Yield Estimation of Mulched Winter Wheat Based on UAV Remote Sensing Optimized by Vegetation Index
Abstract
In order to further improve the accuracy of UAV remote sensing yield estimation, taking the mulched winter wheat from 2021 to 2022 as the research object, the coating background of the multispectral images at the greening stage, jointing stage, ear pumping stage and filling stage was removed, and the best remote sensing window period was selected, and a mulched winter wheat yield estimation model was constructed based on the optimal vegetation index. The results showed that the canopy reflectivity was closer to the true value after removing the coating background by the support vector machine supervised classification method, and the yield estimation accuracy of the ear stage and the grouting stage was higher. The correlation analysis between vegetation index and yield at different growth stages showed that the best remote sensing window period was the ear extraction period. When the optimal vegetation index was selected based on stepwise regression and full subset regression, it was found that the yield inversion model had the highest accuracy when the screening variables were MCARI, MSR, EVI2, NDRE, VARI, NDGI, NGBDI, ExG based on stepwise regression. In addition, among the yield inversion models constructed by three machine learning methods, partial least squares, artificial neural network and random forest, the random forest model based on stepwise regression method had the highest inversion accuracy, with an R2 of 0.82 and an RMSE of 0.84t/hm2. The research result can provide technical support for improving the accuracy of remote sensing yield estimation and realizing the fine management of agricultural production.
Keywords: mulched winter wheat;vegetation index;yield estimation;UAV remote sensing;feature selection;machine learning
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WANG Lei, FAN Tinglu, VAN Van, et al. Effect of different mulch cultivation on soil moisture, yield and quality of dryland winter wheat[ J ]. Journal of Soil and Water Conservation, 2023, 37(3); 248 -257.
LONGFEI Z, RAN M, XING Y, et al. Improved yield prediction of ratoon rice using unmanned aerial vehicle-based multi¬temporal feature method [ J]. Rice Science, 2023, 30(3): 247 -256.
GEIPEL J, LINK J, CLAUPEIN W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system J . Remote Sensing, 2014, 6(11): 10335.
DU M , NOGUCHI N. Multi-temporal monitoring of wheat growth through correlation analysis of satellite images,unmanned aerial vehicle images with ground variable [J]. IFAC-Papers On Line, 2016, 49( 16); 5 -9.
ZHU Wanxue, LI Shiji, ZHANG Xubo, et al. Estimation of winter wheat yield using optimal vegetation indices from unmanned aerial vehicle remote sensing [J]. Transactions of the CSAE, 2018, 34(11): 78 -86. (in Chinese)
SHEN Yangvang, CHEN Zhichao, HU Hao, et al. Estimation of winter wheat yield based on UAV multi-temporal remote sensing image[J]. Journal of Triticeae Crops, 2021 , 41 (10): 1298 - 1306.
MA Xueqin, WU Shufang, GUO Nini. Effects of film mulch on soil water content and temperature in winter wheat field [ J ] .Research of Soil and Water Conservation, 2018, 25(6); 343 -347.
CHEN Y, LIU T, TIAN X, et al. Effects of plastic film combined with straw mulch on grain yield and water use efficiency of winter wheat in Loess Plateau [J] . Field Crops Research, 2015, 172:53 -58.
YAN S, WU Y, FAN J, et al. Optimization of drip irrigation and fertilization regimes to enhance winter wheat grain yield by improving post-anthesis dry matter accumulation and translocation in northwest China [J]. Agricultural Water Management.
YAO Zhihua, CHEN Junying, ZHANG Zhitao, et al. Effect of plastic film mulching on soil salinity inversion by using UAV multispectral remote sensing [J]. Transactions of the CSAE, 2019, 35( 19) ;89 -97. (in Chinese)
TAO Huilin, XU Liangji, FENG Haikuan, et al. Winter wheat yield estimation based on LAV hyperspectral remote sensing data. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(7) ; 147 - 155.
MAIMAITIJIANG M, SAGAN V, SIDIKE P, et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning [J]. Remote Sensing of Environment, 2020, 237:1 1 1599.
W ANG Lijuan, KONG Yuru, YANG Xiaodong, et al. Classification of land use in farming areas based on feature optimization random forest algorithm [J]. Transactions of the CSAE, 2020, 36(4) ;244 -250. (in Chinese)
SONG G, WANG Q. Coupling effective variable selection with machine learning techniques for better estimating leaf photosynthetic capacity in a tree species ( Fagus crenata Blume) from hyperspectral reflectance [J]. Agricultural and Forest Meteorology, 2023, 338:109528.
ZHANG Zhitao, HAN Jia, WANG Xintao, et al. Soil salinity inversion based on best subsets — quantile regression model [J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50( 10) ; 143 - 152.
YAN Haijun, ZHUO Yue, LI Maona, et al. Alfalfa yield prediction using machine learning and UAV multispectral remote sensing [J] . Transactions of the CSAE, 2022, 38(11) ;64—71.
HABOUDANE D. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture [J]. Remote Sensing of Environment, 2004, 90(3) ;337 -352.
XUE J, SU B. Significant remote sensing vegetation indices: a review of developments and applications [J]. Journal of Sensors, 2017, 2017 :1 - 17.
GITELSON A A, MERZLYAK M N. Remote estimation of chlorophyll content in higher plant leaves [J]. International Journal of Remote Sensing, 1997 ,18 ( 12) ; 2691 - 2697.
YANG Jun, DING Feng, CHEN Chen, et al. Correlation of wheat biomass and yield with UAV image characteristic parameters [J] . Transactions of the CSAE, 2019, 35(23); 104 - 110.
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