Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4
Aiming at the real-time detection and classification of the growth period of crops in the current digital cultivation and regulation technology of facility agriculture, an improved YOLOv4 method for identifying the growth period of strawberries in a greenhouse environment was proposed. The attention...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Smart Agriculture
2021-12-01
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| Series: | 智慧农业 |
| Subjects: | |
| Online Access: | http://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-4-99.shtml |
| _version_ | 1828283201453293568 |
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| author | LONG Jiehua GUO Wenzhong LIN Sen WEN Chaowu ZHANG Yu ZHAO Chunjiang |
| author_facet | LONG Jiehua GUO Wenzhong LIN Sen WEN Chaowu ZHANG Yu ZHAO Chunjiang |
| author_sort | LONG Jiehua |
| collection | DOAJ |
| description | Aiming at the real-time detection and classification of the growth period of crops in the current digital cultivation and regulation technology of facility agriculture, an improved YOLOv4 method for identifying the growth period of strawberries in a greenhouse environment was proposed. The attention mechanism into the Cross Stage Partial Residual (CSPRes) module of the YOLOv4 backbone network was introduced, and the target feature information of different growth periods of strawberries while reducing the interference of complex backgrounds was integrated, the detection accuracy while ensured real-time detection efficiency was improved. Took the smart facility strawberry in Yunnan province as the test object, the results showed that the detection accuracy (AP) of the YOLOv4-CBAM model during flowering, fruit expansion, green and mature period were 92.38%, 82.45%, 68.01% and 92.31%, respectively, the mean average precision (mAP) was 83.78%, the mean inetersection over union (mIoU) was 77.88%, and the detection time for a single image was 26.13 ms. Compared with the YOLOv4-SC model, mAP and mIoU were increased by 1.62% and 2.73%, respectively. Compared with the YOLOv4-SE model, mAP and mIOU increased by 4.81% and 3.46%, respectively. Compared with the YOLOv4 model, mAP and mIOU increased by 8.69% and 5.53%, respectively. As the attention mechanism was added to the improved YOLOv4 model, the amount of parameters increased, but the detection time of improved YOLOv4 models only slightly increased. At the same time, the number of fruit expansion period recognized by YOLOv4 was less than that of YOLOv4-CBAM, YOLOv4-SC and YOLOv4-SE, because the color characteristics of fruit expansion period were similar to those of leaf background, which made YOLOv4 recognition susceptible to leaf background interference, and added attention mechanism could reduce background information interference. YOLOv4-CBAM had higher confidence and number of identifications in identifying strawberry growth stages than YOLOv4-SC, YOLOv4-SE and YOLOv4 models, indicated that YOLOv4-CBAM model can extract more comprehensive and rich features and focus more on identifying targets, thereby improved detection accuracy. YOLOv4-CBAM model can meet the demand for real-time detection of strawberry growth period status. |
| first_indexed | 2024-04-13T08:41:31Z |
| format | Article |
| id | doaj.art-da6c68d307364d1383a9d204ba57bf94 |
| institution | Directory Open Access Journal |
| issn | 2096-8094 |
| language | English |
| last_indexed | 2024-04-13T08:41:31Z |
| publishDate | 2021-12-01 |
| publisher | Editorial Office of Smart Agriculture |
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| series | 智慧农业 |
| spelling | doaj.art-da6c68d307364d1383a9d204ba57bf942022-12-22T02:53:54ZengEditorial Office of Smart Agriculture智慧农业2096-80942021-12-01349911010.12133/j.smartag.2021.3.4.202109-SA006202109-SA006Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4LONG Jiehua0GUO Wenzhong1LIN Sen2WEN Chaowu3ZHANG Yu4ZHAO Chunjiang5Beijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, ChinaBeijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, ChinaBeijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, ChinaBeijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, ChinaBeijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, ChinaBeijing Academy of Agriculture and Forestry Sciences Intelligent Equipment Technology Research Center, Beijing 100097, ChinaAiming at the real-time detection and classification of the growth period of crops in the current digital cultivation and regulation technology of facility agriculture, an improved YOLOv4 method for identifying the growth period of strawberries in a greenhouse environment was proposed. The attention mechanism into the Cross Stage Partial Residual (CSPRes) module of the YOLOv4 backbone network was introduced, and the target feature information of different growth periods of strawberries while reducing the interference of complex backgrounds was integrated, the detection accuracy while ensured real-time detection efficiency was improved. Took the smart facility strawberry in Yunnan province as the test object, the results showed that the detection accuracy (AP) of the YOLOv4-CBAM model during flowering, fruit expansion, green and mature period were 92.38%, 82.45%, 68.01% and 92.31%, respectively, the mean average precision (mAP) was 83.78%, the mean inetersection over union (mIoU) was 77.88%, and the detection time for a single image was 26.13 ms. Compared with the YOLOv4-SC model, mAP and mIoU were increased by 1.62% and 2.73%, respectively. Compared with the YOLOv4-SE model, mAP and mIOU increased by 4.81% and 3.46%, respectively. Compared with the YOLOv4 model, mAP and mIOU increased by 8.69% and 5.53%, respectively. As the attention mechanism was added to the improved YOLOv4 model, the amount of parameters increased, but the detection time of improved YOLOv4 models only slightly increased. At the same time, the number of fruit expansion period recognized by YOLOv4 was less than that of YOLOv4-CBAM, YOLOv4-SC and YOLOv4-SE, because the color characteristics of fruit expansion period were similar to those of leaf background, which made YOLOv4 recognition susceptible to leaf background interference, and added attention mechanism could reduce background information interference. YOLOv4-CBAM had higher confidence and number of identifications in identifying strawberry growth stages than YOLOv4-SC, YOLOv4-SE and YOLOv4 models, indicated that YOLOv4-CBAM model can extract more comprehensive and rich features and focus more on identifying targets, thereby improved detection accuracy. YOLOv4-CBAM model can meet the demand for real-time detection of strawberry growth period status.http://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-4-99.shtmlobject detectionstrawberrygrowth period recognitionyolov4residual moduleattention mechanismloss function |
| spellingShingle | LONG Jiehua GUO Wenzhong LIN Sen WEN Chaowu ZHANG Yu ZHAO Chunjiang Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 智慧农业 object detection strawberry growth period recognition yolov4 residual module attention mechanism loss function |
| title | Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 |
| title_full | Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 |
| title_fullStr | Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 |
| title_full_unstemmed | Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 |
| title_short | Strawberry Growth Period Recognition Method Under Greenhouse Environment Based on Improved YOLOv4 |
| title_sort | strawberry growth period recognition method under greenhouse environment based on improved yolov4 |
| topic | object detection strawberry growth period recognition yolov4 residual module attention mechanism loss function |
| url | http://www.smartag.net.cn/article/2021/2096-8094/2096-8094-2021-3-4-99.shtml |
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