A Lightweight Recognition Method for Rice Growth Period Based on Improved YOLOv5s.

Abstract

The identification of the growth and development period of rice is of great significance to achieve high-yield and high-quality rice. However, the acquisition of rice growth period information mainly relies on manual observation, which has problems such as low efficiency and strong subjectivity. In order to solve these problems, a lightweight recognition method is proposed to automatically identify the growth period of rice: Small-YOLOv5, which is based on improved YOLOv5s. Firstly, the new backbone feature extraction network MobileNetV3 was used to replace the YOLOv5s backbone network to reduce the model size and the number of model parameters, thus improving the detection speed of the model. Secondly, in the feature fusion stage of YOLOv5s, we introduced a more lightweight convolution method, GsConv, to replace the standard convolution. The computational cost of GsConv is about 60-70% of the standard convolution, but its contribution to the model learning ability is no less than that of the standard convolution. Based on GsConv, we built a lightweight neck network to reduce the complexity of the network model while maintaining accuracy. To verify the performance of Small-YOLOv5s, we tested it on a self-built dataset of rice growth period. The results show that compared with YOLOv5s (5.0) on the self-built dataset, the number of the model parameter was reduced by 82.4%, GFLOPS decreased by 85.9%, and the volume reduced by 86.0%. The mAP (0.5) value of the improved model was 98.7%, only 0.8% lower than that of the original YOLOv5s model. Compared with the mainstream lightweight model YOLOV5s- MobileNetV3-Small, the number of the model parameter was decreased by 10.0%, the volume reduced by 9.6%, and the mAP (0.5:0.95) improved by 5.0%-reaching 94.7%-and the recall rate improved by 1.5%-reaching 98.9%. Based on experimental comparisons, the effectiveness and superiority of the model have been verified.

Plant phenotyping relevance

イネの生育ステージという植物状態を画像から自動認識する軽量手法を開発し、自作データセットで性能検証しているため、フェノタイピング手法が中心である。

Code and data availability

The paper uses a self-built rice growth period image dataset (4844 images) and an improved YOLOv5s model, but no public dataset, code, or trained model is released. The Data Availability Statement reads 'Not applicable,' and no repository, deposit, or author-provided URL for data or code appears anywhere in the article

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