Recently, deep learning methods are widely used in the rice diseases identification. However, the actual image background of rice disease is complex, the classification performance is not ideal. Therefore, this paper proposed a multi-scale feature extraction method based on stacked autoencoder, named the multi-scale stacked autoencoder (MSSAE), to improve the recognition accuracy of rice diseases. This method extracts the complex rice disease image’s features by two steps. In the first step, the images are preprocessed. Then, the MSSAE extract the multi-scale features through preprocessed rice diseases data in different scales. Through comparative analysis of experiments, the new method achieved greater than 95% precision in the detection of rice diseases. It indicated that the MSSAE model has an outstanding identification performance for actual crop disease image recognition.
The precision spraying of herbicides can significantly reduce herbicide use, and recognizing different field weeds is an important part of it. In order to enhance the efficiency and accuracy of field weed recognition, this article proposed a field weed recognition algorithm based on VGG model called VGG Inception (VGGI). In this article, three optimizations were made. First, the reduced number of convolution layers to reduce parameters of network. Then, the Inception structure was added, which can maintain the main features, and have better classification accuracy. Finally, data augmentation and transfer learning methods were used to prevent the problem of over-fitting, and further enhance the field weed recognition effect. The Kaggle Images dataset was used in the experiment. This work achieved greater than 98% precision in the detection of field weeds. In actual field, the accuracy could reach 80%. It indicated that the VGGI model has an outstanding identification performance for seedling, and has significant potential for actual field weed recognition.
In order to timely and accurately analyze the focus and appeal of public opinion on the Internet, A LSTM-ATTN model was proposed to extract the hot topics and predict their changing trend based on tens of thousands of news and commentary messages. First, an improved LDA model was used to extract hot words and classify the hot topics. Aimed to more accurately describe the detailed characteristics and long-term trend of topic popularity, a prediction model is proposed based on attention mechanism Long Short-Term Memory (LSTM) network, which named LSTM-ATTN model. A large number of numerical experiments were carried out using the public opinion information of "African classical swine fever" event in China. According to results of evaluation indexes, the relative superiority of LSTM-ATTN model was demonstrated. It can capture and reflect the inherent characteristics and periodic fluctuations of the agricultural public opinion information. Also, it has higher convergence efficiency and prediction accuracy.
The widespread dissemination of fake news on social media brings adverse effects on the public and social development. Most existing techniques are limited to a single domain (e.g., medicine or politics) to identify fake news. However, many differences exist commonly across domains, such as word usage, which lead to those methods performing poorly in other domains. In the real world, social media releases millions of news pieces in diverse domains every day. Therefore, it is of significant practical importance to propose a fake news detection model that can be applied to multiple domains. In this paper, we propose a novel framework based on knowledge graphs (KG) for multi-domain fake news detection, named KG-MFEND. The model’s performance is enhanced by improving the BERT and integrating external knowledge to alleviate domain differences at the word level. Specifically, we construct a new KG that encompasses multi-domain knowledge and injects entity triples to build a sentence tree to enrich the news background knowledge. To solve the problem of embedding space and knowledge noise, we use the soft position and visible matrix in knowledge embedding. To reduce the influence of label noise, we add label smoothing to the training. Extensive experiments are conducted on real Chinese datasets. And the results show that KG-MFEND has a strong generalization capability in single, mixed, and multiple domains and outperforms the current state-of-the-art methods for multi-domain fake news detection.
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