不同分辨率无人机数码影像的马铃薯地上生物量估算研究

doi:10.3964/j.issn.1000-0593(2021)05-1470-07

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Abstract Above ground biomass (AGB) is an important parameter that characterizes crop life activities and is particularly critical for crop growth monitoring and yield prediction. Therefore, obtaining AGB information quickly and accurately is of great significance for monitoring crop growth, guiding agricultural management and improving yield. Using UAV as a platform to carry digital camera sensors, due to the advantages of strong maneuverability, low price and high spatial resolution, and to estimate crop AGB timely and accurately has become one of the hotspots in remote sensing estimation research. As the accuracy of the AGB estimation model for digital images of different flying heights with different resolutions of UAV is different, this study tried to set up 5 types of flying heights at 10, 20, 30, 40, and 50 m during the potato tuber growth period to obtain digital images of different resolutions, and to explore its influence on the accuracy of building AGB model based on spectral information, texture features and spectral information + texture features. Firstly, based on the digital image of UAV, the spectral information and texture features are extracted separately, the vegetation index from the spectral information and texture features constructed, combined with the measured AGB obtained by ground experiments respectively for correlation analysis, and the top 10 image indexes and the top 8 texture features with larger absolute values of correlation coefficients are selected separately. Then, three variables integration variance inflation factor (VIF) are used to perform principal component analysis (PCA) dimensionality reduction processing, and the best principal components are obtained and multivariate linear regression (MLR) constructs AGB estimation model. Finally, compare the AGB estimation model precision of digital images with different resolutions with three variables and the same resolution with the same variable. The results show that: (1) When the image resolution changes between 0.43 and 2.05 cm, the correlation between texture features and potato AGB is weaker than that of vegetation index, but both reach a very significant level of correlation (p<0.01). With image resolution is reduced, its correlation is significantly different. (2) Under the same resolution image, spectral information+texture features have the best precision in estimating AGB, followed by a single texture feature model, and a single spectral model has the worst performance. (3) As digital images’ resolution increases, the accuracy of estimating AGB from spectrum information, texture information, and spectrum + texture information gradually improves.

Key words：Potato; Above ground biomass; Image resolution; Spectral information; Texture feature

Received: 2020-10-12 Accepted: 2021-01-27

ZTFLH:

S25

Corresponding Authors: FENG Hai-kuan E-mail: fenghaikuan123@163.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)05-1470-07 OR https://www.gpxygpfx.com/EN/Y2021/V41/I05/1470

[1] Jin X，Kumar L，Li Z，et al. Remote Sensing，2016，8（12）：1.
[2] LIU Yang, FENG Hai-kuan, HUANG Jue, et al(刘杨，冯海宽，黄珏，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2020，36(23)：181.
[3] YUE Ji-bo，YANG Gui-jun，FENG Hai-kuan，et al（岳继博, 杨贵军, 冯海宽, 等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2016，32（18）：175.
[4] YU Lei，HONG Yong-sheng，GENG Lei，et al（于雷，洪永胜，耿雷，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2015，31（14）：103.
[5] LIU Shuai-bing，YANG Gui-jun，JING Hai-tao（刘帅兵，杨贵军，景海涛）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2019，35（11）：75.
[6] CHEN Peng-fei, LIANG Fei（陈鹏飞, 梁飞）. Scientia Agricultura Sinica（中国农业科学），2019，52（13）：2220.
[7] Zhou Z，Jabloun M，Plauborg F，et al. Computers & Electronics in Agriculture，2018，144：154.
[8] TAO Hui-lin，XU Liang-ji，FENG Hai-kuan，et al（陶惠林，徐良骥，冯海宽，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2019，35（19）：107.
[9] ZHANG Ling-xian，CHEN Yun-qiang，LI Yun-xia，et al(张领先，陈运强，李云霞，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2019，39（8）：2501.
[10] JIA Dan，CHEN Peng-fei（贾丹，陈鹏飞）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2020，51（7）：164.
[11] LIU Chang，YANG Gui-jun，LI Zhen-hai，et al（刘畅, 杨贵军, 李振海, 等）. Scientia Agricultura Sinica（中国农业科学），2018，51（16）：3060.
[12] Zheng H，Cheng T，Zhou M，et al. Precision Agriculture，2019，20（3）：611.
[13] Liu Yinuo，Liu Shishi，Li Jing，et al. Computers and Electronics in Agriculture，2019，166：1050.
[14] YANG Fu-qin，FENG Hai-kuan，XIAO Tian-hao，et al（杨福芹，冯海宽，肖天豪，等）. Research of Agricultural Modernization（农业现代化研究），2020，41(4)：718.
[15] CHEN Peng，FENG Hai-kuan，LI Chang-chun, et al（陈鹏，冯海宽，李长春，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2019，35（11）：63.