Classification of Soybean Pubescence from Multispectral Aerial Imagery

Abstract

The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa=0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.

High-throughput field crop phenotyping: current status and challenges

“…Using UAS-RGB images, the emergence of wheat, rice, maize, and potato was evaluated ( Li et al 2019 , Liu et al 2017 , Velumani et al 2021 , Wu et al 2019 ). In a unique study, Bruce et al (2021) assessed the variation of soybean pubescence using UAS multispectral images.…”

Selection based on the phenomenic approach and agronomic ideotic of white oat

“…Faced with the difficulty of using only traditional techniques for selection, phenomics is sought, which collects high‐dimensional phenotypic data, makes it possible to recognize unique characteristics of each genotype, and expresses fast and efficient analysis with high‐resolution and accurate images (Kuo et al., 2016). This type of strategy has contributed to the genetic improvement of rice to estimate plant height, leaf area, number of tillers, and number of grains (Wu et al., 2019); corn in the selection of genotypes tolerant to water stress (Santos et al., 2021); and soybean for tolerance to abiotic stress and qualitative genetic markers (Bruce et al., 2021; Deshmukh et al., 2014; Parmley et al., 2019).…”