ArticleFrontiers in plant science2024
YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- UNet-ECA-Bio: a biologically informed deep learning model for high-throughput micro-phenotyping of rice stem vascular bundles.Frontiers in plant science · 2026Article
- A crop cultivation monitoring platform for evaluating the early growth of cucumbers.Frontiers in plant science · 2026Article
- YOLO-based high-throughput phenotyping pipeline for soybean nodulation traits in genomic research.Frontiers in plant science · 2026Article
- LenRuler: a rice-centric method for automated radicle length measurement with multicrop validation.Plant phenomics (Washington, D.C.) · 2025Article
- Full-time sequence assessment of okra seedling vigor under salt stress based on leaf area and leaf growth rate estimation using the YOLOv11-HSECal instance segmentation model.Frontiers in plant science · 2025Article
- VanillaNet-YOLOv8 segment: detection of nano-iron oxide regulation on rice seedling growth vitality under salt stress.Frontiers in plant science · 2025Article
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8 authors.
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Abstract
The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect full-time sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP
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