Background: As a major food crop, maize is highly susceptible to pathogenic bacteria, which greatly reduces its yield and quality. Metabolomics reveals physiological and biochemical changes in organisms and aids in analyzing metabolic changes caused by various factors. Methods: This study utilized metabolomics to examine maize’s metabolic changes after NCLB infestation, aiming to uncover related pathways and potential biomarkers. The metabolite measurements were performed during the maize silking stage. Results: PCA showed an obvious dispersion between the treated and untreated groups. OPLS-DA identified 1274 differential metabolites, with 242 being downregulated (mainly phenolics and esters) and 1032 upregulated (primarily organic acids, amino acids, sugars, and derivatives). KEGG annotation revealed 50 affected metabolic pathways, and the biosynthesis of secondary metab-olites and amino acids was significantly enriched. Conclusions: We hypothesized that metabolic pathways related to sugar metabolism, proline metabolism, and jasmonic acid synthesis are associated with NCLB susceptibility. These findings provide critical insights into the metabolic responses of maize to biotic stress, offering a theoretical basis for future research on plant resistance mechanisms.

As an important factor affecting the yield and quality of grain crops and threatening grain security, traditional pest and disease monitoring can no longer meet the needs of accurate and efficient agricultural production. The development of remote sensing technology provides a new monitoring method, which is specific, accurate and efficient, and provides real-time, rapid and non-destructive spectral data information for the identification of the occurrence and severity of pests and diseases and can realize large-scale monitoring of grain crop pests and diseases. In this paper, through the statistics and analysis of the published literature on remote sensing monitoring of grain crop diseases and pests, the research hotspots and directions of remote sensing monitoring of grain crop diseases and pests are clarified. Based on this foundation, this paper systematically elaborates the mechanism underlying remote sensing-based monitoring and prediction of diseases and insect pests in grain crops. It reviews various remote sensing monitoring approaches for such diseases and pests by leveraging multi-source remote sensing data. Furthermore, it summarizes methodologies for constructing monitoring and prediction models for grain crop diseases and insect pests. Finally, the paper discusses current challenges and future development trends in this field.