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.

Establishing an early warning model for corporate financial crises is important for managing risks and ensuring the continued stability of the capital market. A financial crisis early warning indicator system for listed companies was constructed, which includes financial indicators, management indicators and annual report text tone features. Using techniques such as web crawlers and text sentiment analysis, we collected data related to 820 listed companies in mainland China from 2017 to 2021. Six models were then constructed and their results were compared. The results of the comparative analysis showed that: there is room for AutoML to be applied and explored in this area; the model performance and inference speed of integrated learning CatBoost are substantially improved compared with traditional methods; feature importance rankings help to understand the formation of corporate financial distress. Thus, textual information such as corporate annual reports can help predict financial crises.