributed small objects. To address this issue, this paper proposed an enhanced lightweight and efficient deep learning model, which is developed based on YOLOv5s, consists of the PLDIoU, four CBAM modules, and one newAnchors, termed P4CN-YOLOv5s, for detecting passion fruit pests. In P4CN-YOLOv5s, the Mosaic-9 and Mixup algorithms are initially used for data augmentation to augment the training dataset and enhance data complexity. Secondly, after analyzing the image set characteristics to be detected in this research, the point-line distance bounding box loss function is utilized to calculate the coordinate distance of the prediction box and target box, and aimed at improving detection speed. Subsequently, a convolutional block attention module (CBAM) and optimized anchor boxes are employed to reduce the false detection rate of the model. Finally, a dataset consisting of 6,000 images of passion fruit pests is used to evaluate the performance of the proposed model. The experimental data analysis reveals that the proposed P4CN-YOLOv5s model achieves superior performance, with an accuracy of 96.99%, an F1-score of 93.99%, and a mean detection time of 7.2 milliseconds. When compared to other widely used target detection models, including SSD, Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, P4C-YOLOv5s, and YOLOv7 on the same dataset, the P4CN-YOLOv5s model demonstrates distinct advantages, such as a low false positive rate and high detection efficiency. Therefore, the proposed model proves to be more effective for detecting passion fruit pests in natural orchard environments.https://www.frontiersin.org/articles/10.3389/fpls.2025.1612642/fullpassion fruit pests detectionlightweight deep learning algorithmYOLOv5Sattention modulepests detection |
| spellingShingle | Zhiping Tan Zhiping Tan Dapeng Ye Jiancong Wang Wenxiang Wang P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s Frontiers in Plant Science passion fruit pests detection lightweight deep learning algorithm YOLOv5S attention module pests detection |
| title | P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s |
| title_full | P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s |
| title_fullStr | P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s |
| title_full_unstemmed | P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s |
| title_short | P4CN-YOLOv5s: a passion fruit pests detection method based on lightweight-improved YOLOv5s |
| title_sort | p4cn yolov5s a passion fruit pests detection method based on lightweight improved yolov5s |
| topic | passion fruit pests detection lightweight deep learning algorithm YOLOv5S attention module pests detection |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1612642/full |
| work_keys_str_mv | AT zhipingtan p4cnyolov5sapassionfruitpestsdetectionmethodbasedonlightweightimprovedyolov5s AT zhipingtan p4cnyolov5sapassionfruitpestsdetectionmethodbasedonlightweightimprovedyolov5s AT dapengye p4cnyolov5sapassionfruitpestsdetectionmethodbasedonlightweightimprovedyolov5s AT jiancongwang p4cnyolov5sapassionfruitpestsdetectionmethodbasedonlightweightimprovedyolov5s AT wenxiangwang p4cnyolov5sapassionfruitpestsdetectionmethodbasedonlightweightimprovedyolov5s |