YOLO11m-SCFPose: An Improved Detection Framework for Keypoint Extraction in Cucumber Fruit Phenotyping

Abstract

To address the issues of low efficiency and large errors in traditional manual cucumber fruit phenotyping methods, this paper proposes the application of keypoint detection technology for cucumber phenotyping and designs an improved lightweight model called YOLO11m-SCFPose. Based on YOLO11m-pose, the original backbone network is replaced with the lightweight StarNet-S1 backbone, reducing model complexity. Additionally, an improved C3K2_PartialConv neck module is used to enhance information interaction and fusion among multi-scale features while maintaining computational efficiency. The Focaler-IoU loss function is employed to improve keypoint localization accuracy. Results show that the improved model achieves an mAP50-95 of 0.924, with a floating-point operation count (GFLOPs) of 32.1, and reduces the model size to 1.229 × 107 parameters. This model demonstrates better computational efficiency and lower resource consumption, providing an effective lightweight solution for crop phenotypic analysis.

Plant phenotyping relevance

キュウリ果実の表現型取得を目的に、キーポイント検出モデルを設計・改良し、精度と計算効率を評価しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's cucumber image dataset (900 images, 2270 after augmentation) and phenotypic measurements are not publicly deposited; the Data Availability Statement says they are available only from the corresponding author upon reasonable request. No author code or model checkpoint URL is provided.

No evidence-backed public reproduction asset is currently recorded.