Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to rapidly and accurately obtain the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), total chlorophylls (Chls) and total carotenoids (Cars) as indicators to study the variations in the leaf positions of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR–HSI). In this study, a “coarse–fine” screening strategy was proposed using competitive adaptive reweighted sampling (CARS) and the iteratively retained informative variable (IRIV) algorithm to extract the characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS–IRIV–PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R²), root mean square error (RMSE) and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43 and 2.38 for Chla; 0.8391, 0.53 and 2.49 for Chlb; 0.7899, 2.24 and 2.18 for Chls; and 0.7577, 0.27 and 2.03 for Cars, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of the pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis and fertilization decisions in plant growth management.

Leaf nitrogen and chlorophyll, crucial crop status indicators, enable precision fertilization through rapid monitoring. This study investigated greenhouse tomatoes subjected to varying nitrogen concentrations in nutrient solutions. Hyperspectral data from leaves across ten nitrogen levels, different growth stages, and leaf positions were integrated with synchronously measured nitrogen and chlorophyll contents. The analysis systematically revealed differences in these indicators under nitrogen stress at various growth stages and leaf positions. The 12-step “coarse–fine–optimal” feature wavelength selection strategy was proposed to identify sensitive spectral bands. The PLSR model was established with a strong predictive performance. Using the optimal model, indicator values for each pixel were retrieved and visualized via pseudocolor imaging, illustrating the distribution of physiological parameters at different scales and growth stages, and aiding in the interpretation of nitrogen stress responses. This study provides a scientific basis for optimizing nitrogen fertilization strategies, contributing to improved tomato yield and quality, reduced environmental impact, and the sustainable development of facility-based agriculture.

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.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

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.