A simulated framework for china’s rural revitalization enabled by big data and smart agriculture under the perspective of new quality productivity

The paper explores how big data and smart agriculture can be used to promote rural redevelopment in China and how the novel technologies intersect to promote the emergence of new quality productivity. Chinese agricultural sector is faced by a number of problems that include low productivity, poor resource management, and environmental destruction. Digitization, networking, and artificial intelligence (AI) were some of the technologies of big data that are transforming traditional farming methods by allowing real time monitoring, predictive analytics and optimization of resources. This article highlights how these innovations have the effect of improving crop yields and at the same time improving environmental sustainability. By means of the deep learning models, i.e., Bidirectional Long Short-Term Memory (BiLSTM) networks, the given study provides the structure of an intelligent crop recommendation and fertilizer optimization with a concentration on the NPK balance of nutrient concentrations. The framework stated in this study is a model that is simulated in nature, which uses agricultural data that is available to the public and not verified data of the region. Such design proves the computational viability of the method and forms a base to take the method to real Chinese datasets in future. The findings show that data-driven farm systems do not only enhance productivity and decision-making processes, but also trigger resource efficiency and long-term sustainability. The paper presents the possibility of change of digital agriculture in advancing the lives of the rural people, the incomes of the farmers, and a role to contribute to the overall goal of revitalizing the rural areas in China.