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

SIMPLE SUMMARY: Achieving automatic detection of plant diseases in real agricultural scenarios where low-computing-power platforms are deployed is a significant research topic. As fine-grained agriculture continues to expand and farming methods deepen, traditional manual detection methods demand a high labor intensity. In recent years, the rapid advancement of computer network vision has greatly enhanced the computer-processing capabilities for pattern recognition problems across various industries. Consequently, a deep neural network based on an automatic pruning mechanism is proposed to enable high-accuracy plant disease detection even under limited computational power. Furthermore, an application is developed based on this method to expedite the translation of theoretical results into practical application scenarios. 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.