Optimization Analysis of Carbon Emission Reduction Paths in Agricultural Transformation Driven by Intelligence

Against the backdrop of the advancement of the carbon peaking and carbon neutrality goals and the accelerated high-quality transformation of agriculture, the regulation of agricultural carbon emissions has become a key measure to realize the coordinated development of ecology and agriculture. Faced with the predicaments of extensive mode, low efficiency and continuously diminishing marginal benefits in traditional agricultural emission reduction, this study focuses on the enabling value of intelligent technologies for agricultural carbon emission reduction. We clarify the connotations of core concepts, analyze the influence paths and synergetic effects of intelligent technologies, and construct a dynamic carbon emission monitoring system, a path optimization model and a verification mechanism. Furthermore, an empirical study is conducted with cases such as the rice planting areas in the Yangtze River Delta to quantify emission reduction effects and explore regional differences and synergetic paths.The study finds that intelligent technologies can achieve significant emission reduction through whole-chain management and control, hybrid intelligent optimization algorithms can effectively screen the optimal emission reduction paths, and regional coordination can address the pain point of unbalanced emission reduction. This research can provide theoretical support and practical reference for the intelligent-driven low-carbon transformation of agriculture, and help the steady realization of agricultural carbon emission reduction goals.