Spider Mites Detection in Wheat Field Based on an Improved RetinaNet
As a daily staple food of more than one third of the world’s population, wheat is one of the main food crops in the world. The increase in wheat production will help meet the current global food security needs. In the process of wheat growth, diseases and insect pests have great influence on the yie...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/12/12/2160 |
| _version_ | 1797461936502210560 |
|---|---|
| author | Denghao Pang Hong Wang Peng Chen Dong Liang |
| author_facet | Denghao Pang Hong Wang Peng Chen Dong Liang |
| author_sort | Denghao Pang |
| collection | DOAJ |
| description | As a daily staple food of more than one third of the world’s population, wheat is one of the main food crops in the world. The increase in wheat production will help meet the current global food security needs. In the process of wheat growth, diseases and insect pests have great influence on the yield, which leads to a significant decline. Wheat spider mites are the most harmful to wheat because they are too small to be found. Therefore, how to use deep learning to identify small pests is a hot spot in modern intelligent agriculture research. In this paper, we propose an improved RetinaNet model and train it on our own dataset of wheat spider mites. Firstly, the wheat spider mites dataset is expanded from 1959 to 9215 by using two different angles and image segmentation methods. Secondly, the wheat spider mite feature detection head is added to improve the identification of small targets. Thirdly, the feature pyramid in FPN is further optimized, and the high-resolution feature maps are fully utilized to fuse the regression information of shallow feature maps and the semantic information of deep feature maps. Finally, the anchor generation strategy is optimized according to the amount of mites. Experimental results on the newly established wheat mite dataset validated our proposed model, yielding 81.7% mAP, which is superior to other advanced object detection methods in detecting wheat spider mites. |
| first_indexed | 2024-03-09T17:26:28Z |
| format | Article |
| id | doaj.art-ef67273dae704e929cf0cef352420fa4 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T17:26:28Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-ef67273dae704e929cf0cef352420fa42023-11-24T12:42:27ZengMDPI AGAgriculture2077-04722022-12-011212216010.3390/agriculture12122160Spider Mites Detection in Wheat Field Based on an Improved RetinaNetDenghao Pang0Hong Wang1Peng Chen2Dong Liang3School of Internet, Anhui University, Hefei 230601, ChinaSchool of Internet, Anhui University, Hefei 230601, ChinaSchool of Internet, Anhui University, Hefei 230601, ChinaSchool of Internet, Anhui University, Hefei 230601, ChinaAs a daily staple food of more than one third of the world’s population, wheat is one of the main food crops in the world. The increase in wheat production will help meet the current global food security needs. In the process of wheat growth, diseases and insect pests have great influence on the yield, which leads to a significant decline. Wheat spider mites are the most harmful to wheat because they are too small to be found. Therefore, how to use deep learning to identify small pests is a hot spot in modern intelligent agriculture research. In this paper, we propose an improved RetinaNet model and train it on our own dataset of wheat spider mites. Firstly, the wheat spider mites dataset is expanded from 1959 to 9215 by using two different angles and image segmentation methods. Secondly, the wheat spider mite feature detection head is added to improve the identification of small targets. Thirdly, the feature pyramid in FPN is further optimized, and the high-resolution feature maps are fully utilized to fuse the regression information of shallow feature maps and the semantic information of deep feature maps. Finally, the anchor generation strategy is optimized according to the amount of mites. Experimental results on the newly established wheat mite dataset validated our proposed model, yielding 81.7% mAP, which is superior to other advanced object detection methods in detecting wheat spider mites.https://www.mdpi.com/2077-0472/12/12/2160wheat spider mitesimproved RetinaNetobject detectionimage processing |
| spellingShingle | Denghao Pang Hong Wang Peng Chen Dong Liang Spider Mites Detection in Wheat Field Based on an Improved RetinaNet Agriculture wheat spider mites improved RetinaNet object detection image processing |
| title | Spider Mites Detection in Wheat Field Based on an Improved RetinaNet |
| title_full | Spider Mites Detection in Wheat Field Based on an Improved RetinaNet |
| title_fullStr | Spider Mites Detection in Wheat Field Based on an Improved RetinaNet |
| title_full_unstemmed | Spider Mites Detection in Wheat Field Based on an Improved RetinaNet |
| title_short | Spider Mites Detection in Wheat Field Based on an Improved RetinaNet |
| title_sort | spider mites detection in wheat field based on an improved retinanet |
| topic | wheat spider mites improved RetinaNet object detection image processing |
| url | https://www.mdpi.com/2077-0472/12/12/2160 |
| work_keys_str_mv | AT denghaopang spidermitesdetectioninwheatfieldbasedonanimprovedretinanet AT hongwang spidermitesdetectioninwheatfieldbasedonanimprovedretinanet AT pengchen spidermitesdetectioninwheatfieldbasedonanimprovedretinanet AT dongliang spidermitesdetectioninwheatfieldbasedonanimprovedretinanet |