Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

Abstract

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture

“…Then we performed principal component analysis (PCA) on the remaining univariate traits and PH traits and treated PCs as the multivariate traits ( Figure 1C ). Like our rationale for using VIF, we chose to use PCA because of its common use in plant phenotyping studies ( Duc et al., 2023 ), but subsequent variations such as sparse PCA may increase interpretability of the multivariate traits ( Zou et al., 2006 , see Conclusion). We made GWA using univariate traits, PH traits, and multivariate traits by a Multi-Locus Mixed Model (MLMM), evaluating both the optimal and maximum models ( Figure 1D ; Ziegler et al., 2018 ).…”

Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes

“…This approach involves gathering all dimensions of an extensive crop data collection to be processed collectively, leading to a more straightforward interpretation [15,50,51]. Although several studies have employed multivariate analysis on image-derive phenotypic traits [37,38,52,53], the joint application of IBP and multivariate analysis in detecting tomato fruit fresh weight has not been extensively tested, particularly in segregated populations.…”

Editorial: A wonder legume, soybean: prospects for improvement

“…Moreover, the rich genetic diversity of soybeans proves crucial in investigating heterosis and male sterility, fundamental for breeding initiatives ( Ramlal et al., 2022b ; Farinati et al., 2023 ). Additionally, it plays a vital role in weight prediction using image-based machine-learning techniques ( Duc et al., 2023 ). Nonetheless, it’s important to acknowledge that soybean faces substantial challenges due to climate change, making it susceptible to both biotic and abiotic stressors that significantly impair yield and production ( Jiang et al., 2023 ).…”