YOLOv8-segANDcal: segmentation, extraction, and calculation of soybean radicle features.

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 50-95 , and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties.

Plant phenotyping relevance

画像セグメンテーションと計算モデルにより、ダイズ幼根の形態特徴と長さを抽出する手法の開発が中心であり、植物表現型計測に該当する。

Code and data availability

The article describes a soybean radicle segmentation model (YOLOv8-segANDcal), a 600-image germination dataset, and a custom monitoring system, but no supplied block contains any public data or code availability statement, repository name, DOI, or URL for the dataset, images, annotations, model weights, or Cal/Segment/

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