Phenotypic detection of flax plants based on improved Flax-YOLOv5.

Abstract

Accurate detection and counting of flax plant organs are crucial for obtaining phenotypic data and are the cornerstone of flax variety selection and management strategies. In this study, a Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data. Based on the solid foundation of the original YOLOv5x feature extraction network, the network structure was extended to include the BiFormer module, which seamlessly integrates bi-directional encoders and converters, enabling it to focus on key features in an adaptive query manner. As a result, this improves the computational performance and efficiency of the model. In addition, we introduced the SIoU function to compute the regression loss, which effectively solves the problem of mismatch between predicted and actual frames. The flax plants grown in Lanzhou were collected to produce the training, validation, and test sets, and the detection results on the validation set showed that the average accuracy (mAP@0.5) was 99.29%. In the test set, the correlation coefficients (R) of the model's prediction results with the manually measured number of flax fruits, plant height, main stem length, and number of main stem divisions were 99.59%, 99.53%, 99.05%, and 92.82%, respectively. This study provides a stable and reliable method for the detection and quantification of flax phenotypic characteristics. It opens up a new technical way of selecting and breeding good varieties.

Plant phenotyping relevance

Flax-YOLOv5による器官検出・計数と、草丈・果実数などの形質推定手法を開発・検証しており、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 paper describes a custom flax plant image dataset (630 plants) and an improved Flax-YOLOv5 model, but no blocks contain any data or code availability statement, public repository deposit, or authors' public URL for the dataset, images, annotations, trained weights, or analysis code. The only URL mentioned (ultraliz

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