Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean.

Abstract

BACKGROUND: Phenotypic characterization of mature soybean pods is a crucial aspect of breeding programs, yet efficiently obtaining accurate pod phenotypic parameters remains a major challenge. Recent advances in deep learning, particularly in keypoint detection models, have introduced innovative methods for pod phenotype extraction. However, precise identification and analysis of fine-scale phenotypic traits in soybean pods remain challenging in current research. RESULTS: We propose Pod-pose, an innovative top-down keypoint detection model for precise soybean pod phenotyping that adapts human pose estimation techniques to plant phenotyping. Specifically, Pod-pose integrates the architectural strengths of various advanced YOLO (You Only Look Once) models through bottleneck structure optimization and positional feature enhancement to achieve superior detection accuracy. Furthermore, we implemented a two-stage detection method augmented with transfer learning, which not only reduces training complexity but also significantly enhances the model's performance. Extensive evaluation of our custom-built dataset demonstrated Pod-Pose's superior performance, with the X variant achieving an Average Precision of 0.912 at an IoU threshold of 0.5 (AP@IoU = 0.5). Notably, four critical pod-related phenotypic traits were successfully quantified: pod length, bending length, curvature, and inflection point width. CONCLUSIONS: This study establishes Pod-Pose as a viable solution for pod phenotyping, with potential applications in soybean breeding optimization.

Plant phenotyping relevance

大豆莢の表現型を抽出する深層学習キーポイント検出モデルを開発し、精度評価と複数形質の定量化を行っており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks describe a custom soybean pod dataset (public + proprietary) and the Pod-pose model, but contain no authors' data or code availability statement, no public deposit URL for the dataset, images, annotations, trained models, or analysis scripts. The only URLs present (YOLOv5/YOLOv8 GitHub repos) are Cc

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