Seed priming is a state-of-the-art, low-cost, and environment-friendly strategy to improve seed germination, seed vigor, abiotic and biotic stress tolerance, and the yield of field and horticultural crops. Seed priming involves imbibing the seeds in a priming solution under a desired set of environmental conditions for a period followed by drying before the radicle protrusion. Several seed priming approaches including hydropriming, osmopriming, bio-priming, hormonal priming, nutrient priming, nanoparticle priming, and electropriming can be effectively employed under different environmental conditions to improve crop growth and stress resilience. Seed priming is known to trigger enzymatic, hormonal, physiological, transcriptomic, metabolomic, and proteomic regulations in seed embryos during seed germination and plant growth, which leads to faster and synchronized seed germination and higher abiotic and biotic stress tolerance in crop plants. Furthermore, seed priming can induce cross-tolerance between abiotic and biotic stressors and induce stress memory for higher resilience of the next generation to environmental stresses. The present review paper discusses the applications of seed priming in biotic and abiotic stress tolerance and the underlying abiotic and biotic stress tolerance physiological, biochemical, and molecular mechanisms of seed priming. Furthermore, we discuss the current challenges/bottlenecks in the widespread application of seed priming in crop production.

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R²_c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R²_cv/R²_v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R²_cv/R²_v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.

Novel drought-tolerant grain legumes like mothbean (Vigna acontifolia), tepary bean (Phaseolus acutifolius), and guar (Cyamopsis tetragonoloba) may also serve as summer forages, and add resilience to agricultural systems in the Southern Great Plains (SGP). However, limited information on the comparative response of these species to different water regimes prevents identification of the most reliable option. This study was conducted to compare mothbean, tepary bean and guar for their vegetative growth and physiological responses to four different water regimes: 100% (control), and 75%, 50% and 25% of control, applied from 27 to 77 days after planting (DAP). Tepary bean showed the lowest stomatal conductance (g_s) and photosynthetic rate (A), but also maintained the highest instantaneous water use efficiency (WUE_i) among species at 0.06 and 0.042 m³ m⁻³ soil moisture levels. Despite maintaining higher A, rates of vegetative growth by guar and mothbean were lower than tepary bean due to their limited leaf sink activity. At final harvest (77 DAP), biomass yield of tepary bean was 38–60% and 41–56% greater than guar and mothbean, respectively, across water deficits. Tepary bean was the most drought-tolerant legume under greenhouse conditions, and hence future research should focus on evaluating this species in extensive production settings.

Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1,000–1,100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R²_v = 0.53–0.59) and canopy cover (R²_v = 0.72–0.76). The blue/green index (BGI₁) was strongly related to canopy height (R²_v = 0.65–0.78), dry biomass (R²_v = 0.42–0.49), and N concentration (R²_v = 0.70–0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.