Design and trial of precision spraying system for weeds in winter wheat field at tillering stage
During the tillering stage of wheat, the distribution of weeds in the field is irregular, often showing single plants or clusters. Current precision spraying systems are mainly suitable for locating and spraying single-plant vegetation, which usually leads to the system missing or under-spraying whe...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003910 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321254602932224 |
|---|---|
| author | Bo Li Peijie Guo Yu Chen Jun Chen Haiying Wang Jing Zhang Zhixing Zhang |
| author_facet | Bo Li Peijie Guo Yu Chen Jun Chen Haiying Wang Jing Zhang Zhixing Zhang |
| author_sort | Bo Li |
| collection | DOAJ |
| description | During the tillering stage of wheat, the distribution of weeds in the field is irregular, often showing single plants or clusters. Current precision spraying systems are mainly suitable for locating and spraying single-plant vegetation, which usually leads to the system missing or under-spraying when dealing with clustered weeds. In this study, a precision spraying control method is proposed to reduce the effect of camera frame rate on weed localization failure through three sets of position determination regions, and to address the effect of solenoid valve response frequency on precision spraying by controlling the spray nozzle to continuously spray herbicides on clustered weeds through a velocity-adaptive dynamic overlap region. To improve the accuracy of weed detection, GCGS-YOLO is proposed as a weed target detection model, and we integrate the Global Context (GC) attention mechanism with the traditional C3 module to optimize the backbone feature extraction network, and introduce the GSConv module to improve the neck network. The improved models P, R, mAP and F1 were 88 %, 84.6 %, 92.2 % and 86.3 %, which were 3 %, 3.1 %, 2.7 % and 3.1 % higher compared to the original model. The precision spraying algorithms and systems were integrated in a test bed and sprayer to carry out the tests. The tests showed that the recognition rate and spraying rate on the test bed could reach >98 % at different speeds. The results of the field test showed that the recognition rate and spray application rate of the sprayer were 91.2 % and 96.1 %, respectively, at a speed of 0.2 m/s. The research results can reduce the waste of herbicide, improve the efficiency of weeding, and provide reference for large-scale precision weeding. |
| format | Article |
| id | doaj-art-69eb58e8462b449084ba7e783f1dcf86 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-69eb58e8462b449084ba7e783f1dcf862025-08-20T03:49:49ZengElsevierSmart Agricultural Technology2772-37552025-12-011210115910.1016/j.atech.2025.101159Design and trial of precision spraying system for weeds in winter wheat field at tillering stageBo Li0Peijie Guo1Yu Chen2Jun Chen3Haiying Wang4Jing Zhang5Zhixing Zhang6College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, ChinaCollege of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Corresponding author.College of Innovation and Entrepreneurship (Engineering Training Center), Taiyuan University of Technology, Taiyuan 030024, ChinaShanxi Tangdi Technology Co. Ltd, Taiyuan 030032, ChinaDuring the tillering stage of wheat, the distribution of weeds in the field is irregular, often showing single plants or clusters. Current precision spraying systems are mainly suitable for locating and spraying single-plant vegetation, which usually leads to the system missing or under-spraying when dealing with clustered weeds. In this study, a precision spraying control method is proposed to reduce the effect of camera frame rate on weed localization failure through three sets of position determination regions, and to address the effect of solenoid valve response frequency on precision spraying by controlling the spray nozzle to continuously spray herbicides on clustered weeds through a velocity-adaptive dynamic overlap region. To improve the accuracy of weed detection, GCGS-YOLO is proposed as a weed target detection model, and we integrate the Global Context (GC) attention mechanism with the traditional C3 module to optimize the backbone feature extraction network, and introduce the GSConv module to improve the neck network. The improved models P, R, mAP and F1 were 88 %, 84.6 %, 92.2 % and 86.3 %, which were 3 %, 3.1 %, 2.7 % and 3.1 % higher compared to the original model. The precision spraying algorithms and systems were integrated in a test bed and sprayer to carry out the tests. The tests showed that the recognition rate and spraying rate on the test bed could reach >98 % at different speeds. The results of the field test showed that the recognition rate and spray application rate of the sprayer were 91.2 % and 96.1 %, respectively, at a speed of 0.2 m/s. The research results can reduce the waste of herbicide, improve the efficiency of weeding, and provide reference for large-scale precision weeding.http://www.sciencedirect.com/science/article/pii/S2772375525003910Precision sprayingDeep learningTarget identificationWeeds in wheat fieldsField evaluation |
| spellingShingle | Bo Li Peijie Guo Yu Chen Jun Chen Haiying Wang Jing Zhang Zhixing Zhang Design and trial of precision spraying system for weeds in winter wheat field at tillering stage Smart Agricultural Technology Precision spraying Deep learning Target identification Weeds in wheat fields Field evaluation |
| title | Design and trial of precision spraying system for weeds in winter wheat field at tillering stage |
| title_full | Design and trial of precision spraying system for weeds in winter wheat field at tillering stage |
| title_fullStr | Design and trial of precision spraying system for weeds in winter wheat field at tillering stage |
| title_full_unstemmed | Design and trial of precision spraying system for weeds in winter wheat field at tillering stage |
| title_short | Design and trial of precision spraying system for weeds in winter wheat field at tillering stage |
| title_sort | design and trial of precision spraying system for weeds in winter wheat field at tillering stage |
| topic | Precision spraying Deep learning Target identification Weeds in wheat fields Field evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003910 |
| work_keys_str_mv | AT boli designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage AT peijieguo designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage AT yuchen designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage AT junchen designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage AT haiyingwang designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage AT jingzhang designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage AT zhixingzhang designandtrialofprecisionsprayingsystemforweedsinwinterwheatfieldattilleringstage |