PodNet: Pod real-time instance segmentation in pre-harvest soybean fields.

ABSTRACT: Noninvasive analysis of pod phenotypic traits under field conditions is crucial for soybean breeding research. However, previous pod phenotyping studies focused on postharvest materials or were limited to indoor scenarios, failing to generalize to real-field environments. To address these issues, this paper employs an instance segmentation approach for the precise extraction of the pod area from multiplant RGB images in preharvest soybean fields. We first introduce a cost-effective workflow for constructing datasets of densely planted crop images with a uniform backdrop. Starting with video recording, high-quality static frames are collected by automatic selection. Then, a large vision model is explored to facilitate dense annotation and build a large-scale soybean dataset comprising 20k p

SUBMITTER: Zhou S 

PROVIDER: S-EPMC12710035 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature