Comparative prediction accuracy of hyperspectral bands for different soybean crop variables: From leaf area to seed composition
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2021-09-15
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Elsevier B.V.
Abstract
Prediction of soybean seed yield and seed composition at a plot scale before harvesting has potential uses in breeding programs for early-season selection and harvesting decisions. Reflectance information from hyperspectral bands have been mainly used for predicting yield and other crop variables. However, an analysis comparing the prediction accuracy among different crop variables such as LAI, biomass, seed yield and seed protein and oil, when using hyperspectral bands as predictors, is lacking. Our objective is to rank the prediction accuracy among different crop variables using hyperspectral bands captured at different timepoints during the growing season. Our hypothesis is based on a physiological framework where crop variables that are closely associated with light interception (i.e., LAI) would be best predicted by the hyperspectral signal (350 nm to 2500 nm) than variables that involve more physiological processes (i.e., biomass, seed yield and seed protein and oil) for their determination. The dataset used for testing this hypothesis involved different genotypes, environments, and management practices. We used Partial Least Squares regression with cross-validation to test the association between the observed variables and the hyperspectral bands. Our results showed that LAI can be best predicted using reflectance information, and suggest that hyperspectral bands are necessary but not sufficient to improve the prediction of other crop variables such as biomass, seed yield, and seed composition traits.
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This is a manuscript of an article published as Chiozza, Mariana V., Kyle A. Parmley, Race H. Higgins, Asheesh K. Singh, and Fernando E. Miguez. "Comparative prediction accuracy of hyperspectral bands for different soybean crop variables: From leaf area to seed composition." Field Crops Research 271 (2021): 108260. doi:10.1016/j.fcr.2021.108260.
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This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.