Abstract Digital seed phenotyping offers an efficient and objective alternative to conventional, labor-intensive methods for morphological characterization in plant breeding programs. In castor bean, rapid and reliable tools are essential to support genetic improvement. This study evaluated the potential of digital phenotyping for seed characterization and its application in breeding. Seeds from 65 accessions (2023) and 51 accessions (2024) were photographed with an RGB camera and processed in ImageJ® for extraction of morphological traits. Agreement between digital and manual measurements was assessed by correlation and Bland–Altman analysis, while machine learning models were trained to predict hundred-seed weight (HSW). Genetic diversity was explored using principal component analysis (PCA) and clustering, and variance components and heritability were estimated with mixed linear models. Digital phenotyping showed strong agreement with manual measurements (r = 0.95–0.97) and enabled accurate HSW prediction, with Ridge Regression achieving the best performance (R 2 = 0.88; RMSE = 3.83; MAE = 3.19). PCA explained 85.7% of the variance and revealed three phenotypic clusters. Traits such as seed length (H 2 = 0.88) and aspect ratio (H 2 = 0.87) exhibited high heritability, while roundness (H 2 = 0.79), perimeter (H 2 = 0.72), and area (H 2 = 0.67) were moderate. These findings demonstrate that digital phenotyping is a reliable and high-throughput method for castor bean seed characterization, supporting genotype selection and the integration of machine learning approaches into breeding programs for greater precision and efficiency.
Santos et al. (2026) studied this question.