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Path recognition of UAV based on improved super-greening algorithm and design of adaptive Trichogramma pill dispenser

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Abstract

Aiming at the problem that the traditional Trichogramma pill dispenser is inaccurate due to the change of wind speed during operation, and the traditional algorithm cannot meet the accuracy and applicability of the Unmanned Aerial Vehicle (UAV) path identification and the delivery process, this paper proposes path recognition of UAV based on an improved super-greening algorithm and design of adaptive Trichogramma pill dispenser. The improved super-greening algorithm and the maximum inter-class variance method are used to automatically obtain the green feature binary image, and the morphological processing is used to improve the image quality; the crop location point extraction algorithm based on edge detection is used to extract the feature points, and the feature points are used to fit the navigation line. The wind pressure sensor is used to obtain the voltage signal, and the measured voltage is output after being processed by the airborne 5G board, and the motor speed is controlled according to the adaptive PID control algorithm. The simulation results show that the improved super-greening algorithm more truly reflects the green content in the image, which makes the image extraction more accurate; the crop positioning point extraction algorithm of edge detection is 3.4 and 3.7 smaller than the vertical projection algorithm in average error and positioning error respectively; compared with the vertical projection method, the edge detection method reduces the time consumption by 150 ms and increases the accuracy by 8%; adaptive PID control algorithm compared with the traditional PID control algorithm, the rise time, adjustment time and overshoot are 1.5%, 4.8% and 11.5% smaller respectively. The research results can provide research ideas for the application of UAV precise delivery of Trichogramma bee pills technology in agriculture.

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References

  1. Bhatti UA, Yu Z, Yuan L et al (2020) Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing[J]. IEEE Access PP(99):1–1

    Google Scholar 

  2. Bhatti UA, Zhou M, Huo Q et al (2021) Advanced color edge detection using clifford algebra in satellite images[J]. IEEE Photon J PP(99):1–1

    Article  Google Scholar 

  3. Bhatti UA, Zhao Y et al (2022) Local similarity-based spatial–spectral fusion hyperspectral image classification with deep CNN and Gabor filtering [J]. IEEE Trans Geosci Remote Sens PP(99):1–1

    Article  Google Scholar 

  4. Bzowska-Bakalarz BP, Bere PK et al (2020) Using gyroplane for application of Trichogramma spp. against the European corn borer in maize[J]. Pest Manag Sci 76(6):2243–2250

    Article  Google Scholar 

  5. Diwakar M, Kumar M (2018) A review on CT image noise and its denoising[J]. Biomed Signal Process Control 42(APR.):73–88

    Article  Google Scholar 

  6. Diwakar M, Kumar M et al (2018) CT image denoising using NLM and correlation-based wavelet packet thresholding[J]. IET Image Process 12(5):708–715

    Article  Google Scholar 

  7. Gao Z, Lei Z, Feng W et al (2011) Application of improved median filter algorithm in image denoising [J]. Appl Opt 32(4):5

    Google Scholar 

  8. Hua R, Jian L, Shu M et al (2017) DC motor speed regulation system based on fuzzy adaptive PID control [J]. Commun Power Technol 34(1):3

    Google Scholar 

  9. Jie H, Qing M, Man Z et al (2014) Extraction of agricultural machinery navigation baseline based on edge detection and scanning filtering [J]. J Agric Mach S1:265–270

    Google Scholar 

  10. Jiu F, Feng Z (2007) Two-dimensional Otsu curve threshold segmentation method for grayscale images [J]. Chin J Electron 35(4):5

    Google Scholar 

  11. Kairo G, Biron DG, Abdelkader FB et al (2017) Nosema ceranae, Fipronil and their combination compromise honey bee reproduction via changes in male physiology[J]. Sci Rep 7(1):1–4

    Article  Google Scholar 

  12. Kashyap Y, Khare A, Lipton M et al (2012) An improved SOBEL algorithm for palm image edge detection using OTSU method[J]. Biom Bioinformatics 4(7)

  13. Lian W, Dun L, Bao L et al (2019) Comparison of control effects of different species of Trichogramma on Asian corn borer [J]. Chin J Appl Entomol 2:1–6

    Google Scholar 

  14. Ling Y, Tian W, Xu H et al (2017) Extraction of crop rows based on random sampling consensus algorithm (RANSAC) [J]. Jiangsu Agri Sci 45(2):1–3

    Google Scholar 

  15. Montalvo M, Pajares G, Guerrero JM, Romeo J, Guijarro M, Ribeiro A, Ruz JJ, Cruz JM (2012) Automatic detection of crop rows in maize fields with high weeds pressure[J]. Expert Syst Appl 39(15):11889–11897

    Article  Google Scholar 

  16. Qingge L, Zheng R, Zhao X et al (2020) An improved Otsu threshold segmentation algorithm[J]. Int J Comput Sci Eng 22(1):146

    Google Scholar 

  17. State Forestry and Grassland Administration (2018) Technical guidelines for UAV release of Trichogramma: LY/T3028—2018 [S]. Shanxi Provincial Bureau of Forestry Pest Control and Quarantine, Shanxi, pp 1–16

    Google Scholar 

  18. Subramaniam R, Hajjaj S, Gsangaya KR et al (2021) Redesigning dispenser component to enhance performance crop-dusting agriculture drones[J]. Mater Today: Proceed 1

  19. Watros A, Lipińska H, Lipiński W et al (2018) The relationship between mineral nitrogen content and soil ph in grassland and fodder crop soils[J]. Appl Ecol Environ Res 17(1):107–121

    Article  Google Scholar 

  20. Yong S, Guo J, Gang L et al (2010) Early crop row centerline detection method based on least squares method [J]. J Agric Mach 041(007):163–167 185

  21. Zhan Y, Chen S, Wang G, Fu J, Lan Y (2021) Biological control technology and application based on agricultural unmanned aerial vehicle (UAV) intelligent delivery of insect natural enemies (Trichogramma) carrier[J]. Pest Manag Sci 77:3259–3272

    Article  Google Scholar 

  22. Zhen C, Xue Y, Lin W et al (2019) Design and experiment of an adaptive variable spray system for unmanned aerial vehicles based on neural network PID [J]. J South China Agric Univ 040(004):100–108

    Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering, Shandong University of Technology, Zhangdian District, Zibo, 255049, China

    Shengzheng Ji & Chuanxin Yao

  2. School of Agricultural Engineering and Food Science, Shandong University of Technology, Zhangdian District, Zibo, 255049, China

    Zihan Feng

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  1. Shengzheng Ji
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  2. Chuanxin Yao
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  3. Zihan Feng
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Correspondence to Shengzheng Ji.

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Ji, S., Yao, C. & Feng, Z. Path recognition of UAV based on improved super-greening algorithm and design of adaptive Trichogramma pill dispenser. Multimed Tools Appl 82, 17301–17320 (2023). https://doi.org/10.1007/s11042-022-14209-9

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  • DOI: https://doi.org/10.1007/s11042-022-14209-9

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