The application of appropriate nitrogen and molybdenum fertilizer can improve the growth and development of plants, increase photosynthetic efficiency, regulate active oxygen metabolism in vivo, maintain the oxidation balance required for normal cell growth, enhance the activity of crop antioxidant enzymes and dry matter accumulation, so as to increase crop yield. In order to investigate the effect mechanism of nitrogen fertilizer combined with foliar molybdenum fertilizer on millet yield and antioxidant enzyme activity, two nitrogen application gradients (N₀ (0 kg/hm2) and N₁ (75 kg/hm2) were set with millet variety Changnong 47 as material. Leaf molybdenum fertilizer Mo₀ (0 %), Mo₁ (0.1 %), Mo₂ (0.2 %), Mo₃ (0.3 %) and Mo₄ (0.4 %) were sprayed at the joining stage. Photosynthetic parameters, chlorophyll content, antioxidant enzyme activity, dry matter accumulation and yield at the complete ripening stage were measured. After the analysis of significant difference, the results showed that the combined application of molybdenum nitrogen significantly increased the yield of millet, and the maximum yield under the Mo3 treatment was 5869.04 kg/hm₂ under the N1 condition, which was 13% higher than that under the no fertilization treatment. The total dry matter accumulation was 36.96 g/ plant, which was 31% higher than that without fertilization. The net photosynthetic rate (Pn) and stomatal conductivity (gs) increased first and then decreased with the increase of molybdenum fertilizer concentration gradient, and reached the maximum values under N₁Mo₃ condition, which were 24.77 μmol•m_-2•s_-1 and 391.33 mol•m_-2•s_-1, respectively. Application of molybdenum fertilizer can improve the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) in the test samples. In conclusion, under N₁ condition, Mo₃ (0.3%) treatment can effectively improve millet yield, photosynthetic characteristics and antioxidant enzyme activity. The results of this study provided theoretical basis and data support for the application of nitrogen and molybdenum fertilizer in millet production.

The levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizers can affect the ratio of amylose and amylopectin components. These components are typically determined using chemical analysis methods, which are difficult to apply on a large scale for nutrient deficiency diagnosis and do not meet the original intention of precise agricultural development. This study set up five different gradient treatments for organic fertilizer (sheep manure) application. Hyperspectral imaging combined with chemometrics was employed to achieve rapid and non-destructive detection of the content of amylose and amylopectin in foxtail millet flour. The aim of this study was to determine the optimal dosage of organic fertilizers for application. Spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), and iterated retaining informative variables (IRIVs) was employed for key band extraction. Partial least squares regression (PLSR) was then used to establish the prediction model and regression equation, which was used to visualize the two components. Results demonstrated that the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC–RF–IRIV–PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23 g/(100 g), respectively. Similarly, the prediction model for amylopectin using MSC–CARS–IRIV–PLSR also demonstrated good performance, with the R and RMSE values predicted to be 0.59 and 7.34 g/(100 g), respectively. The results of visualization and physicochemical determination showed that the amount of amylopectin accumulation was highest, and the amount of amylose was lowest, under the application of 22.5 t/ha of organic fertilizer. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research.

The grain filling rate (GFR) plays a crucial role in determining grain yield. However, the regulatory and molecular mechanisms of the grain filling rate (GFR) in foxtail millet remains unclear. In this study, we found that the GFR of ′Changnong No.47′ (CN47) was significantly higher at 14 DAF (days after flowering) and 21 DAF in comparison to ‘Changsheng 13’ (CS13). Furthermore, CN47 also exhibited higher a thousand-grain weight and yield than CS13. Therefore, RNA-seq and UHPLC-MS/MS were used to conduct transcriptome and metabolome analyses during two stages of grain filling in both cultivars. Conjoint analysis of transcriptomics and metabolomics was adopted in order to analyze the biological processes and functional genes associated with GFR. The results identified a total of 765 differentially expressed genes (DEGs) and 246 differentially accumulated metabolites (DAMs) at the 14 DAF stage, while at the 21 DAF stage, a total of 908 DEGs and 268 DAMs were identified. The integrated analysis of co-mapped DAMs and DEGs revealed enriched pathways, including flavonoid biosynthesis, plant hormone signal transduction, tyrosine metabolism, ATP-binding cassette (ABC) transporters, and beta-Alanine metabolism, as well as stilbenoid, diarylheptanoid, and gingerol biosynthesis. In order to elucidate their potential functions in the context of GFR, we developed a gene–metabolite regulatory network for these metabolic pathways. Notably, we found that some genes associated with ABC transporters and the plant hormone signal transduction pathway were implicated in auxin transport and signal transduction, highlighting the crucial role of auxin during grain filling. These findings provide initial insights into the regulatory and molecular mechanisms underlying GFR in foxtail millet, as well as offering valuable genetic resources for further elucidation of GFR in future studies. The findings have also established a theoretical basis for improving the efficiency of yield breeding in foxtail millet.

Foxtail millet is a vital grain whose amino acid content affects nutritional quality. Traditional detection methods are destructive, time-consuming, and inefficient. This work established a rapid and non-destructive method for detecting essential amino acids in the foxtail millet. To address these limitations, this study developed a rapid, non-destructive approach for quantifying eight essential amino acids—lysine, phenylalanine, methionine, threonine, isoleucine, leucine, valine, and histidine—in foxtail millet (variety: Changnong No. 47) using near-infrared hyperspectral imaging. A total of 217 samples were collected and used for model development. The spectral data were preprocessed using Savitzky–Golay, adaptive iteratively reweighted penalized least squares, and standard normal variate. The key wavelengths were extracted using the competitive adaptive reweighted sampling algorithm, and four regression models—Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM)—were constructed. The results showed that the key wavelengths selected by CARS account for only 2.03–4.73% of the full spectrum. BiLSTM was most suitable for modeling lysine (R² = 0.5862, RMSE = 0.0081, RPD = 1.6417). CNN demonstrated the best performance for phenylalanine, methionine, isoleucine, and leucine. SVR was most effective for predicting threonine (R² = 0.8037, RMSE = 0.0090, RPD = 2.2570), valine, and histidine. This study offers an effective novel approach for intelligent quality assessment of grains.

Foxtail millet is a nutritionally important cereal whose fat and protein content directly influence its nutritional quality and processing properties. To overcome the limitations of traditional detection methods, developing rapid, non-destructive, and interpretable models is essential. A total of 214 samples of the foxtail millet cultivar “Changnong No. 47” were used in this study. The Sparrow Search Algorithm was introduced to screen stable key wavelengths by statistically analyzing their selection frequency. Based on the selected wavelengths, quantitative models were constructed using Partial Least Squares Regression (PLS), Random Forest (RF), and Support Vector Machine. The SHapley Additive exPlanations method was employed to quantify the direction and magnitude of contributions of the key wavelengths within the model. Results show the selection of 13 key wavelengths for fat and 15 for protein. The RF model delivered the best prediction for fat content (R_P² = 0.797, RMSE_P = 0.218%, RPD_P = 2.219), while the PLS model performed best for protein content (R_P² = 0.695, RMSE_P = 0.268%, RPD_P = 1.811). The methodology established in this study can not only be applied to the rapid quality assessment of millet but also be extended to analyze the nutritional components of other grains.