No abstract

In the long developmental process, China’s agriculture has transformed from organic agriculture to inorganic agriculture. New technologies have made the modernization of agriculture possible. However, most older people who are engaged in agriculture may not completely understand the modernization of agriculture. Based on the limitations of traditional image target detection methods, a deep learning-based pest target detection and recognition method is proposed from a blockchain perspective, to analyze and research agricultural data supervision and governance and explore the effectiveness of deep learning methods in crop pest detection and recognition. The comparative analysis demonstrates that the average precision (AP) of GA-CPN-LAR (global activation-characteristic pyramid network-local activation region) increases by 4.2% compared with other methods. Whether under the Inception or ResNet-50 backbone networks, the AP of GA-CPN-LAR is significantly better than other methods. Compared with the ResNet-50 backbone network, GA-CPN-LAR has higher accuracy and recall rates under Inception. Precision-recall curve measurement shows that the proposed method can significantly reduce the false detection rate and missed detection rate. The GA-CPN-LAR model proposed here has a higher AP value on the MPD dataset than the other target detection methods, which can be increased by 4.2%. Besides, the accuracy and recall of the GA-CPN-LAR method corresponding to two representative pests under the initial feature extractor are higher than the MPD dataset baseline. In addition, the research results of the MPD dataset and AgriPest dataset also show that the pest target detection method based on convolutional neural networks (CNNs) has a good presentation effect and can significantly reduce false detection and missed detection. Moreover, the pest regulation based on blockchain and deep learning comprehensively considers global and local feature extraction and pattern recognition, which positively impacts the conscientization of agricultural data processing and promotes the sustainable development of rural areas.

At present, the research on the influence mechanism of psychological capital on farmers’ entrepreneurial behavior is relatively mature. However, the relationship between farmers’ entrepreneurial behavior and macroeconomics by entrepreneurial psychological capital (PsyCap) is still unclear. Based on this, firstly, this work analyzes the entrepreneurial PsyCap in detail. Secondly, the research hypothesis is put forward and a conceptual model is implemented. A questionnaire is designed to analyze the current situation of farmers’ entrepreneurial PsyCap and entrepreneurial behavior. Finally, a structural equation model (SEM) is implemented to explore the relationship between farmers’ entrepreneurial behavior and macroeconomics. The path test of the SEM is utilized to obtain the following. Macroeconomic growth has a significant positive impact on entrepreneurial behavior. Macroeconomics can affect farmers’ entrepreneurial behavior to varying degrees by affecting the four entrepreneurial PsyCap of farmers’ subjective cognition, Tenacity, hope and open-mindedness. This indicates that entrepreneurial PsyCap plays an intermediary role between farmers’ entrepreneurial behavior and macroeconomics. The purpose of this work is to explore the relationship among farmers’ entrepreneurial behavior, macroeconomics, and the role of entrepreneurial PsyCap through empirical analysis, thereby providing a theoretical reference for the subsequent country’s optimization of farmers’ entrepreneurial strategies.