A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.

Abstract

Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94.

Plant phenotyping relevance

植物病害を画像から検出するCNN手法の開発・検証が研究の中心であり、病害状態という植物表現型を推定している。低計算環境向けの動的プルーニング、データセット収集、アブレーション検証を含む。

Code and data availability

The paper's own plant disease image dataset (four crops, 12 diseases) is described but no public deposit or availability statement is given. The Kaggle Global Wheat Head dataset is a third-party external dataset used only for generalization testing, not a paper-specific asset. No author code, models, or supplementary材料

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