Spectroscopy and Spectral Analysis, Volume. 42, Issue 5, 1620(2022)
Comparison of Machine Learning Algorithms for Remote Sensing Monitoring of Rice Yields
Figures & Tables(11)
Fig. 1. Correlation between canopy spectra and growth parameters at different growth stages of rice in 2019 and 2020
Fig. 2. Correlation between canopy spectra and growth parameters at different growth stages of rice in 2020
Fig. 4. Yield prediction model based on full band spectrum-growth parameters
Fig. 6. Yield prediction model based on full band spectrum-growth parameters
Fig. 7. Yield prediction model based on full band spectrum-growth parameters-crop nutrient absorption
Table 1. Test data acquisition
View tableView in ArticleTable 1. Test data acquisition
参数 2019年 2020年 分化期 抽穗期 分化期 抽穗期 冠层光谱 √ √ √ √ 长势参数(AGB, LAI) √ √ √ √ 土壤养分参数 未获取 √ √
Table 2. Correlation between biomass, LAI, yield and crop nutrient uptake in 2020
View tableView in ArticleTable 2. Correlation between biomass, LAI, yield and crop nutrient uptake in 2020
作物养分吸收量/
(kg·hm-2)相关系数r 产量 AGB LAI 分化期 抽穗期 分化期 抽穗期 至抽穗期作物吸收N量 0.713** 0.797** 0.623** 0.789** 0.731** 至抽穗期作物吸收P量 -0.086 -0.085 -0.181 -0.093 -0.098 至抽穗期作物吸收K量 0.526* 0.536* 0.425 0.591** 0.490* 至成熟期作物吸收N量 0.723** 0.595** 0.609** 0.818** 0.716** 至成熟期作物吸收P量 -0.242 -0.288 -0.415 -0.254 -0.378 至成熟期作物吸收K量 0.402 0.380 0.275 0.478* 0.341
Table 3. Model accuracies based on the data in 2019 and 2020
View tableView in ArticleTable 3. Model accuracies based on the data in 2019 and 2020
参数 模型 精度 分化期 抽穗期 R2 NRMSE
/%R2 NRMSE
/%BRR 0.897 1 6.76 0.864 7 7.76 全波段 SVR 0.877 6 7.59 0.865 5 7.82 PLSR 0.754 1 10.44 0.555 1 14.04 BRR 0.903 0 6.57 0.915 2 6.24 全波段+AGB, LAI SVR 0.850 1 8.22 0.870 9 8.01 PLSR 0.752 0 10.48 0.560 7 13.95
Table 4. Model construction accuracy based on the data in 2020
View tableView in ArticleTable 4. Model construction accuracy based on the data in 2020
参数 模型 精度 分化期 抽穗期 R2 NRMSE
/%R2 NRMSE
/%全波段 BRR 0.902 2 5.54 0.849 3 6.89 SVR 0.836 2 7.30 0.827 6 7.41 PLSR 0.644 8 10.53 0.549 5 11.85 全波段+
AGB, LAIBRR 0.925 0 4.86 0.905 7 5.45 SVR 0.837 0 7.26 0.845 7 7.36 PLSR 0.654 6 10.38 0.659 8 11.58 全波段+
AGB、 LAI+
作物养分吸收量BRR 0.940 3 4.34 0.922 4 4.95 SVR 0.845 7 7.03 0.889 0 6.39 PLSR 0.659 8 10.30 0.575 3 11.51
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Xia JING, Jie ZHANG, Jiao-jiao WANG, Shi-kang MING, You-qiang FU, Hai-kuan FENG, Xiao-yu SONG. Comparison of Machine Learning Algorithms for Remote Sensing Monitoring of Rice Yields[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1620
Paper Information
Category: Research Articles
Received: Sep. 30, 2021
Accepted: --
Published Online: Nov. 10, 2022
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