Yikang Huang

Achieving automatic question-and-answering for agricultural scenarios based on machine reading comprehension can facilitate production staff to query information and process data efficiently. Nevertheless, when studying agricultural question-and-answer classification, there are barriers, such as small-scale corpus, narrow content range of corpus, or the need for manual annotation. In the context of such production needs, this paper proposed a text classification model based on text-relational chains and applied it to machine reading comprehension and open-ended question-and-answer tasks in agricultural scenarios. This paper modified the BERT network based on semi-supervised and contrastive learning to enhance the model’s performance. By incorporating the text-relational chains with the BERT network, the Chains-BERT model is constructed. Our efficient mode method outperformed other methods on the CAIL2018 dataset. Ultimately, we developed an automatic question-and-answering application to embed the contrastive-learning information aggregation model in this paper. The accuracy of the proposed model exceeded that of several contrasting mainstream models in many open-source datasets. In agricultural scenarios, the model has achieved state-of-the-art levels and is the best in efficiency.

Due to the booming development of computer vision technology and artificial intelligence algorithms, it has become more feasible to implement artificial rearing of animals in real production scenarios. Improving the accuracy of day-age detection of chickens is one of the examples and is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. Due to the huge amount of data and the different computing power of different devices in practical application scenarios, it is important to maximize the computing power of edge computing devices without sacrificing accuracy. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. In order to accommodate different computing power in different scenarios, this paper proposes a dual-ended adaptive federated learning framework; in order to adapt to low computing power scenarios, this paper performs lightweighting operations on the mainstream model; and in order to verify the effectiveness of the model, this paper conducts a number of targeted experiments. Compared with AlexNet, VGG, ResNet and GoogLeNet, this model improves the classification accuracy to 96.1%, which is 14.4% better than the baseline model and improves the Recall and Precision by 14.8% and 14.2%, respectively. In addition, by lightening the network, our methods reduce the inference latency and transmission latency by 24.4 ms and 10.5 ms, respectively. Finally, this model is deployed in a real-world application and an application is developed based on the wechat SDK.

Thanks to the boom of computer vision techniques and artificial intelligence algorithms, it is more available to achieve artificial rearing for animals in real production scenarios. Improving the accuracy of chicken day-age detection is one of the instances, which is of great importance for chicken rearing. To solve this problem, we proposed an attention encoder structure to extract chicken image features, trying to improve the detection accuracy. To cope with the imbalance of the dataset, various data enhancement schemes such as Cutout, CutMix, and MixUp were proposed to verify the effectiveness of the proposed attention encoder. This paper put the structure into various mainstream CNN networks for comparison and multiple ablation experiments. The final experimental results show that by applying the attention encoder structure, ResNet-50 can improve the accuracy of chicken age detection to 95.2%. Finally, this paper also designed a complete image acquisition system for chicken houses and a detection application configured for mobile devices.