Deep learning-based target spraying control of weeds in wheat fields at tillering stage
In this study, a target spraying decision and hysteresis algorithm is designed in conjunction with deep learning, which is deployed on a testbed for validation. The overall scheme of the target spraying control system is first proposed. Then YOLOv5s is lightweighted and improved. Based on this, a ta...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1540722/full |
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| _version_ | 1850063161966723072 |
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| author | Haiying Wang Yu Chen Shuo Zhang Peijie Guo Yuxiang Chen Guangrui Hu Yuxuan Ma |
| author_facet | Haiying Wang Yu Chen Shuo Zhang Peijie Guo Yuxiang Chen Guangrui Hu Yuxuan Ma |
| author_sort | Haiying Wang |
| collection | DOAJ |
| description | In this study, a target spraying decision and hysteresis algorithm is designed in conjunction with deep learning, which is deployed on a testbed for validation. The overall scheme of the target spraying control system is first proposed. Then YOLOv5s is lightweighted and improved. Based on this, a target spraying decision and hysteresis algorithm is designed, so that the target spraying system can precisely control the solenoid valve and differentiate spraying according to the distribution of weeds in different areas, and at the same time, successfully solve the operation hysteresis problem between the hardware. Finally, the algorithm was deployed on a testbed and simulated weeds and simulated tillering wheat were selected for bench experiments. Experiments on a dataset of realistic scenarios show that the improved model reduces the GFLOPs (computational complexity) and size by 52.2% and 42.4%, respectively, with mAP and F1 of 91.4% and 85.3%, which is an improvement of 0.2% and 0.8%, respectively, compared to the original model. The results of bench experiments showed that the spraying rate under the speed intervals of 0.3-0.4m/s, 0.4-0.5m/s and 0.5-0.6m/s reached 99.8%, 98.2% and 95.7%, respectively. Therefore, the algorithm can provide excellent spraying accuracy performance for the target spraying system, thus laying a theoretical foundation for the practical application of target spraying. |
| format | Article |
| id | doaj-art-c9d77bb967d741d9ae68e99a1c35b9e4 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-c9d77bb967d741d9ae68e99a1c35b9e42025-08-20T02:49:44ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-03-011610.3389/fpls.2025.15407221540722Deep learning-based target spraying control of weeds in wheat fields at tillering stageHaiying Wang0Yu Chen1Shuo Zhang2Peijie Guo3Yuxiang Chen4Guangrui Hu5Yuxuan Ma6College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaSchool of Design, Xi’an Technological University, Xian, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ChinaIn this study, a target spraying decision and hysteresis algorithm is designed in conjunction with deep learning, which is deployed on a testbed for validation. The overall scheme of the target spraying control system is first proposed. Then YOLOv5s is lightweighted and improved. Based on this, a target spraying decision and hysteresis algorithm is designed, so that the target spraying system can precisely control the solenoid valve and differentiate spraying according to the distribution of weeds in different areas, and at the same time, successfully solve the operation hysteresis problem between the hardware. Finally, the algorithm was deployed on a testbed and simulated weeds and simulated tillering wheat were selected for bench experiments. Experiments on a dataset of realistic scenarios show that the improved model reduces the GFLOPs (computational complexity) and size by 52.2% and 42.4%, respectively, with mAP and F1 of 91.4% and 85.3%, which is an improvement of 0.2% and 0.8%, respectively, compared to the original model. The results of bench experiments showed that the spraying rate under the speed intervals of 0.3-0.4m/s, 0.4-0.5m/s and 0.5-0.6m/s reached 99.8%, 98.2% and 95.7%, respectively. Therefore, the algorithm can provide excellent spraying accuracy performance for the target spraying system, thus laying a theoretical foundation for the practical application of target spraying.https://www.frontiersin.org/articles/10.3389/fpls.2025.1540722/fullweed identificationweed distribution determinationhysteresis propertytarget sprayingdeep learning |
| spellingShingle | Haiying Wang Yu Chen Shuo Zhang Peijie Guo Yuxiang Chen Guangrui Hu Yuxuan Ma Deep learning-based target spraying control of weeds in wheat fields at tillering stage Frontiers in Plant Science weed identification weed distribution determination hysteresis property target spraying deep learning |
| title | Deep learning-based target spraying control of weeds in wheat fields at tillering stage |
| title_full | Deep learning-based target spraying control of weeds in wheat fields at tillering stage |
| title_fullStr | Deep learning-based target spraying control of weeds in wheat fields at tillering stage |
| title_full_unstemmed | Deep learning-based target spraying control of weeds in wheat fields at tillering stage |
| title_short | Deep learning-based target spraying control of weeds in wheat fields at tillering stage |
| title_sort | deep learning based target spraying control of weeds in wheat fields at tillering stage |
| topic | weed identification weed distribution determination hysteresis property target spraying deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1540722/full |
| work_keys_str_mv | AT haiyingwang deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage AT yuchen deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage AT shuozhang deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage AT peijieguo deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage AT yuxiangchen deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage AT guangruihu deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage AT yuxuanma deeplearningbasedtargetsprayingcontrolofweedsinwheatfieldsattilleringstage |