Abstract
Monitoring of soybean genotypes is important because of intellectual property over seed technology, better management over seed genetics, and more efficient strategies for its agricultural production process. This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf-based hyperspectral reflectance. Leaf reflectance spectra were collected using a hyperspectral proximal sensor. Two experiments were conducted as field trials: one experiment was at Embrapa Soja in the 2016/2017, 2017/2018, and 2018/2019 cropping seasons, where ten soybean genotypes were grown under four water conditions; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season, where nine soybean genotypes were evaluated. The spectral data collected was divided into nine spectral datasets, comprising single and multiple cropping seasons (from 2016 to 2019), and two contrasting crop-growing environments. Principal component analysis, applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset, explained over 94% of the spectral variance in the first three principal components. Linear discriminant analysis, used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype, achieved accuracy between 61% and 100% in the calibration procedure and between 50% and 100% in the validation procedure. Misclassification was observed only between genotypes from the same genetic background. The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf-scale, regardless of the phenological stages or water status to which plants were submitted.
Highlights
Soybean production plays a role in world crop production, with impacts on food security and financial markets
As some of the most harmful factors that interfere in soybean production, especially when they occur in the reproductive phenological stages, have impaired around 30% of the Brazilian soybean production [7] and, in 38 years, caused financial losses of over 79 billion US$ [8]
The accuracy in the calibration procedure ranged from 82% to 100% and in the validation procedure from 75% to 100%. It was observed an accuracy decrease in the classification of soybean genotypes compared to the Linear Discriminant Analysis (LDA) performed using spectral data from Embrapa (Figure 12a) and Unoeste University (Figure 12b). These results corroborate the results found in the LDA
Summary
Soybean production plays a role in world crop production, with impacts on food security and financial markets. Brazil is responsible for one-third of the world’s soybean production [1,2]. Precise, timely, and efficient understanding of soybean production 4.0/). 2021, 13, 172 is important for government and corporate decisions over technical issues, supply regulation, food security, financial market, and strategic planning relative to social environmental and economic policies [3,4,5,6]. As some of the most harmful factors that interfere in soybean production, especially when they occur in the reproductive phenological stages, have impaired around 30% of the Brazilian soybean production [7] and, in 38 years, caused financial losses of over 79 billion US$ [8]. Exploring soybean genotypes with different mechanisms of drought tolerance has proven to be an essential and a necessary task to cope with water deficit periods [9,10]
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