Method for UAV spraying pattern measurement with PLS model based spectrum analysis

Ruirui zhang, Yao Wen, Longlong Li, Liping Chen, Gang Xu, Yanbo Huang, Qing Tang, Tongchuan Yi

Abstract


Unmanned aerial vehicle (UAV) chemical application is widely used for crop protection, and spraying pattern is one of the most important factors that influence the chemical control efficacy. A method for UAV spraying pattern measurement with partial least squares (PLS) model based spectrum analysis was proposed in this study to measure the UAV spraying pattern more accurately. The method involved the steps of fluorescent tracer solution spray and its droplets collection, the spectrum on paper strip acquiring, spectrum processing and analysis, PLS modeling. In order to verify the applicability of the method and obtain the parameters of the PLS model, UAV spraying experiments were performed in the field. Then Fluorescent tracer solution was sprayed and its droplets are collected by paper strip, and the original spectrum on paper strip obtained by a spectrometer was processed by the Savitzky-Golay and standard normalized variable (SNV) method. The prediction model of coverage rate selected as the droplet deposition parameter to measure the UAV spraying pattern, was established by using PLS method. To verify the superiority of the PLS model, a traditional linear regression (LR) model of coverage rate was established simultaneously. The results demonstrate that the method with PLS model based spectrum analysis can measure the UAV spraying pattern effectively, and PLS model has a better performance of RV2=0.94 and RMSEP=0.9446 than that of the LR model.
Keywords: pattern measurement, UAV spraying, spectrum model, coverage rate, partial least squares method
DOI: 10.25165/j.ijabe.20201303.5341

Citation: Zhang R R, Wen Y, Li L L, Chen L P, Xu G, Huang Y B, et al. Method for UAV spraying pattern measurement with PLS model based spectrum analysis. Int J Agric & Biol Eng, 2020; 13(3): 22–28.

Keywords


pattern measurement, UAV spraying, spectrum model, coverage rate, partial least squares method

Full Text:

PDF

References


Zhou Z Y, Zang Y, Luo X W, Lan Y B, Xue X Y. Technology innovation development strategy on agricultural aviation industry for plant protection in China. Transactions of the CSAE, 2013; 29(24): 1–10.

Huang Y B, Thomson S J, Hoffmann W C, Lan Y B, Fritz B K. Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int J Agric & Biol Eng, 2013; 6(3): 1–10.

Tang Q, Zhang R R, Chen L P, Xu M, Yi T C, Zhang B. Droplets movement and deposition of an eight-rotor agricultural UAV in downwash flow field. Int J Agric & Biol Eng, 2017; 10(3): 47–56.

Li J Y, Lan Y B, Wang J W, Cheng S D, Huang C, Liu Q, et al. Distribution law of rice pollen in the wind field of small UAV. Int J Agric & Biol Eng, 2017; 10(4): 32–40.

Jensen P K, Olesen M H. Spray mass balance in pesticide application: A review. Crop Protection, 2014; 61: 23–31.

Zhu H, Salyani M, Fox R D. A portable scanning system for evaluation of spray deposit distribution. Computers and Electronics in Agriculture, 2011; 76(1): 38–43.

Ferguson J C, Chechetto R G, O'Donnell C C, Fritz B K, Hoffmann W C, et al. Assessing a novel smartphone application-SnapCard, compared to five imaging systems to quantify droplet deposition on artificial collectors. Computers and Electronics in Agriculture, 2016; 128: 193–198.

Chen S D, Lan Y B, Li J Y, Zhou Z Y, Jin J, Liu A M. Effect of spray parameters of small unmanned helicopter on distribution regularity of droplet deposition in hybrid rice canopy. Transactions of the CSAE, 2016; 32(17): 40–46.

Zhang S C, Xue X Y, Qin W C, Sun Z, Ding S M, Zhou L X. Simulation and experimental verification of aerial spraying drift on N-3 unmanned spraying helicopter. Transactions of the CSAE, 2015; 31(3): 87–93.

Wang L, Lan Y B, Hoffmann W C, Fritz B K, Chen D, Wang S M. Design of variable spraying system and influencing factors on droplets deposition of small UAV. Transactions of the CSAM, 2016; 47(1): 15–22.

Wang X N, He X K, Song J L, Wang Z C, Wang C L, Wang S L, et al. Drift potential of UAV with adjuvants in aerial applications. Int J Agric & Biol Eng, 2018; 11(5): 54–58.

Xue X Y, Tu K, Qin W C, Lan Y B, Zhang H H. Drift and deposition of

ultra-low altitude and low volume application in paddy field. Int J Agric & Biol Eng, 2014; 7(4): 23–28.

Duga A T, Delele M A, Ruysen K, Dekeyser D, Nuyttens D, et al. Development and validation of a 3D CFD model of drift and its application to air-assisted orchard sprayers. Biosystems Engineering, 2017; 154: 62–75.

Zhang B, Tang Q, Chen L P, Zhang R R, Xu M. Numerical simulation of spray drift and deposition from a crop spraying aircraft using a CFD approach. Biosystems Engineering, 2018; 166: 184–199.

Yuan X, Qi L J, Ji R H, Zhang J H, Wang H, Huang S K. Analysis on features of air-velocity distribution and droplets deposition pattern for greenhouse air-assisted mist sprayer. Transactions of the CSAM, 2012; 43(8): 71–77.

Kesterson M A, Luck J D, Sama M P. Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application. Sensors, 2015; 15(12): 31965–31972.

Zhang R R, Chen L P, Lan Y B, Xu G, Kan J, Zhang D Y. Development of a deposit sensing system for aerial spraying application. Transactions of the CSAM, 2014; 45(8): 123–127.

Zhang H H, Lan Y B, Lacey R, Hoffmann W C, Martin D E, Fritz B, et al. Ground-based spectral reflectance measurements for evaluating the efficacy of aerially-applied glyphosate treatments. Biosystems Engineering, 2010; 107(1): 10–15.

Lv M Q, Xiao S P, Tang Y, He Y. Influence of UAV flight speed on droplet deposition characteristics with the application of infrared thermal imaging. Int J Agric & Biol Eng, 2019; 12(3): 10–17.

Bae Y, Koo Y M. Flight attitudes and spray patterns of a roll-balanced agricultural unmanned helicopter. Applied Engineering in Agriculture, 2013; 29(5): 675–682.

Zhang D Y, Lan Y B, Wang X, Zhou X G, Cheng L P, Li B, et al. Assessment of aerial agrichemical spraying effect using moderate-resolution satellite imagery. Spectroscopy and Spectral Analysis, 2016; 36 (6): 1971–1977.

Zheng Y J, Yang S H, Lan Y B, Hoffmann C, Zhao C J, Chen L P, et al. A novel detection method of spray droplet distribution based on LIDARs. Int J Agric & Biol Eng, 2017; 10(4): 54–65.

Zhang R R, Wen Y, Yi T C, Cheng L P, Xu G. Development and application of aerial spray droplets deposition performance measurement system based on spectral analysis technology. Transactions of the CSAE, 2017; 33(24): 80–87.

Wen Y, Zhang R R, Cheng L P, Huang Y B, Yi T C, Xu G, Li L L, Andrew J H. A new spray deposition pattern measurement system based on spectral analysis of a fluorescent tracer. Computers and Electronics in Agriculture, 2019; 160: 14–22.

Yang X W, Zhou J Z, He X Q, Herbst A. Influences of nozzle types on pesticide deposition and insecticidal effect to wheat aphids. Transactions of the CSAE, 2012; 28(7): 46–50.

Xu G, Chen L P, Zhang R R. An image processing system for evaluation of aerial application quality. International Conference on Intelligent Information Processing. ACM, 2016. 10.1145/3028842.3028895

Hou P G, Li N , Chang J, Wang S T, Song T. Research on analysis of oil in water based on the joint optimization of Savitzky-Golay smoothing and IBPLS models. Spectroscopy and Spectral Analysis, 2015; 35(6): 1529–1533.

Kamruzzaman M, Makino Y, Oshita S. Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning. Journal of Food Engineering, 2016; 170(7): 8–15.

Hodge V J, Austin J. A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 2004; 22(2): 85–126.

Cao D S, Liang Y Z, Xu Q S, Li H D, Chen X. A new strategy of outlier detection for QSAR/QSPR. Journal of Computational Chemistry, 2010; 31(3): 592–602.

Li H D, Xu Q S, Liang Y Z. libPLS: An integrated library for partial least squares regression and linear discriminant analysis. Chemometrics and Intelligent Laboratory Systems, 2018; 176: 34–43.

Li H D, Xu Q S, Liang Y Z. Random frog: an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. Analytica Chimica Acta, 2012; 740: 20–26.

Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 2001; 58(2): 109–130.




Copyright (c) 2020 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office