A Counting Method of Red Jujube Based on Improved YOLOv5s
Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which r...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/12/2071 |
| _version_ | 1797461876745961472 |
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| author | Yichen Qiao Yaohua Hu Zhouzhou Zheng Huanbo Yang Kaili Zhang Juncai Hou Jiapan Guo |
| author_facet | Yichen Qiao Yaohua Hu Zhouzhou Zheng Huanbo Yang Kaili Zhang Juncai Hou Jiapan Guo |
| author_sort | Yichen Qiao |
| collection | DOAJ |
| description | Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision. |
| first_indexed | 2024-03-09T17:26:35Z |
| format | Article |
| id | doaj.art-097cd74235b2453e9bdfb6f23f76870f |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T17:26:35Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-097cd74235b2453e9bdfb6f23f76870f2023-11-24T12:40:52ZengMDPI AGAgriculture2077-04722022-12-011212207110.3390/agriculture12122071A Counting Method of Red Jujube Based on Improved YOLOv5sYichen Qiao0Yaohua Hu1Zhouzhou Zheng2Huanbo Yang3Kaili Zhang4Juncai Hou5Jiapan Guo6College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Optical, Mechanical, and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, ChinaBernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, 9747 AG Groningen, The NetherlandsDue to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision.https://www.mdpi.com/2077-0472/12/12/2071count red jujubesred jujubeimproved YOLOv5sShuffleNet V2 UnitStemBiFPN |
| spellingShingle | Yichen Qiao Yaohua Hu Zhouzhou Zheng Huanbo Yang Kaili Zhang Juncai Hou Jiapan Guo A Counting Method of Red Jujube Based on Improved YOLOv5s Agriculture count red jujubes red jujube improved YOLOv5s ShuffleNet V2 Unit Stem BiFPN |
| title | A Counting Method of Red Jujube Based on Improved YOLOv5s |
| title_full | A Counting Method of Red Jujube Based on Improved YOLOv5s |
| title_fullStr | A Counting Method of Red Jujube Based on Improved YOLOv5s |
| title_full_unstemmed | A Counting Method of Red Jujube Based on Improved YOLOv5s |
| title_short | A Counting Method of Red Jujube Based on Improved YOLOv5s |
| title_sort | counting method of red jujube based on improved yolov5s |
| topic | count red jujubes red jujube improved YOLOv5s ShuffleNet V2 Unit Stem BiFPN |
| url | https://www.mdpi.com/2077-0472/12/12/2071 |
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