Big Data-Driven Optimization of Agricultural Resource Allocation Efficiency Using the DEA Model

With the advancement of agricultural modernization, how to efficiently allocate agricultural resources has become an urgent problem to be solved. This study uses big data technology to optimize the allocation efficiency of agricultural resources and uses the data envelopment analysis (DEA) model to carry out empirical analysis. Therefore, 10 representative agricultural enterprises are selected as research samples, and a DEA model containing various input and output factors is constructed. Regarding input factors, it covers land area, labor force, consumption of fertilizers and pesticides, and distribution costs. As output factors, including the yield, freshness and market value of agricultural products. The results show obvious differences in resource allocation efficiency among different agricultural enterprises, and some enterprises have redundant input and insufficient output problems. Through in-depth analysis using the DEA model, this paper puts forward optimization suggestions, such as adjusting the proportion of production factors and reducing resource waste. Experiment shows that the big data-driven DEA model can effectively evaluate and optimize the efficiency of agricultural resource allocation and provide scientific support for improving the efficiency of agricultural production.