Design of a precision spraying control system with unmanned aerial vehicle based on image recognition

WANG Linhui; GAN Haiming; YUE Xuejun; LAN Yubin; WANG Jian; LIU Yongxin; LING Kangjie; CEN Zhenzhao

doi:10.7671/j.issn.1001-411X.2016.06.004

Journal of South China Agricultural University > 2016 > 37(6): 23-30. > DOI: 10.7671/j.issn.1001-411X.2016.06.004

WANG Linhui, GAN Haiming, YUE Xuejun, LAN Yubin, WANG Jian, LIU Yongxin, LING Kangjie, CEN Zhenzhao. Design of a precision spraying control system with unmanned aerial vehicle based on image recognition[J]. Journal of South China Agricultural University, 2016, 37(6): 23-30. DOI: 10.7671/j.issn.1001-411X.2016.06.004

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Design of a precision spraying control system with unmanned aerial vehicle based on image recognition

WANG Linhui^{1, 2,},
GAN Haiming^{1, 2},
YUE Xuejun^{1, 2, ,},
LAN Yubin^{2, 3},
WANG Jian^{1, 2},
LIU Yongxin^{1, 2},
LING Kangjie^{1, 2},
CEN Zhenzhao^{1, 2}

1.
College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China
2.
International Laboratory of Agricultural Aviation Pesticide Spraying Technology/International Laboratory of Agriculture Aviation Applied Technology/Engineering Research Center for Agricultural Aviation Application of Guangdong Province, Guangzhou 510642, China
3.
College of Engineering, South China Agricultural University, Guangzhou 510642, China

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Received Date: July 21, 2016
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
In order to improve the efficiency and utilization of conventional unmanned aerial vehicle (UAV) spraying in fertilizer and pesticide applications, an variable rate UAV spraying system was developed based on image recognition.
Method
Median filter was applied to the images for denoising. K_means clustering algorithm was then used to segment the UAV images to extract 22 texture features and the color of non-crop region. Support vector machine (SVM) classifier was designed for classification. According to the 17 selected characteristic parameters, the non-crop region was recognized through the SVM classifier with Radial basis function (RBF) as the kernel function. Finally, precision spraying was achieved with controllable nozzles based on the recognition results.
Result
The recognition accuracy reached up to 76.56%. In undisturbed wind farm, the reduction rate reached 32.7% with the threshold P of 10%.
Conclusion
This research can serve as reference guides for application of precise spraying control technology in agricultural aviation.
- support vector machine,
- unmanned aerial vehicle,
- image recognition,
- precision spray

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References (19)

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