The mechanization rate of cotton picking continues to increase with the continuous improvement and development of China's agricultural modernization level. However, when picking cotton, the machine cannot distinguish between cotton fibers and impurities well, resulting in a certain gap in impurity content compared to manually picked cotton. This paper combines machine vision and image processing technology to adopt an improved Canny-based impurity image processing algorithm. By performing light processing, selecting a color space, filtering images, and removing noise from machine-harvested cotton images, the suppression of virtual edges on impurity images allows for more accurate identification of impurities on the cotton surface. Finally, experimental details and results conclusively demonstrate the effectiveness of this method, providing a basis for detecting and classifying cotton impurities.

Fake news detection has become a significant research top in natural language processing. Since the outbreak of the covid-19 epidemic, a large amount of fake news about covid-19 has spread on social media, making the detection of fake news a challenging task. Applying deep learning models may improve predictions. However, their lack of explainability poses a challenge to their widespread adoption and use in practical applications. This work aims to design a deep learning framework for accurate and explainable prediction of covid-19 fake news. First, we choose BiLSTM as the base model and improve the classification performance of the BiLSTM model by incorporating BERT-based distillation. Then, a post-hoc interpretation method SHAP is used to explain the classification results of the model to improve the transparency of the model and increase people's confidence in the practical application. Finally, utilizing visual interpretation methods, such as significance plots, to analyze specific sample classification results for gaining insights into the key terms that influence the model’s decisions. Ablation experiments demonstrated the reliability of the explainable method.