Abstract
Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.
Highlights
Soybean (Glycine max (L.) Merr.) is one of the most economically important crops in the world that is used for food, feed and industrial products [1]
The simultaneous improvement in both yield and fresh biomass (FBIO) production in soybean seems to be necessary to meet the various demands in the near future
We implemented an EB based on the bagging strategy to improve the prediction performance of individual algorithms as described by
Summary
Soybean (Glycine max (L.) Merr.) is one of the most economically important crops in the world that is used for food, feed and industrial products [1]. Great attention has been paid to increase soybean fresh biomass (FBIO) due to its biorefinery properties [3]. It would be a significant investment for farmers to make a profit from yield and from. Improving yield and biomass that are considered as complex quantitative traits controlled by several genetic and environmental factors [4] requires significant time and financial investment in breeding programs. Pre-harvest prediction of soybean yield and FBIO will enabled plant breeders to accurately select promising genotypes in large breeding populations at early growth stage while reducing the cost and time in their cultivar development programs [5]
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