Comparison of Mask-R-CNN and Thresholding-Based Segmentation for High-Throughput Phenotyping of Walnut Kernel Color.

ABSTRACT: High-throughput phenotyping has become essential for plant breeding programs, replacing traditional methods that rely on subjective scales influenced by human judgment. Machine learning (ML) computer vision systems have successfully used convolutional neural networks (CNNs) for image segmentation, providing greater flexibility than thresholding methods that may require carefully staged images. This study compares two quantitative image analysis methods, rule-based thresholding using the magick package in R and an instance-segmentation pipeline based on the widely used Mask-R-CNN architecture, and then compares the output of each to two different sets of human evaluations. Walnuts were collected over three years from over 3000 individual trees maintained by the UC Davis walnut breeding prog

SUBMITTER: Lee SH 

PROVIDER: S-EPMC12610562 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature