The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

Under the background of the global “carbon neutrality” goal, it is of great significance to study the environmental effect of FDI in rapid economic development. This paper proposes an original framework to determine the relative influence of five factors on the Belt and Road countries with a strong FDI-CO₂ association. Based on the panel smooth transition regression (PSTR) model, we establish country-specific and time-specific FDI-CO₂ coefficients for 59 Belt and Road countries during 2003–2018. These coefficients are assumed to change smoothly as a function of five threshold variables, considered the most important in the literature devoted to the FDI-CO₂ correlations. The results show that the degree of GDP per capita, industrialization, openness, and total factor productivity significantly influences the FDI-CO₂ relationship. However, they showed obvious heterogeneity. The coefficient of elasticity of the environmental effects of FDI smoothly transitions between the different intervals, the relationship between GDP per capita and FDI-CO₂ coefficient shows a bell-shaped change, the relationship between degree of trade openness and FDI-CO₂ coefficient also shows a bell-shaped change, the relationship between industrialization level and FDI-CO₂ coefficient shows an inverted N-shaped change, the change of a country’s technological level shows a bell-shaped relationship with the FDI-CO₂ coefficient. The results indicate that PSTR model can be used to study the threshold effect on FDI’s influence on carbon dioxide emissions and the individual and time differences in coefficients of elasticity, to provide a new research perspective and new conclusions on the environmental effect of FDI in rapid economic development.

National parks play a crucial role in protecting ecosystems and biodiversity while facing challenges in balancing nature conservation and economic development. However, because of the difficulty in the unified simulation of natural protection functions and regional socioeconomic development, there is a lack of integrated prediction research on the comprehensive development pathways of national parks and their surrounding areas. This study adopts the Shared Socioeconomic Pathway (SSP) framework that links climate change research with socioeconomic development paths, taking China’s first national park—Sanjiangyuan National Park—region as an example, to conduct research on the synergistic development path of regional socioeconomic development and national parks. The model design includes five typical paths to cover a wide range of socioeconomic development possibilities. These paths are based on different assumptions, including factors such as population growth, economic development, energy use, technological progress, and policy choices. By applying scenario planning methods, optimal development pathways are identified based on environmental, economic, and social priorities. The results show that GDP growth is fastest under the sustainable development and fossil fuel development pathways. By 2050, the population difference under different pathways will approach 100,000, and the GDP gap will be close to CNY 200 billion. This study provides valuable insights for the planning, decision-making, and management of Sanjiangyuan and similar national parks and their surrounding areas, contributing to the promotion of sustainable ecological and economic development of national parks and their regions.