抄録
Soil microbial communities, as key factors in regulating crop disease resistance, have a profound impact on crop health and agricultural productivity due to their complex structures and diverse functions. This study establishes a multidimensional analytical framework based on big data analysis and artificial intelligence (AI) technologies to systematically explore the association between soil microbial communities and crop disease resistance. Firstly, by deeply analyzing the co-regulation mechanisms between soil microbial communities and crop gene expression, the role of specific key microbes in enhancing crop immunity is elucidated. Secondly, the study compares microbial community diversity under different soil environments and investigates their differential effects on disease resistance regulation. Through machine learning models and big data processing methods, the study achieves accurate prediction of microbial effects on crop disease resistance, providing a theoretical basis for optimizing disease-resistant breeding and biological control strategies. Finally, typical case studies demonstrate the practical application of big data and AI technologies in this field of research. This study aims to uncover the intrinsic mechanisms of soil microbial communities in regulating crop disease resistance, offering new insights for the development of microbe-based precision agriculture and disease-resistant breeding technologies, and exploring innovative pathways for sustainable agricultural development.
