Background Boron is an essential micronutrient for plant reproductive growth, and its key function in plants is mediated through cross-linking with the cell wall pectic polysaccharide rhamnogalacturonan II. As an important forage crop, alfalfa suffers from inhibited pollen tube elongation under low boron conditions, leading to floret abortion and reduced seed set. However, the molecular mechanism by which low boron regulates pectin to affect alfalfa pollen-tube elongation remains unclear.Results Under low boron condition, methylesterified pectin shifted from an apical to subapical and shank localization, while de-esterified pectin became predominantly concentrated at the pollen tube apex, indicating a boron-dependent reorganization of pectin distribution. Proteomic analysis of pollen tubes grown under varying boron concentrations revealed significant enrichment of pectin methylesterase activity, polygalacturonase activity, and carbohydrate metabolic processes. Among 56 shared differentially expressed proteins, two pectin methylesterase inhibitors - MsPMEI14 and MsPMEI15 - were identified as candidates potentially associated with this response. Chromosomal localization, phylogenetic, and cis-element analyses suggested that both genes were evolutionarily conserved. Subcellular localization analysis demonstrated that both MsPMEI14 and MsPMEI15 localized to the endoplasmic reticulum and acidic vesicular compartments, indicating that both proteins are secreted to the cell wall via the conventional secretory pathway (CPS), where they regulate pectin methylesterification to support pollen tube elongation.Conclusions These findings suggest that low boron-induced inhibition of pollen tube elongation is associated with altered spatial distribution of methylesterified and de-esterified pectins, potentially mediated through the regulatory activity of MsPMEI14 and MsPMEI15 on pectin methylesterase activity. Together, this study provides new insights into how pectin dynamics respond to low boron conditions to regulate pollen tube elongation, and highlights MsPMEI14 and MsPMEI15 as promising candidates for future functional characterization aimed at elucidating the molecular basis of low boron-induced pollen tube growth inhibition.

Common oat (Avena sativa) is a globally important hexaploid cereal crop with a large and complex genome. While its nuclear and chloroplast genomes have been extensively characterized, the mitochondrial genome of hexaploid oat has remained unresolved, limiting our understanding of organellar evolution and mitochondrial function in this polyploid species. We report the first complete assembly and comprehensive analysis of the A. sativa mitochondrial genome, which comprises two circular chromosomes totaling 634,262 bp with a GC content of 44.11

Alfalfa (Medicago sativa L.) seed production in arid regions is significantly limited by water scarcity. However, achieving optimal irrigation management remains challenging due to intricate interactions between water supply, environmental conditions, and yield components. This study aimed to assess the impact of diverse irrigation treatments on alfalfa seed yield and develop an unmanned aerial vehicle (UAV)-based monitoring system that integrates machine learning and SHapley Additive exPlanations (SHAP) to support future precision irrigation strategies. A three-year field experiment (2023-2025) was conducted in the arid region of Northwest China, applying nine irrigation treatments ranging from 1200 to 3000 m & sup3; /ha. Multispectral images were captured using a DJI Mavic 3 Multispectral UAV at four crucial growth stages (branching, budding, flowering, and pod-setting) across five flight altitudes (12-100 m). A wide range of predictors, including spectral vegetation indices, RGB-derived color, and texture features, along with meteorological variables, were employed to predict seed yield through four machine learning algorithms: Random Forest, Support Vector Regression (SVR), Ridge Regression, and XGBoost. The results showed that treatment T3 (3000 m & sup3;/ha) achieved the highest seed yield (1309 kg/ha), while T7 (1200 m & sup3;/ha) displayed the highest irrigation water use efficiency (0.8 kg/m & sup3;), indicating a significant trade-off between yield maximization and water use efficiency. Structural equation modeling revealed complex yield formation processes, with florets per inflorescence having the largest negative direct impact on seed yield (beta = -0.49, P < 0.001). The machine learning models delivered consistent predictive performance, achieving R & sup2; values of 0.77-0.79 when utilizing integrated features. The optimal configuration was identified at a 20 m flight altitude and the combination of flowering and pod-setting stages (P3 + P4). SHAP analysis revealed that growing season temperature was the most influential predictor (scaled |SHAP| = 1.00), presenting strong negative correlations with yield (Tmin: r = -0.77; Tmax: r = -0.69). A complementary single-feature permutation analysis confirmed this dominance: permuting growing-season maximum temperature alone reduced cross-validated R & sup2; from 0.76 to 0.28, exceeding any UAV-derived feature by nearly an order of magnitude. Because pod-setting features fall outside the actionable window for irrigation decisions, SHAP analysis was further restricted to the budding (P2) and flowering (P3) stages, when irrigation interventions remain effective, to identify critical physiological thresholds during the budding (ExG < 0.251, NMDI < 0.341) and flowering stages (ExG < 0.262, LAB_L < 36.705, HSV_S > 0.377), demonstrating consistent temporal trends across years. By coupling causal inference (SEM), predictive interpretation (SHAP), and convergent permutation evidence, this framework distinguishes predictive from actionable features in alfalfa seed-production irrigation management. In summary, this study provides a robust scientific basis for transitioning from fixed irrigation schedules to adaptive, precision irrigation strategies guided by real-time monitoring of alfalfa physiological status through UAV remote sensing.

Drought stress severely restricts seed germination and seedling establishment in oat (Avena sativa). In this study, we synthesized a novel nanocomposite priming agent, 6-benzylaminopurine-loaded mesoporous silica nanoparticles (6-BA@MSNs), and investigated its effects on drought tolerance and the underlying regulatory mechanisms. Multiple analytical techniques, including transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FT-IR), were used to confirm the successful and stable loading of 6-BA onto MSNs. The synthesized composite exhibited a uniform spherical morphology (ca.55 nm) and good colloidal stability. Germination assays showed that 6-BA@MSNs restored oat germination percentage to levels comparable to those of the non-stressed control and significantly improved root length, seedling length, and fresh weight compared with free 6-BA, MSNs, or their physical mixture treatments. Physiological analyses demonstrated that 6-BA@MSNs alleviated oxidative damage by restoring the activities of drought-inhibited antioxidant enzymes (CAT, POD, and SOD), scavenging excess reactive oxygen species (H2O2 and O2 & sdot;-), reducing MDA accumulation, and improving chlorophyll accumulation and osmotic adjustment capacity. Transcriptomic analysis further revealed that 6-BA@MSNs coordinately regulated the expression of drought-responsive genes by upregulating POD70 to enhance reactive oxygen species (ROS) scavenging, downregulating POD2 and POD4 to alleviate excessive lignification and structural constraints on seedling growth, and upregulating the photosystem II-related genes PsbQ and PsbC to maintain photosynthetic efficiency, thereby supporting seedling development. These findings demonstrate that 6-BA@MSNs effectively integrates the hormonal regulatory effects of 6-BA with the priming advantages and structural protective functions of MSNs, thereby synergistically improving oat seed vigor and drought tolerance. The sustained-release properties of the composite reduced the required dosage of 6-BA and improved its utilization efficiency, highlighting its considerable potential as a sustainable nano-priming strategy for crop production.

Annual climatic and agronomic shifts induce phenotypic plasticity, causing standard deep learning models to fail in high-throughput automated phenotyping tasks, such as alfalfa (Medicago sativa L.) seed maturity assessment. Here, we developed a deep learning-based transfer learning framework to confer climate robustness to such models, validated on a multispectral imaging dataset (365-970 nm) covering five maturity stages across three environmentally distinct years. We designed the Multispectral Spatial Attention Network (MSANet), a hybrid architecture integrating a 3D-CNN backbone with spectral and spatial attention modules to extract complex spatio-spectral features. On single-year data, MSANet achieved 93% classification accuracy, significantly surpassing both traditional Support Vector Machine (77%) and deep learning baselines (e.g., ResNet18, 88%). However, this high intra-year performance did not generalize; direct model transfer to a different year caused accuracy to collapse to 41%, quantifying a profound domain shift. To mitigate this, We proposed an innovative Earth Mover's Distance (EMD)-guided 'diagnose-adapt-finetune' framework. This approach utilized EMD to diagnose layer-specific distributional shifts, employed EMD-guided Adaptive Batch Normalization (AdaBN) to align feature statistics across domains, and concluded with a data-efficient, few-shot fine-tuning strategy. The framework restored predictive accuracy to >90% on out-of-domain data using only 100 labeled samples per class from the target year, representing an approximate 90% reduction in annotation costs compared to full supervision. Crucially, the adapted model exhibited remarkable resilience to real-world data imperfections, maintaining stability under scenarios of class imbalance and label noise. Interpretability analyses further indicated that the model learned biologically plausible spectral correlates associated with seed maturation. Our work presents a generalizable methodology for developing environmentally robust phenotyping platforms, offering a promising pathway to enhance the reliability of AI systems in variable agricultural environments.

Alfalfa (Medicago sativa) is an important legume forage crop. Salt stress severely impairs seed germination, compromising field establishment and subsequent seedling growth. However, the key genes underlying alfalfa seed responses to salt stress during germination remain largely unidentified, hindering targeted genetic improvement of salt tolerance within the genus Medicago. In this study, transcriptomic sequencing was performed on germinated and ungerminated seeds of alfalfa treated with 150 mmol/L NaCl, using the ‘Zhongmu No. 4’ reference genome. Through integrated KEGG pathway enrichment, gene expression fold-change analysis, and Gene Set Enrichment Analysis (GSEA), we identified the gene MsPAL2 (Msa1079410), which encodes the key rate-limiting enzyme in phenylpropanoid metabolism, as a critical salt-tolerance gene. Functional characterization via virus-induced gene silencing (VIGS) revealed that silencing MsPAL2 did not affect seed germination under normal conditions but significantly reduced the germination rate under salt stress. These findings suggest that MsPAL2 acts as a positive regulator of alfalfa seed germination under salt stress. Promoter analysis and AlphaFold3 prediction collectively revealed MYB-binding cis-elements and identified MYB transcription factors as candidate upstream regulators of MsPAL2, laying the groundwork for elucidating its regulatory network. Phenylpropanoid metabolism constitutes a key mechanism underlying salt tolerance in alfalfa. Through transcriptome analysis, we identified MsPAL2, a phenylpropanoid pathway gene, as a critical regulator of alfalfa seed germination under salt stress, providing valuable genetic resources for molecular breeding of salt-tolerant alfalfa.

Soil salinity is a significant environmental challenge that adversely affects plant yield and quality. Zoysiagrass (Zoysia japonica), a member of the Gramineae family, is highly salt-tolerant, making it an excellent model for studying salt stress response mechanisms. We performed physiological and transcriptomic analyses on two contrasting Zoysiagrass germplasm accessions under high salt conditions. The salt-tolerant germplasm ST68 demonstrated superior growth phenotypes, higher chlorophyll and relative water content, greater photochemical efficiency, and lower relative electrolyte leakage and sodium ion content compared to the salt-sensitive germplasm SS9. Transcriptomic analysis revealed differential expression in pathways involved in photosynthesis, flavonoid biosynthesis, cell wall macromolecule catabolism, phosphate ion homeostasis, and reactive oxygen species response in the tolerant vs the sensitive line under salt stress. Notably, the ZjHEMA gene, which encodes glutamyl-tRNA reductase, a rate-limiting enzyme in chlorophyll biosynthesis, was identified as a key regulator due to its significant upregulation under salt stress in the salt-tolerant germplasm, compared to the sensitive one. Overexpression of the salt-responsive glutamyl-tRNA reductase gene, associated with chlorophyll metabolism in Zoysiagrass, in Arabidopsis led to increased salt tolerance, as evidenced by elevated chlorophyll content, relative water content, and photochemical efficiency compared to wild-type plants. Our findings offer new insights into the mechanisms of salt tolerance in Zoysiagrass, laying a foundation for breeding salt-tolerant germplasm.

Boron, an essential micronutrient, plays a crucial role in plant reproductive processes. Boron deficiency is widespread in many regions and significantly reduces alfalfa (Medicago sativa) seed yield, yet the molecular mechanisms underlying its impact on reproductive organ development remain elusive. This study found that boron deficiency inhibited alfalfa pollen tube elongation and was accompanied by elevated reactive oxygen species (ROS) levels in the pollen tube. The application of exogenous hydrogen peroxide (H2O2) and ascorbic acid (AsA) indicated that maintaining ROS homeostasis was crucial for boron-mediated pollen tube elongation. Based on phylogenetic tree analysis and expression pattern analysis, MsWRKY49 was identified as a central regulator responding to low boron. Transgenic analysis showed that mswrky49 mutants alleviated the pollen tube phenotype and reduced ROS accumulation under low boron conditions, while overexpression of MsWRKY49 in alfalfa pollen tubes led to boron sensitivity declining and ROS accumulation. Furthermore, RNA-Seq analysis revealed that several antioxidant-related genes were significantly downregulated in the MsWRKY49-OE lines. Yeast one-hybrid and luciferase reporter assays demonstrated that MsWRKY49 suppressed the expression of MsAPX1, MsGSTU19, MsNADH and MsCu/Zn-SOD, thereby potentially leading to ROS overaccumulation and inhibiting pollen tube elongation. Our results reveal that MsWRKY49 inhibits pollen tube elongation under low-boron conditions by promoting ROS accumulation, providing a new perspective for understanding the molecular mechanism by which boron deficiency causes the decline of seed yield in alfalfa.

Spikelets play a crucial role in photosynthesis during seed formation. This study used two oat (Avena sativa) varieties with significantly different lemma colors, "Challenger" from Canada and "Qinghai444" from China, as experimental materials. Phenotypic, physiological, proteomic, and transcriptional analyses were conducted on oat glumes, lemmas, and paleas after nitrogen application during the grain-filling stage. Results indicated that glumes outperformed lemmas in photosynthetic efficiency. After nitrogen application, "Challenger" glumes exhibited increased stomatal area but decreased chlorophyll a content, maximum photochemical efficiency of photosystem II (Fv/fm), and the quantum yield of photosystem II in steady state (ΦPSII). Concurrently, chloroplast membrane structure was repaired, and the expression of CAO, PsbR, and genes encoding chlorophyll protein complexes (LHCs) was upregulated, enhancing net photosynthetic rate (Pn) and photosynthetic capacity. Conversely, "Qinghai444" glumes showed decreased stomatal area but increased chlorophyll a content, Fv/fm, ΦPSII, and non-photochemical quenching (NPQ). The chloroplast structure of glumes was improved, whereas that of the lemmas was damaged. The CP47 subunit of photosystem II (PSII) accumulated on the thylakoid lamella, and the expression of petA, PsbB, and PsbR genes was upregulated, with no change in Pn or photosynthetic capacity. This study revealed that photosynthetic responses to nitrogen varied among oat varieties and spikelet tissues, with "Challenger" showing more pronounced enhancements. The findings of this study elucidate the patterns of photosynthetic responses to nitrogen in oat spikelets, guiding nitrogen fertilizer use and supporting the breeding of high-yielding oat varieties.

Pollen viability is an important factor influencing the seed-setting rate of angiosperms. The Early 2 Factor (E2F) family of transcription factors is a central regulator of the cell cycle, playing a critical role in processes such as cell division, DNA damage repair, and cell size control. However, the mechanisms of E2F transcription factors on pollen viability in plants have been rarely reported. In this study, we identified five MsE2F genes in the genome of alfalfa (Medicago sativa), classified into three groups (E2F, DEL, and DP) with uneven distribution across four chromosomes. The promoter regions of MsE2F genes contained numerous cis-regulatory elements associated with plant growth, hormone signaling, and stress responses. Furthermore, MsE2F genes were found to be differentially regulated during pollen tube elongation, indicating that members of the MsE2F gene family might play critical roles in pollen viability. The structural features of MsE2F5 that were different from the other four MsE2F proteins suggested that it might have unique functions. Meanwhile, MsE2F5 presented a strong correlation with Actin Binding Proteins (ABPs) related to pollen tube elongation. Yeast one-hybrid assays demonstrated that MsE2F5 regulated the expression of key ABPs in pollen tube elongation, including MsFORMIN, MsADF, MsPROFILIN, and MsFIMBRIN, by binding to their promoter regions. Our findings offer new insights into the role of the E2F gene family in plant reproductive development.

Alfalfa is widely regarded as one of the most important forage crops globally. However, its growth and development are primarily constrained by various abiotic stresses. FIMBRINs are crucial actin-binding proteins involved in regulating cellular dynamics in plants under various stress conditions and during developmental processes. The Fimbrin (FIM) gene family has been reported only in Arabidopsis, while a comprehensive identification of the FIM gene family in alfalfa and the responses of its members to abiotic stresses remain unclear. In this study, six MsFIM genes were identified in the alfalfa genome, distributed across three chromosomes. Phylogenetic analysis grouped these genes into four clades, all containing the conserved CH domain. Gene duplication events suggested that large fragment duplications contribute to gene amplification. Furthermore, cis-regulatory element analysis highlighted their pivotal roles in plant development and response to external abiotic stresses. RT-qPCR analyses revealed that the MsFIM genes exhibited differential expression across various tissues, with predominant expression in flowers, stems, and leaves. The MsFIM genes showed elevated expression under abiotic stresses (drought, cold, and salt) as well as hormone treatment (abscisic acid, ABA), suggesting that they served as positive regulators in alfalfa’s resistance to abiotic stresses and its growth and development. This study investigates the MsFIM genes in alfalfa, further analyzing their potential roles in plant development and response to abiotic stresses. These findings will provide novel insights into the molecular mechanisms of alfalfa's stress response.

This study explored the effects of nitrogen application on superior and inferior grains in smooth bromegrass (Bromus inermis) to provide insights for improving seed quality and yield. The study was conducted using a randomized block design with two nitrogen treatments (0 and 200 kg·N·ha-¹) during the 2021–2022 growing seasons. Seed dry weight, fresh weight, and storage protein content were measured at multiple stages after anthesis. PacBio full-length transcriptome sequencing generated a comprehensive transcriptome consisting of 124,425 high-quality transcripts, and metabolomic profiling were performed across developmsental stages. Genetic transformation in Arabidopsis was used to validate gene function. Nitrogen application significantly increased seed dry and fresh weights and storage protein content, particularly gliadin and glutelin. Metabolomic and transcriptomic analyses revealed that nitrogen treatment upregulated glutamate and asparagine levels and enhanced nitrogen transport and protein synthesis pathways. Two α-gliadin nitrogen-responsive genes, BiGli1 and BiGli2, were identified. Overexpression of these genes in Arabidopsis confirmed their role in regulating seed size and vigor. This study highlights the critical role of α-gliadin in enhancing seed quality, particularly in promoting the development of inferior grains, offering valuable insights for the development of high-yield seed varieties and the optimization of specialized forage seed production.

The high fat content in oat seeds makes them susceptible to aging during storage, leading to reduced seed vigor, delayed germination, and even seed death. Much evidence suggests that lipid remodeling is closely associated with successful seed germination. However, the dynamic behavior and response mechanisms of lipids during the germination of aged oat seeds remain unclear. In this study, ‘Monida’ (aging-tolerant) and ‘Haywire’ (aging-sensitive), were used to investigate the lipid profiles in the embryo and endosperm and the dynamic transcriptomic differences in the embryo during the germination. The results demonstrate that phospholipid alterations during the germination of aged seeds are more significant compared to unaged seeds, indicating that aging affects lipid remodeling during germination, particularly in the ‘Haywire’. Further analysis revealed that the most critical lipid response events occurred at the end of germination stage II (32 h) in embryo, primarily regulated through the PLC-DGK pathway to modulate phospholipid and glycerolipid molecules. Specifically, transcripts of PLC, DGK, and DGAT were upregulated, promoting the generation of diacylglycerol (DG) from various phospholipids, which further increased the monogalactosyldiacylglycerol/digalactosyldiacylglycerol (MGDG/DGDG) ratio, thereby influencing membrane repair. Additionally, at 6 h of germination in aged seeds, PC(3:0/0:0) levels significantly decreased. Compared to ‘Monida,’ the aging-sensitive ‘Haywire’ seeds exhibited substantial production of PE(19:0/0:0) and PC(15:0/0:0) at 32 h of germination, which may be key factors contributing to the seed's sensitivity to aging and the significant reduction in germination percentage after aging. Therefore, PC(3:0/0:0), PE(19:0/0:0), and PC(15:0/0:0) could serve as important lipid metabolic markers in future studies on the mechanisms of oat seed vigor. The findings of this study provide insights into the specificity of lipid remodeling and its response mechanisms during the germination of aged oat seeds, providing a theoretical foundation for the safe preservation of oat germplasm and the development of aging-tolerant varieties.

Seed priming is an effective way to activate defense mechanisms before germination, enhancing seed vigor and stress resistance. Ascorbic acid (AsA) is an important signaling molecule that plays a crucial role in balancing cellular reactive oxygen species. However, whether AsA priming can enhance seed vigor in oat (Avena sativa) and the underlying mechanisms remain unclear. This study primed aged seeds (controlled deterioration at 45°C for 5 days) with 1.5 mM AsA for 24 h. Primed seeds were then sampled after 36 h of imbibition for seed assays. Significant increases in germination percentage, vigor index, shoot and root length, coupled with a significant reduction in mean germination time, demonstrated that AsA priming effectively restored seed vigor. Ultrastructural observations of mitochondria isolated from embryos presented that AsA priming repaired structural damage in aged seeds, with intact double membranes and clear internal cristae observed. Excessive H2O2 accumulation was discovered in mitochondria of aged seeds, while AsA priming reduced H2O2 levels by increasing the activities of CAT, GR, MDHAR and DHAR. AsA priming also increased antioxidant content, particularly DHA, contributing to reduced oxidative stress. Furthermore, transcriptomic analysis highlighted the upregulation of genes associated with antioxidant defense, including APX, CAT, DHAR and MDHAR, indicating enhanced repair and protection pathways in the mitochondrial AsA-GSH cycle. This suggests that AsA priming would increase the activity of antioxidant enzymes, the content of antioxidants, and expression of genes related to AsA-GSH cycle in aged oat seeds, which was conducive to repairing mitochondrial damage and enhancing seed vigor.

Smooth bromegrass (Bromus inermis) was adopted as experiment materials for identifying the seed maturity using a combination of multispectral imaging and machine learning. The trials were conducted to investigate the effects of three nitrogen application levels (0, 100 and 200 kg N ha− 1, defined as CK, N1 and N2 respectively) and two spikelet grain positions: superior grain (SG) at the basal position and inferior grain (IG) at the upper position, on smooth bromegrass seeds. The germination characteristics of the seeds revealed that the variations in nitrogen application and grain positions significantly influenced seeds vigor. The seed vigor of increased gradually with their maturity, reaching a high level at 30 and 36 days after anthesis. A stacking ensemble learning approach was employed to identify the seed maturity based on multispectral imaging and autofluorescence imaging. The results demonstrated that the Ensemble model outperformed Support Vector Machine, Bayesian, XGBoost and Random Forest across all evaluated metrics in different scenarios. The model accuracy in CK, N1 and N2 were 89

The chloroplast biogenesis occurs in cotyledon during alfalfa seed germination before true leaf formation, and is extremely important for the followed plant development and growth. In this study, we conducted a simulation of alfalfa seed germination in the soil by using tin foil and focused on 10 pivotal time points of chloroplast biogenesis in cotyledons before and after light exposure, which showed significant differences in multispectral images, and covered the whole process of chloroplast biogenesis from proplastid, etioplast to mature chloroplast. We revealed three phases that referred to the programmed involvements of photosynthesis promotion, ultrastructure maturity, transcriptomic expression, and protein complex construction, and observed distinct transcriptional expressions of genes from nuclear and chloroplast genomes. In phase I at dark germination before light exposure, chloroplast-encoded genes showed up-regulated expressions together with the importation of chloroplast proteins. In phase II for the first day after light exposure, nuclear-encoded genes' expressions were initiated at 2 h after light exposure (E2h), followed by swift assembly of chloroplast thylakoid membrane protein complexes, and roaring Fv/Fm and contents of chlorophyll a, chlorophyll b and carotenoid. The initiation at E2h was pronounced by the observation of gradual accumulation of single lamella, and facilitated the formation of granum stacks (thylakoid) at E8h in phase II. In phase III from the second day after light exposure, chloroplast became gradually complete with the fully established photosynthetic capacity. Altogether, our results layed a theoretical foundation for enhancing potential photosynthetic efficiency in alfalfa and related species.

Moisture significantly impacts seed sales, storage, and processing. Traditional moisture testing methods are often slow, labor-intensive, and inadequate for the rapid detection demands of modern agriculture, particularly for non-destructive testing of individual seeds. This study applied multispectral imaging to obtain morphological and spectral data from alfalfa seeds at six moisture levels (4 %, 8 %, 12 %, 16 %, 25 %, and 41 %). By integrating algorithms such as Support Vector Machines (SVM), Random Forests (RF), Linear Discriminant Analysis (LDA), Back Propagation Neural Network (BPNN), and normalized typical discriminant analysis (nCDA) algorithms, classification models were developed to distinguish between safe and unsafe moisture levels. The Results indicated that spectral data alone significantly improved model accuracy and prediction. nCDA visualizations effectively illustrated spatial moisture distribution, highlighting stark color differences between seeds in the safe moisture range (4 %, 8 %, 12 %) and those in the unsafe range (16 %, 25 %, 41 %). BPNN exhibited high model precision, achieving a recognition accuracy rate of 90.1 % for safe and unsafe moisture content. Key wavelengths identified by the Permutation method included 970, 880, 570, and 490 nm. Pearson correlation analysis showed a significant positive correlation between germination indicators and spectral data, which strengthened with longer seed storage. These findings confirm the potential of multispectral imaging for assessing the safe moisture content of alfalfa seeds, supporting the development of detection systems for evaluating moisture content in individual seeds. This advancement enables the rapid removal of high-moisture seeds, preventing deterioration during storage.

Drought stress affects plant photosynthesis, leading to a reduction in the quality and yield of crop production. Non-foliar organs play a complementary role in photosynthesis during plant growth and development and are important sources of energy. However, there are limited studies on the performance of non-foliar organs under drought stress. The photosynthetic-responsive differences of oat spikelet organs (glumes, lemmas and paleas) and flag leaves to drought stress during the grain-filling stage were examined. Under drought stress, photosynthetic performance of glume is more stable. Intercellular CO2 concentration (Ci), chlorophyll b, maximum photochemical efficiency of photosystem II. (Fv/Fm), and electron transport rate (ETR) were significantly higher in the glume compared to the flag leaf. The transcriptome data revealed that stable expression of the RCCR gene under drought stress was the main reason for maintaining higher chlorophyll content in the glume. Additionally, no differential expression genes (DEGs) related to Photosystem Ⅰ (PSI) reaction centers were found, and drought stress primarily affects the Photosystem II (PSII) reaction center. In spikelets, the CP43 and CP47 subunits of PSII and the AtpB subunit of ATP synthase were increased on the thylakoid membrane, contributing to photosynthetic stabilisation of spikelets as a means of supplementing the limited photosynthesis of the leaves under drought stress. The results enhanced understanding of the photosynthetic performance of oat spikelet during the grain-filling stage, and also provided an important basis on improving the photosynthetic capacity of non-foliar organs for the selection and breeding new oat varieties with high yield and better drought resistance.

Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha−1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha−1, MAE = 29.43 kg ha−1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year.