Intelligent identification method for rice seedling growth stages and its application in laser supplementary lighting control research.

Abstract

Accurately identifying the growth stages of rice seedlings is crucial for managing factory nurseries and ensuring consistent seedling quality. This study introduces PGL-ShuffleNetV2, a lightweight and advanced model designed for efficient and accurate recognition of rice seedling growth stages. The proposed model achieves a streamlined architecture by: 1). removing the second 1 × 1 convolution in the downsampling block's right branch. 2) Reducing the repetition of basic units for improved efficiency. Additionally, the GELU activation function replaces ReLU to enhance nonlinear representation capabilities, and a parallel weighted hybrid attention module (PWMAM) is incorporated to improve feature extraction. Experimental results demonstrate that PGL-ShuffleNetV2 achieves a remarkable 98.80% recognition accuracy and a 98.82% F1 score, with a compact model size of just 0.84 MB. Its optimal balance between accuracy and parameter efficiency makes it highly suitable for deployment on resource-constrained devices, enabling effective monitoring and management of rice seedlings in factory nursery environments. Based on the advantages of this model, this study further applied it to rice seedlings under laser supplementary lighting conditions to investigate the impact of laser on the growth stages of seedlings, providing technical support for the application of laser technology in intelligent seedling cultivation.

Plant phenotyping relevance

イネ幼苗の生育段階を画像等から認識する軽量モデルを開発し、精度・F1スコアを評価したうえで補光条件の生育段階評価に適用しており、植物表現型取得手法が中心である。

Code and data availability

The supplied article blocks describe a rice seedling image dataset (458 original images, augmented to 19,928) and the PGL-ShuffleNetV2 model, but contain no data availability statement, no public dataset deposit, no code repository, and no trained model checkpoint release. No paper-specific public asset is available.

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