Plant stomata play a crucial role in photosynthesis by regulating transpiration and gas exchange. Meanwhile, environmental cues can also affect the formation of stomata. Stomatal formation, therefore, is optimized for the survival and growth of the plant despite variable environmental conditions. To adapt to environmental conditions, plants open and close stomatal pores and even regulate the number of stomata that develop on the epidermis. There are great differences in the leaf structure and developmental origin of the cell in the leaf between Arabidopsis and grass plants. These differences affect the fine regulation of stomatal formation due to different plant species. In this paper, a comprehensive overview of stomatal formation and the molecular networks and genetic mechanisms regulating the polar division and cell fate of stomatal progenitor cells in dicotyledonous plants such as Arabidopsis and Poaceae plants such as Oryza sativa and Zea mays is provided. The processes of stomatal formation mediated by plant hormones and environmental factors are summarized, and a model of stomatal formation in plants based on the regulation of multiple signaling pathways is outlined. These results contribute to a better understanding of the mechanisms of stomatal formation and epidermal morphogenesis in plants and provide a valuable theoretical basis and gene resources for improving crop resilience and yield traits.

Maize is a globally important crop, and reliable identification of seed varieties is vital for breeding and quality assurance. To overcome the limitations of conventional methods, this study developed a non-destructive approach integrating hyperspectral imaging (HSI) and deep learning. A Residual Mamba One-Dimensional Convolutional Neural Network (RM1DNet) is proposed, which integrates residual and Mamba modules to enhance feature learning. RM1DNet achieved 94.85% accuracy in classifying 20 maize seed varieties, outperforming traditional classifiers and baseline deep models. These results demonstrate the robustness and efficiency of RM1DNet, highlighting its potential to advance intelligent seed variety identification using hyperspectral data.

As a pillar grain crop in China’s agriculture, the yield and quality of corn are directly related to food security and the stable development of the agricultural economy. Corn varieties from different regions have significant differences inblade, staminate and root cap characteristics, and these differences provide a basis for variety classification. However, variety characteristics may be mixed in actual cultivation, which increases the difficulty of identification. Deep learning classification research based on corn nodulation features can help improve classification accuracy, optimize planting management, enhance production efficiency, and promote the development of breeding and production technologies. In this study, we established a dataset of maize plants at the elongation stage containing 31,000 images of 40 different types, including corn leaves, staminates, and root caps, and proposed a DenXt framework model. Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. The proposed network model achieves a classification accuracy of 97.79%, which outperforms VGG16, Mobilenet V3, ResNet50 and ConvNeXt image classification models in terms of performance. Compared with the original DenseNet 121 network model, the DenXt model improved the classification accuracy by 3.23% and reduced the parameter count by 32.65%. In summary, the new approach addresses the challenges of convolutional neural networks and provides easy-to-deploy lightweight networks to support corn variety recognition applications.

Leaf color mutants are commonly characterized by altered chlorophyll content and aberrant chloroplast development, making them valuable models for investigating photosynthetic mechanisms and chloroplast biogenesis. In this study, an albino mutant was isolated from a population of transgenic maize breeding lines. Genetic analysis indicated that the mutant phenotype is inherited in a Mendelian manner and is controlled by a single nuclear locus. This was supported by a χ² test performed on the T₂ generation, which confirmed a segregation ratio consistent with 3:1 (176:68, χ² = 1.07 < χ²_0.05 = 3.84, p > 0.05). Microscopic examination revealed the absence of normally developed chloroplasts in mutant cells. Further expression analysis of chloroplast genes via Northern blotting and quantitative real-time PCR (qRT-PCR) suggested that the mutation impairs the regulation of plastid-encoded polymerase (PEP)-dependent chloroplast gene expression. Notably, PCR-based co-segregation analysis indicated that the mutant phenotype is associated with the entire inserted vector sequence, rather than a point mutation or a small genomic deletion. In conclusion, this paper reports the isolation and phenotypic characterization of an etiolated mutant from a transgenic maize breeding population, including comparative ultrastructural analysis of chloroplasts, co-segregation validation, and chloroplast gene expression profiling. These results enhance our understanding of the physiological and molecular mechanisms underlying chlorophyll-deficient mutations in plants.

Malic enzyme (ME), a key enzyme involved in various metabolic pathways, catalyzes the oxidative decarboxylation of malate to generate pyruvate, CO₂, and NADPH. This enzyme plays essential roles in plant growth, development, and stress responses. In this study, 13 maize ME genes were identified by performing homologous sequence alignment using the sequences of the Arabidopsis ME gene family as references. Chromosomal localization analysis demonstrated that ME genes were not detected on chromosomes 9 and 10, whereas the remaining eight chromosomes exhibited an uneven distribution of these genes. Phylogenetic analysis indicated a high degree of conservation between maize ME genes and their orthologs in teosinte (Zea luxurians L.) throughout the evolutionary history of Poaceae crops. Furthermore, cis-acting element analysis of promoters demonstrated that members of the maize ME gene family harbor regulatory elements associated with stress responses, phytohormones signaling, and light responsiveness, which suggests their potential role in abiotic stress adaptation. Expression profiling under stress conditions revealed differential expression levels of maize ME genes, with ZmME13 emerging as a promising candidate gene for enhancing stress resistance. These results lay a solid foundation for further investigation into the biological functions of the maize ME gene family.