In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance.

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology.

The chromosome instability (CIN) is one of the hallmarks of cancer and is closely related to tumor metastasis. However, the sheer size and resolution of histopathology whole-slide images (WSIs) already challenges the capabilities of computational pathology. In this study, we propose a correlation graph attention network (MLP-GAT) that can construct graphs for classifying multi-type CINs from the WSIs of breast cancer. We construct a WSIs dataset of breast cancer from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). Extensive experiments show that MLP-GAT far outperforms accepted state-of-the-art methods and demonstrate the advantages of the constructed graph networks for analyzing WSI data. The visualization shows the difference among the tiles in a WSI. Furthermore, the generalization performance of the proposed method was verified on the stomach cancer. This study provides guidance for studying the relationship between CIN and cancer from the perspective of image phenotype.

Affective understanding of language is an important research focus in artificial intelligence. The large-scale annotated datasets of Chinese textual affective structure (CTAS) are the foundation for subsequent higher-level analysis of documents. However, there are very few published datasets for CTAS. This paper introduces a new benchmark dataset for the task of CTAS to promote development in this research direction. Specifically, our benchmark is a CTAS dataset with the following advantages: (a) it is Weibo-based, which is the most popular Chinese social media platform used by the public to express their opinions; (b) it includes the most comprehensive affective structure labels at present; and (c) we propose a maximum entropy Markov model that incorporates neural network features and experimentally demonstrate that it outperforms the two baseline models.

Monitoring the wheat growth process by machine vision with artificial feature extraction suffers from poor objectivity and low efficiency. To solve this problem, this paper introduces deep learning into monitoring the wheat growth process. The convolutional neural network (CNN) has been widely used in image classification as a common algorithm in deep learning. The application of deep feature extraction networks could automatically recognize and extract image features. However, the large number of parameters and computational overhead brought by regular deep CNNs make it difficult to apply these models to embedded devices with limited storage space and computing power. Therefore, the knowledge distillation method has been proposed for the feature extraction network of the target detection network to improve the performance of shallow feature extraction networks and ensure recognition accuracy while reducing the computational burden and model size. This paper took ResNet50 and MobileNet as two different teacher networks to guide the student model MobileNet for training. The experimental results showed that when ResNet50 was used as teacher model, MobileNet had the best recognition effect. The average recognition accuracy of the student model MobileNet was 97.3%, and the model size was compressed to only 19.71 MB, which is 88.9% smaller than that of ResNet50. Knowledge distillation improves the accuracy of the obtained model and reduces the number of network model parameters. Furthermore, it reduces the model running time and the cost of model deployment greatly. The application of the knowledge distillation method could provide further technical support for intelligent wheat production in the field.