Summary: Maize is a major staple crop widely used for food, feedstocks and industrial products. Shade‐avoidance syndrome (SAS), which is triggered when plants sense competition of light from neighbouring vegetation, is detrimental for maize yield production under high‐density planting conditions. Previous studies have shown that the red and far‐red photoreceptor phytochromes are responsible for perceiving the shading signals and triggering SAS in Arabidopsis; however, their roles in maize are less clear. In this study, we examined the expression patterns of ZmPHYC1 and ZmPHYC2 and found that ZmPHYC1, but not ZmPHYC2, is highly expressed in leaves and is regulated by the circadian clock. Both ZmPHYC1 and ZmPHYC2 proteins are localized to both the nucleus and cytoplasm under light conditions and both of them can interact with themselves or with ZmPHYBs. Heterologous expression of ZmPHYCs can complement the Arabidopsis phyC‐2 mutant under constant red light conditions and confer an attenuated SAS in Arabidopsis in response to shading. Double knockout mutants of ZmPHYC1 and ZmPHYC2 created using the CRISPR/Cas9 technology display a moderate early‐flowering phenotype under long‐day conditions, whereas ZmPHYC2 overexpression plants exhibit a moderately reduced plant height and ear height. Together, these results provided new insight into the function of ZmPHYCs and guidance for breeding high‐density tolerant maize cultivars.

Maize (Zea mays) originated in southern Mexico and has spread over a wide latitudinal range. Maize expansion from tropical to temperate regions has necessitated a reduction of its photoperiod sensitivity. In this study, we cloned a quantitative trait locus (QTL) regulating flowering time in maize and show that the maize ortholog of Arabidopsis thaliana EARLY FLOWERING3, ZmELF3.1, is the causal locus. We demonstrate that ZmELF3.1 and ZmELF3.2 proteins can physically interact with ZmELF4.1/4.2 and ZmLUX1/2, to form evening complex(es; ECs) in the maize circadian clock. Loss-of-function mutants for ZmELF3.1/3.2 and ZmLUX1/2 exhibited delayed flowering under long-day and short-day conditions. We show that EC directly represses the expression of several flowering suppressor genes, such as the CONSTANS, CONSTANS-LIKE, TOC1 (CCT) genes ZmCCT9 and ZmCCT10, ZmCONSTANS-LIKE 3, and the PSEUDORESPONSE REGULATOR (PRR) genes ZmPRR37a and ZmPRR73, thus alleviating their inhibition, allowing florigen gene expression and promoting flowering. Further, we identify two closely linked retrotransposons located in the ZmELF3.1 promoter that regulate the expression levels of ZmELF3.1 and may have been positively selected during postdomestication spread of maize from tropical to temperate regions during the pre-Columbian era. These findings provide insights into circadian clock-mediated regulation of photoperiodic flowering in maize and new targets of genetic improvement for breeding.

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models—AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2—are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.