ST-YOLO: a deep learning based intelligent identification model for salt tolerance of wild rice seedlings.

Abstract

Background In response to the limited models for salt tolerance detection in wild rice, the subtle leaf features, and the difficulty in capturing salt stress characteristics, resulting in low recognition and detection rates and accuracy, a deep learning-based ST-YOLO wild rice seedling salt tolerance phenotype evaluation and identification model is proposed. Method In order to improve accuracy and achieve model lightweighting, a multi branch structure DBB (Diverse Branch Block) is used to replace the convolutional layers in the C2f module, and a reparameterization module C2f DBB is proposed to replace some C2f modules. Diversified feature extraction paths are introduced to enhance the ability of feature extraction; Introducing CAFM (Context Aware Feature Modulation) convolution and attention fusion modules into the backbone network to enhance feature representation capabilities while improving the fusion of features at various scales; Design a more flexible and effective spatial pyramid pooling layer using deformable convolution and spatial information enhancement modules to improve the model's ability to represent target features and detection accuracy. Results The experimental results show that the improved algorithm improves the average precision by 2.7% compared with the original network; the accuracy rate improves by 3.5%; and the recall rate improves by 4.9%. Conclusion The experimental results show that the improved model significantly improves in precision compared with the current mainstream model, and the model evaluates the salt tolerance level of wild rice varieties, and screens out a total of 2 varieties that are extremely salt tolerant and 7 varieties that are salt tolerant, which meets the real-time requirements, and has a certain reference value for the practical application.

Plant phenotyping relevance

深層学習モデルによる野生イネ幼苗の塩耐性表現型評価・識別が研究の中心であり、モデル改良と精度検証を実施しているため、植物フェノタイピング手法に該当する。

Code and data availability

The article describes a wild rice salt-tolerance image dataset (2,032 images) and the ST-YOLO model, but no supplied block contains any data availability statement, public repository deposit, or authors' URL for the dataset, images, annotations, or code. The only URL present is the CC BY license link, which is not a p�

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