With global warming, drought stress is becoming increasingly severe, causing serious impacts on crop yield and quality. In order to survive under adverse conditions such as drought stress, plants have evolved a certain mechanism to cope. The tolerance to drought stress is mainly improved through the synergistic effect of regulatory pathways, such as transcription factors, phytohormone, stomatal movement, osmotic substances, sRNA, and antioxidant systems. This study summarizes the research progress on plant drought resistance, in order to provide a reference for improving plant drought resistance and cultivating drought-resistant varieties through genetic engineering technology.

Stalk rot is a prevalent disease of maize (Zea mays L.) that severely affects maize yield and quality worldwide. The ascomycete fungus Fusarium spp. is the most common pathogen of maize stalk rot. At present, the molecular mechanism of Fusarium proliferation during the maize stalk infection that causes maize stalk rot has rarely been reported. In this study, we investigated the response of maize to F. proliferatum infestation by analyzing the phenotypic, transcriptomic, and metabolomic data of inbred lines ZC17 (resistant) and CH72 (susceptible) with different levels of resistance to stalk rot. Physiological and phenotypic results showed that the infection CH72 was significantly more severe than ZC17 after inoculation. Transcriptome analysis showed that after inoculation, the number of differentially expressed genes (DEGs) was higher in CH72 than in ZC17. Nearly half of these DEGs showed the same expression trend in the two inbred lines. Functional annotation and enrichment analyses indicated that the major pathways enriched for DEGs and DEMs included the biosynthesis of plant secondary metabolites, phenylalanine metabolism, biosynthesis of plant hormones, and plant–pathogen interactions. The comprehensive analysis of transcriptome and metabolome data indicated that phenylalanine metabolism and the phenylalanine, tyrosine, and tryptophan biosynthesis pathways played a crucial role in maize resistance to F. proliferatum infection. In addition, a transcription factor (TF) analysis of the DEGs showed that several TF families, including MYB, bHLH, NAC, and WRKY, were significantly activated after inoculation, suggesting that these TFs play important roles in the molecular regulatory network of maize disease resistance. The findings of this study provide valuable insights into the molecular basis of the response of maize to Fusarium proliferatum infection and highlight the importance of combining multiple approaches, such as phenotyping, transcriptomics, and metabolomics, to gain a comprehensive understanding of plant–pathogen interactions.

Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI’s role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding.

The rhizosphere microbiome plays crucial roles in plant health by regulating nutrient cycling and enhancing stress resilience. However, due to its complexity, the rhizosphere microbiome is quite challenging to analyze using conventional approaches. Recent advances in artificial intelligence (AI) offer unprecedented opportunities to decipher intricate microbial interactions and leverage their potential for crop breeding. In this review, we assess AI methodologies derived from human microbiome studies that address foundational data challenges, including high dimensionality, compositionality, and sparsity. Next, we examine the uses of these methods for the functional prediction of microbial traits. We then shift our focus to the rhizosphere, exploring AI-driven approaches for predictive modeling of rhizosphere dynamics, integrating plant phenotypic and microbiome data, and designing synthetic microbial communities (SynComs). Finally, we discuss the major challenges and future prospects of using AI in rhizosphere microbiome research. Specifically, we propose an emerging AI paradigm that integrates complementary inside-out (hologenome-based genomic selection) and outside-in (SynCom design) strategies, powered by transformative technologies such as federated learning, large language models, digital twins, and autonomous AI agents. This review underscores the potential for AI to revolutionize microbiome science and crop improvement.

Southern corn rust (SCR), caused by the obligate biotrophic fungus Puccinia polysora Underw., represents one of the most devastating threats to maize production, potentially resulting in yield losses exceeding 50%. Due to global climate change and cropping practices, epiphytotics of SCR have been increasingly reported, and are progressively spreading from tropical and subtropical maize growing areas to higher latitude areas. Over the past decade, researchers worldwide have undertaken extensive investigations into SCR, encompassing its occurrence and transmission pathways, the causative pathogen, the identification of resistant/tolerant germplasms along with associated genes/QTL, as well as potential control strategies. Nevertheless, information pertaining to this disease remains fragmented; thus far, standardized preventive and control measures have yet to be established. In response to this situation, this review seeks to comprehensively synthesize research findings on SCR while providing valuable insights into its occurrence, prevention, and control strategies aimed at mitigating the adverse impact and losses caused by SCR on global maize production.

The trihelix transcription factor, which is a plant-specific family, play a critical role in plant growth and development and stress responses. Drought is the main limiting factor affecting yield of maize (Zea mays). However, the identification and characterization of this gene family in maize and its biological functions in response to drought stress have not been reported. Here, 46 Zea mays trihelix genes (ZmTHXs) were identified in the genome. Phylogenetic analysis of the ZmTHXs revealed that the genes were clustered into five subfamilies: GT-1, GT-2, GTγ, SH4, and SIP1. Chromosomal localization analysis showed that the 46 ZmTHXs were unevenly distributed across 10 chromosomes in maize. Cis-acting elements related to abiotic stress in ZmTHXs were found. Most ZmTHXs genes showed significant changes in expression levels under drought treatment. In addition, ZmTHX15-overexpressing Arabidopsis exhibited stronger drought tolerance with less secondary oxidative damage and higher photosynthetic rate. These findings could serve as a basis for future studies on the roles of ZmTHXs and the potential genetic markers for breeding stress-resistant and high-yielding maize varieties.