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Abstract Background Chitosan biopolymer is an emerging non-toxic and biodegradable plant elicitor or bio-stimulant. Chitosan nanoparticles (CSNPs) have been used for the enhancement of plant growth and development. On the other hand, NO is an important signaling molecule that regulates several aspects of plant physiology under normal and stress conditions. Here we report the synthesis, characterization, and use of chitosan-GSNO nanoparticles for improving drought stress tolerance in soybean. Results The CSGSNONPs released NO gas for a significantly longer period and at a much lower rate as compared to free GSNO indicating that incorporation of GSNO in CSNPs can protect the NO-donor from rapid decomposition and ensure optimal NO release. CS-GSNONPs improved drought tolerance in soybean plants reflected by a significant increase in plant height, biomass, root length, root volume, root surface area, number of root tips, forks, and nodules. Further analyses indicated significantly lower electrolyte leakage, higher proline content, higher catalase, and ascorbate peroxidase activity, and reduction in MDA and H2O2 contents after treatment with 50 μM CS-GSNONPs under drought stress conditions. Quantitative real-time PCR analysis indicated that CS-GSNONPs protected against drought-induced stress by regulating the expression of drought stress-related marker genes such as GmDREB1a, GmP5CS, GmDEFENSIN, and NO-related genes GmGSNOR1 and GmNOX1. Conclusions This study highlights the potential of nano-technology-based delivery systems for nitric oxide donors to improve plant growth, and development and protect against stresses.

Soybean, a major legume crop, has seen a decline in its production owing to challenges in seed germination and the development of seedlings. Thus, in this study, we systematically investigated the influence of various chitosan–S-nitrosoglutathione (chitosan-GSNO) nanoparticle (0, 25, 50, and 100 µM) and Si (0, 0.5, and 1 mM) priming concentrations on soybean seed germination and seedling growth over five different priming durations (range: 1–5 h at each concentration). Significant differences were observed in all parameters, except seedling diameter, with both treatments. Seed germination was significantly enhanced after 3 h of priming in both treatments. The final germination percentage (FGP), peak germination percentage (PGP), vigor index (VI), seedling biomass (SB), hypocotyl length (HL), and radical length (RL) of 100 μM chitosan-GSNO-nanoparticle-primed seeds increased by 20.3%, 41.3%, 78.9%, 25.2%, 15.7%, and 65.9%, respectively, compared with those of the control; however, the mean germination time (MGT) decreased by 18.43%. Si priming at 0.5 mM increased the FGP, PGP, VI, SB, HL, and RL by 13.9%, 55.17%, 39.2%, 6.5%, 22.5%, and 25.1%, respectively, but reduced the MGT by 12.29% compared with the control treatment. Chitosan-GSNO and Si treatment up-regulated the relative expression of gibberellic acid (GA)-related genes (GmGA3ox3 and GmGA2ox1) and down-regulated that of abscisic acid (ABA)-related genes (GmABA2, GmAAO3, and GmNCED5). Chitosan-GSNO and Si application increased bioactive GA4 levels and simultaneously reduced ABA content. Hence, the use of exogenous chitosan-GSNO nanoparticles and Si as priming agents had a beneficial effect on seed germination and seedling growth because of the up-regulation in the expression of GA and down-regulation in the expression of ABA. Additional research is needed to understand the combined impact of Si and chitosan-GSNO nanoparticles, including their effects on the expression levels of other hormones and genes even in the later growth stage of the crop.

Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans’ genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits’ genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands.

Abstract The beauty of conserving germplasm is the securement of genetic resources with numerous important traits, which could be utilized whenever they need to be incorporated into current cultivars. However, it would not be as useful as expected if the proper information was not given to breeders and researchers. In this study, we demonstrated that there is a large variation, both among and within germplasm, using a low-cost image-based phenotyping method; this could be valuable for improving gene banks’ screening systems and for crop breeding. Using the image analyses of 507 accessions of buckwheat, we identified a wide range of variations per trait between germplasm accessions and within an accession. Since this implies a similarity with other important agronomic traits, we suggest that the variance of the presented traits should be checked and provided for better germplasm enhancement.

To diversify upland cropping systems, Italian ryegrass (Lolium multiflorum; IRG) can be incorporated as forage or green manure to soybean (Glycine max L.). The current study was conducted to analyze the effect of IRG cultivar and usage methods on the subsequent soybean above- and below-ground growth, as well as the yield, under different conditions. Three cycles of crop rotation were implemented with the combination of IRG cultivar (early maturing “Kowinearly”; late maturing “Winterhawk”), IRG usage (green manure for which both above- and below-ground biomass was incorporated, +CC; and forage for which only below-ground biomass was incorporated, −CC), and fallow soil as control. The soybean yield of +CC was consistently high regardless of the IRG cultivar, and it demonstrated an increase even under unfavorable weather conditions, while −CC treated did not differ from control. Incorporated IRG could serve as a starter fertilizer (79 to 156 kg ha−1 of N input). Soybean root characteristic differences showed substantial variability depending on the year and treatments. However, when compared to the control, no adverse effects of IRG were evident. Thus, using IRG as a green manure has the potential to enhance soybean yield, while using IRG as a forage could bring additional harvested matter to the rotational system.

Root morphological traits (RMTs) profoundly influence plant growth, resilience to abiotic stresses, and yield in soybean (Glycine max). In a comprehensive study spanning two consecutive years (2021–2022), the RMTs were assessed in 216 soybean accessions from 34 diverse origins. The investigation involved randomized batches with plants cultivated in PVC pipes filled with horticultural soil and harvested at the V2 growth stage. All the germplasms exhibited significant differences (p < 0.001) in all measured traits, i.e., total root length (TRL), root volume (RV), average diameter (AD), number of tips (NT), number of forks (NF), and tertiary total length (TTL). Among the top 5% performers in TRL, which, interestingly, were exclusively of Korean origin, germplasm IT115491 displayed an impressive average TRL value of 1426.24 cm. Notably, germplasms from Serbia and Korea predominantly occupied the upper AD quantile, with IT156262 exhibiting the highest AD value of 0.57 mm. A correlation analysis showed strong positive associations of TRL with RV (r = 0.85), NT (r = 0.84), NF (r = 0.96), and TTL (r = 0.88), whereas it had a negative association with AD (r = −0.25). A principal component analysis (PCA) showed a cumulative 95% of the total variance in the data in the first three principal components (PCs). PC1 (eigenvalue = 4.64) accounted for a 77.00% variance, with TRL, RV, NF, NT, and TTL exhibiting the highest associated eigenvectors. K-means clustering was performed with three clusters. Cluster 2 contained accessions with higher AD values, whereas Cluster 3 comprised accessions with increased TRL, NT, NF, and TTL, which mostly originated from Korea. Our findings offer targeted insights for plant breeders to optimize specific root traits and enhance crop performance across diverse environmental conditions by strategically targeting these clusters. Additionally, the influence of cultivar origin on root traits warrants further investigation, with implications for future breeding programs.

Accurate and early diagnosis of bean common mosaic virus (BCMV) in Phaseolus vulgaris tissues is critical since the pathogen can spread easily and have long-term detrimental effects on bean production. The use of resistant varieties is a key factor in the management activities of BCMV. The study reported here describes the development and application of a novel SYBR Green-based quantitative real-time PCR (qRT-PCR) assay targeting the coat protein gene to determine the host sensitivity to the specific NL-4 strain of BCMV. The technique showed high specificity, validated by melting curve analysis, without cross-reaction. Further, the symptoms development of twenty advanced common bean genotypes after mechanical BCMV-NL-4 infection was evaluated and compared. The results showed that common bean genotypes exhibit varying levels of host susceptibility to this BCMV strain. The YLV-14 and BRS-22 genotypes were determined as the most resistant and susceptible genotypes, respectively, in terms of aggressiveness of symptoms. The accumulation of BCMV was analyzed in the resistant and susceptible genotypes 3, 6, and 9 days following the inoculation by the newly developed qRT-PCR. The mean cycle threshold (Ct) values showed that the viral titer was significantly lower in YLV-14, which was evident in both root and leaf 3 days after the inoculation. The qRT-PCR thus facilitated an accurate, specific, and feasible assessment of BCMV accumulation in bean tissues even in low virus titers, allowing novel clues in selecting resistant genotypes in the early stages of infection, which is critical for disease management. To the best of our knowledge, this is the first study of a successfully performed qRT-PCR to estimate BCMV quantification.

Soybean (Glycine max) is a crucial legume crop known for its nutritional value, as its seeds provide large amounts of plant protein and oil. To ensure maximum productivity in soybean farming, it is essential to carefully choose high-quality seeds that possess desirable characteristics, such as the appropriate size, shape, color, and absence of any damage. By studying the relationship between seed shape and other traits, we can effectively identify different genotypes and improve breeding strategies to develop high-yielding soybean seeds. This study focused on the analysis of seed traits using a Python algorithm. The seed length, width, projected area, and aspect ratio were measured, and the total number of seeds was calculated. The OpenCV library along with the contour detection function were used to measure the seed traits. The seed traits obtained through the algorithm were compared with the values obtained manually and from two software applications (SmartGrain and WinDIAS). The algorithm-derived measurements for the seed length, width, and projected area showed a strong correlation with the measurements obtained using various methods, with R-square values greater than 0.95 (p < 0.0001). Similarly, the error metrics, including the residual standard error, root mean square error, and mean absolute error, were all below 0.5% when comparing the seed length, width, and aspect ratio across different measurement methods. For the projected area, the error was less than 4% when compared with different measurement methods. Furthermore, the algorithm used to count the number of seeds present in the acquired images was highly accurate, and only a few errors were observed. This was a preliminary study that investigated only some morphological traits, and further research is needed to explore more seed attributes.

The National Agrobiodiversity Center under the Rural Development Administration (RDA) in Jeonju, Republic of Korea stands as the foremost national genebank in the country. Over the years, the National Agrobiodiversity Center has remained committed to enriching its collection with foreign genetic resources, elevating its status to a world-class plant genetic resources (PGR)- holding genebank. Currently, several steps are being undertaken to improve the accessibility of the collection to national as well as international researchers, improve the data available on the resources, and amend the passport information for the accessions. With the implementation of the Nagoya Protocol, the origin of genetic resources is being highlighted as an important input in the passport information. The RDA-Genebank actively responds to the Nagoya Protocol by supplementing passport data for resources lacking information on their origin. In addition, a large number of conserved resources are continuously multiplied, and agronomic traits are investigated concurrently. With the traditional methods of characterization of the germplasm requiring a significant amount of time and effort, we have initiated high-throughput phenotyping using digital techniques to improve our germplasm data. Primarily, we have started adding seed phenotype information followed by measuring root phenotypes which are stored under agronomic traits. This may be the initial step toward using largescale high-throughput techniques for a germplasm. In this study, we aim to provide an introduction to the RDA-Genebank, to adopted international standards, and to the establishment of high-throughput phenotyping techniques for the improvement of passport information.

The application of silicon (Si) fertilizer positively impacts crop health, yield, and seed quality worldwide. Si is a “quasi-essential” element that is crucial for plant nutrition and stress response but is less associated with growth. This study aimed to investigate the effect of Si on the yield of cultivated soybean (Glycine max L). Two locations, Gyeongsan and Gunwi, in the Republic of Korea were selected, and a land suitability analysis was performed using QGIS version 3.28.1. The experiments at both locations consisted of three treatments: the control, Si fertilizer application at 2.3 kg per plot (9 m × 9 m) (T1), and Si fertilizer application at 4.6 kg per plot (9 m × 9 m) (T2). The agronomic, root, and yield traits, as well as vegetative indices, were analyzed to evaluate the overall impact of Si. The results demonstrated that Si had consistently significant effects on most root and shoot parameters in the two experimental fields, which led to significantly increased crop yield when compared with the control, with T2 (22.8% and 25.6%, representing an output of 2.19 and 2.24 t ha−1 at Gyeongsan and Gunwi, respectively) showing a higher yield than T1 (11% and 14.2%, representing 1.98 and 2.04 t ha−1 at Gyeongsan and Gunwi, respectively). These results demonstrate the positive impact of exogenous Si application on the overall growth, morphological and physiological traits, and yield output of soybeans. However, the application of the optimal concentration of Si according to the crop requirement, soil status, and environmental conditions requires further studies.

Abstract In January 2020, anthropogenic emissions in Northeast Asia reduced due to the COVID-19 outbreak. When outdoor activities of the public were limited, PM2.5 concentrations in China and South Korea between February and March 2020 reduced by − 16.8 μg/m3 and − 9.9 μg/m3 respectively, compared with the average over the previous three years. This study uses air quality modeling and observations over the past four years to separate the influence of reductions in anthropogenic emissions from meteorological changes and emission control policies on this PM2.5 concentration change. Here, we show that the impacts of anthropogenic pollution reduction on PM2.5 were found to be approximately − 16% in China and − 21% in South Korea, while those of meteorology and emission policies were − 7% and − 8% in China, and − 5% and − 4% in South Korea, respectively. These results show that the influence on PM2.5 concentration differs across time and region and according to meteorological conditions and emission control policies. Finally, the influence of reductions in anthropogenic emissions was greater than that of meteorological conditions and emission policies during COVID-19 period.

Silicon (Si) is absorbed and accumulated by some plant species; it has been shown to improve plant growth and performance. The beneficial role of Si in plants is based on the fundamental assumptions, and the biological function of Si is still being researched due to its complex nature, distinctiveness, and interaction. The present study included two distinct experiment sets: a screening test and an advanced test. In the initial examination, we used 21 soybean (Glycine max L.) cultivars. Following the evaluation, we chose four cultivars to investigate further. In particular, the positive response cultivars, Taeseon and Geomjeongsaeol, showed a 14% increase in net photosynthesis (PN), and a 19–26% increase in transpiration in Si-treated plants when compared to the control plants. Si-treated Taeseon, Geomjeongsaeol, and Somyongkong, Mallikong cultivars showed significant differences in root morphological traits (RMTs) and root system architecture (RSA) when compared to the control plants. Taeseon and Geomjeongsaeol showed a 26 and 46% increase in total root length (TRL) after Si application, respectively, compared to the control, whereas Mallikong and Somyongkong showed 26 and 20% decrease in TRL after Si treatment, respectively, compared to the control. The Si application enhanced the overall RMTs and RSA traits in Taeseon and Geomjeongsaeol; however, the other two cultivars, Somyongkong and Mallikong, showed a decrease in such RMTs and RATs. Furthermore, to understand the underlying molecular mechanism and the response of various cultivars, we measured the Si content and analyzed the gene expression of genes involved in auxin transport and root formation and development. We showed that the Si content significantly increased in the Si-treated Somyongkong (28%) and Taeseon (30%) compared to the control cultivars. Overall, our results suggested that Si affects root development as well as the genes involved in the auxin synthesis, transport pathway, and modulates root growth leading to cultivar-dependent variation in soybeans.

Facility cultivation has been evolved from greenhouses to smart farms using artificial intelligence (AI) that simulates big data to maximize production. However, the big data for AI in smart farm is not studied well; the effect of differences among varieties within a crop remains unclear. Therefore, the response of two varieties of blueberry, 'Suziblue' and 'Star', to light was tested using SAPD meter in order to demonstrate the environmental responses could be different among varieties within the same species. The results showed that those two varieties had significant differences in SPAD values based on the leaf's position and time, whereas 'Star' did not. This indicates that the effect of light depends on the variety, which implies that other traits and other crops may show similar differences. These results are based on a simple experiment. However, it is enough to elucidate that it is extremely important to characterize responses to the environment not only for each crop but also for each variety to collect data for smart farming to increase accuracy for modeling; consequently, to maximize the efficiency of these facilities.

Plant diseases that affect crop production and productivity harm both crop quality and quantity. To minimize loss due to disease, early detection is a prerequisite. Recently, different technologies have been developed for plant disease detection. Hyperspectral imaging (HSI) is a nondestructive method for the early detection of crop disease and is based on the spatial and spectral information of images. Regarding plant disease detection, HSI can predict disease-induced biochemical and physical changes in plants. Bacterial infections, such as Pseudomonas syringae pv. tabaci, are among the most common plant diseases in areas of soybean cultivation, and have been implicated in considerably reducing soybean yield. Thus, in this study, we used a new method based on HSI analysis for the early detection of this disease. We performed the leaf spectral reflectance of soybean with the effect of infected bacterial wildfire during the early growth stage. This study aimed to classify the accuracy of the early detection of bacterial wildfire in soybean leaves. Two varieties of soybean were used for the experiment, Cheongja 3-ho and Daechan, as control (noninoculated) and treatment (bacterial wildfire), respectively. Bacterial inoculation was performed 18 days after planting, and the imagery data were collected 24 h following bacterial inoculation. The leaf reflectance signature revealed a significant difference between the diseased and healthy leaves in the green and near-infrared regions. The two-way analysis of variance analysis results obtained using the Python package algorithm revealed that the disease incidence of the two soybean varieties, Daechan and Cheongja 3-ho, could be classified on the second and third day following inoculation, with accuracy values of 97.19% and 95.69%, respectively, thus proving his to be a useful technique for the early detection of the disease. Therefore, creating a wide range of research platforms for the early detection of various diseases using a nondestructive method such HSI is feasible.

The root is the most critical plant organ for water and nutrient acquisition. Although the root is vital for water and nutrient uptake, the diverse root characters of soybean still need to be identified owing to the difficulty of root sampling. In this study, we used 150 wild and 50 cultivated soybean varieties to collect root image samples. We analyzed root morphological traits using acquired-image. Except for the main total length (MTL), the root morphological traits for most cultivated and wild plants were significantly different. According to correlation analysis, the wild and cultivated plants showed a significant correlation among total root length (TRL), projected area (PA), forks, total lateral length (TLL), link average diameter, and MTL. In particular, TRL was highly correlated with PA in both cultivated (0.92) and wild (0.82) plants compared with between MTL (0.43 for cultivated and 0.27 for wild) and TLL (0.82 for cultivated and 0.52 for wild). According to principal component analysis results, both plants could be separated; however, there was some overlap of the traits among the wild and cultivated individuals from some regions. Nevertheless, variation among the cultivated plants was higher than that found in the wild plants. Furthermore, three groups, including MTL, TLL, and the remaining traits, could explain all the variances.

Bentazone is a broadleaf post-emergence herbicide widely used for crop production that inhibits photosynthetic activity, resulting in phytotoxicity and injury in plants. Evaluating and identifying herbicide-tolerant genotypes is a critical step in plant breeding programs. In this study, we determined the reaction of 138 Korean soybean cultivars to bentazone using visual evaluation, and selected cultivars were further evaluated to determine the effects of bentazone on physiological parameters. For physiological parameters, we measured the normalized difference vegetation index (NDVI) from hyperspectral reflectance images. From 2 to 4 DAT, the NDVI for two sensitive cultivars was between 0.60 and 0.69, while the NDVI for tolerant cultivars was between 0.70 and 0.86. Photosynthesis rate (A), transpiration (E), stomatal conductance (gsw), and total conductance of CO2 (gtc) were measured using chlorophyll fluorescence. Visual score evaluation showed that moderate bentazone-tolerant cultivars were predominant among the Korean cultivars. For physiological measurements, differences in NDVI were detected between bentazone-tolerant and -sensitive cultivars 2 days after treatment (DAT). However, the A, E, gsw, and gtc levels dramatically decreased 1 DAT in the sensitive cultivars. This study provides insights into the tolerance and sensitivity of soybeans to bentazone.

Silicon (Si), despite being abundant in nature, is still not considered a necessary element for plants. Si supplementation in plants has been extensively studied over the last two decades, and the role of Si in alleviating biotic and abiotic stress has been well documented. Owing to the noncorrosive nature and sustainability of elemental Si, Si fertilization in agricultural practices has gained more attention. In this review, we provide an overview of different smart fertilizer types, application of Si fertilizers in agriculture, availability of Si fertilizers, and experiments conducted in greenhouses, growth chambers, and open fields. We also discuss the prospects of promoting Si as a smart fertilizer among farmers and the research community for sustainable agriculture and yield improvement. Literature review and empirical studies have suggested that the application of Si-based fertilizers is expected to increase in the future. With the potential of nanotechnology, new nanoSi (NSi) fertilizer applications may further increase the use and efficiency of Si fertilizers. However, the general awareness and scientific investigation of NSi need to be thoughtfully considered. Thus, we believe this review can provide insight for further research into Si fertilizers as well as promote Si as a smart fertilizer for sustainability and crop improvement.

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants’ agronomic traits and data-driven HTP resolutions for precision breeding.

Silicon promotes nodule formation in legume roots which is crucial for nitrogen fixation. However, it is very time-consuming and laborious to count the number of nodules and to measure nodule size manually, which led nodule characterization not to be study as much as other agronomical characters. Thus, the current study incorporated various techniques including machine learning to determine the number and size of root nodules and identify various root phenotypes from root images that may be associated with nodule formation with and without silicon treatment. Among those techniques, the machine learning for characterizing nodule is the first attempt, which enabled us to find high correlations among root phenotypes including root length, number of forks, and average link angles, and nodule characters such as number of nodules and nodule size with silicon treatments. The methods here could greatly accelerate further investigation such as delineating the optimal concentration of silicon for nodule formation.

ABSTRACT Drought is a major threat worldwide for crop production, especially due to the rapid climate changes. Current drought solutions involve improving irrigation system, rainwater harvesting, damming, cloud seeding, and changes of cultivation methods. Despite effective, each solution has economic, environmental, and temporal drawbacks. Among all solutions, the most effective, inexpensive and manageable method is the use of drought-tolerant cultivars via plant breeding. However, conventional plant breeding is a time-consuming and laborious task, especially for phenotypic data acquisition of target traits of numerous progenies. High-throughput phenotyping (HTP) is a recently developed method and has potential to overcome the mentioned issues. HTP offers massive, accurate, rapid, and automatic data acquisition in the breeding procedure and can be a breakthrough for developing drought resistant/tolerant cultivars. This study introduces various methods of HTP to detect drought stress, which can accelerate the breeding processes of drought-tolerant cultivars to provide helpful guidelines for breeders and researchers to choose appropriate methods.

Salt stress is one of the major abiotic stressors that causes huge losses to the agricultural industry worldwide. Different strategies have been adopted over time to mitigate the negative impact of salt stress on plants and reclaim salt-affected lands. In the current study, we used silicon (Si) as a tool for salinity alleviation in soybean and investigated the influence of exogenous Si application on the regulation of reactive oxygen and reactive nitrogen species and other salt stress–related parameters of the treated plants. Our results revealed that the canopy temperature was much higher in sole NaCl–treated plants but declined in Si + NaCl–treated plants. Furthermore, the chlorophyll contents decreased with sole NaCl treatment, whereas Si + NaCl–treated plants showed improved chlorophyll contents. In addition, Si application normalized the photosynthetic responses, such as transpiration rate (E) and net photosynthesis rate (PN) in salt-treated plants, and reduced the activity of ascorbate peroxidase and glutathione under salt stress. The expression levels of antioxidant-related genes GmCAT1, GmCAT2, and GmAPX1 started to decline at 12 h after addition of Si to NaCl-treated plants. Similarly, the S-nitrosothiol and nitric oxide (NO)–related genes were upregulated in the salt stress condition but reduced after Si supplementation. Si application downregulated genes associated with reactive oxygen species and reactive nitrogen species and reduced enzymatic and non-enzymatic antioxidants of the treated plants. Results of the current study conclude that Si mitigated the adverse effects of NaCl-induced stress by modulating the crosstalk between antioxidants and NO scavengers. It is suggested that Si may be used in agricultural systems for alleviating salt stress.

Abstract Background Waterlogging (WL) is a key factor hindering soybean crop productivity worldwide. Plants utilize various hormones to avoid various stress conditions, including WL stress; however, the physiological mechanisms are still not fully understood. Results To identify physiological mechanisms during WL stress, different phytohormones, such as ethephon (ETP; donor source of ethylene), abscisic acid, gibberellins, indole-3-acetic acid, kinetin, jasmonic acid, and salicylic acid were exogenously applied to soybean plants. Through this experiment, we confirmed the beneficial effects of ETP treatment. Thus, we selected ETP as a candidate hormone to mitigate WL. Further mechanistic investigation of the role of ETP in waterlogging tolerance was carried out. Results showed that ETP application mitigated WL stress, significantly improved the photosynthesis pigment, and increased the contents of endogenous GAs compared to those in untreated plants. The amino acid contents during WL stress were significantly activated by EPT treatments. The amino acid contents were significantly higher in the 100 μM ETP-treated soybean plants than in the control. ETP application induced adventitious root initiation, increased root surface area, and significantly increased the expressions of glutathione transferases and relative glutathione activity compared to those of non-ETP-treated plants. ETP-treated soybeans produced a higher up-regulation of protein content and glutathione S-transferase (GSTs) than did soybeans under the WL only treatment. Conclusions In conclusion, the current results suggest that ETP application enabled various biochemical and transcriptional modulations. In particular, ETP application could stimulate the higher expression of GST3 and GST8. Thus, increased GST3 and GST8 induced 1) increased GSH activity, 2) decreased reactive oxygen species (ROS), 3) mitigation of cell damage in photosynthetic apparatus, and 4) improved phenotype consecutively.

Abstract Background Silicon (Si) has been known to regulate plant growth; however, the underlying mechanisms of short-term exogenous Si application on the regulation of calcium (Ca) and nitrogen (N), endogenous phytohormones, and expression of essential proteins have been little understood. Results Exogenous Si application significantly increased Si content as compared to the control. Among Si treatments, 1.0 mM Si application showed increased phosphorus content as compared to other Si treatments (0.5, 2.0, and 4.0 mM). However, Ca accumulation was significantly reduced (1.8- to 2.0-fold) at the third-leaf stage in the control, whereas all Si treatments exhibited a dose-dependent increase in Ca as determined by radioisotope 45Ca analysis. Similarly, the radioisotope 15N for nitrogen localization and uptake showed a varying but reduced response (ranging from 1.03–10.8%) to different Si concentrations as compared to 15N application alone. Physiologically active endogenous gibberellin (GA1) was also significantly higher with exogenous Si (1.0 mM) as compared to GA20 and the control plants. A similar response was noted for endogenous jasmonic and salicylic acid synthesis in rice plants with Si application. Proteomic analysis revealed the activation of several essential proteins, such as Fe-S precursor protein, putative thioredoxin, Ser/Thr phosphatase, glucose-6-phosphate isomerase (G6P), and importin alpha-1b (Imp3), with Si application. Among the most-expressed proteins, confirmatory gene expression analysis for G6P and Imp3 showed a similar response to those of the Si treatments. Conclusions In conclusion, the current results suggest that short-term exogenous Si can significantly regulate rice plant physiology by influencing Ca, N, endogenous phytohormones, and proteins, and that 1.0 mM Si application is more beneficial to plants than higher concentrations.

Roots are the most important plant organ for absorbing essential elements, such as water and nutrients for living. To develop new climate-resilient soybean cultivars, it is essential to know the variation in root morphological traits (RMT) among diverse soybean for selecting superior root attribute genotypes. However, information on root morphological characteristics is poorly understood due to difficulty in root data collection and visualization. Thus, to overcome this problem in root research, we used a 2-dimensional (2D) root image in identifying RMT among diverse soybeans in this research. We assessed RMT in the vegetative growth stage (V2) of 372 soybean cultivars propagated in polyvinyl chloride pipes. The phenotypic investigation revealed significant variability among the 372 soybean cultivars for RMT. In particular, RMT such as the average diameter (AD), surface area (SA), link average length (LAL), and link average diameter (LAD) showed significant variability. On the contrary RMT, as with total length (TL) and link average branching angle (LABA), did not show differences. Furthermore, in the distribution analysis, normal distribution was observed for all RMT; at the same time, difference was observed in the distribution curve depending on individual RMT. Thus, based on overall RMT analysis values, the top 5% and bottom 5% ranked genotypes were selected. Furthermore, genotypes that showed most consistent for overall RMT have ranked accordingly. This ultimately helps to identify four genotypes (IT 16538, IT 199127, IT 165432, IT 165282) ranked in the highest 5%, whereas nine genotypes (IT 23305, IT 208266, IT 165208, IT 156289, IT 165405, IT 165019, IT 165839, IT 203565, IT 181034) ranked in the lowest 5% for RMT. Moreover, principal component analysis clustered cultivar 2, cultivar 160, and cultivar 274 into one group with high RMT values, and cultivar 335, cultivar 40, and cultivar 249 with low RMT values. The RMT correlation results revealed significantly positive TL and AD correlations with SA (r = 0.96) and LAD (r = 0.85), respectively. However, negative correlations (r = −0.43) were observed between TL and AD. Similarly, AD showed a negative correlation (r = −0.22) with SA. Thus, this result suggests that TL is a more vital factor than AD for determining SA compositions.

Roots play an essential function in the plant life cycle, as they utilize water and essential nutrients to promote growth and plant productivity. In particular, root morphology characteristics (such as length, diameter, hairs, and lateral growth) and the architecture of the root system (spatial configuration in soil, shape, and structure) are the key elements that ensure growth and a fine-tuned response to stressful conditions. Silicon (Si) is a ubiquitous element in soil, and it can affect a wide range of physiological processes occurring in the rhizosphere of various crop species. Studies have shown that Si significantly and positively enhances root morphological traits, including root length in rice, soybean, barley, sorghum, mustard, alfalfa, ginseng, and wheat. The analysis of these morphological traits using conventional methods is particularly challenging. Currently, image analysis methods based on advanced machine learning technologies allowed researchers to screen numerous samples at the same time considering multiple features, and to investigate root functions after the application of Si. These methods include root scanning, endoscopy, two-dimensional, and three-dimensional imaging, which can measure Si uptake, translocation and root morphological traits. Small variations in root morphology and architecture can reveal different positive impacts of Si on the root system of crops, with or without exposure to stressful environmental conditions. This review comprehensively illustrates the influences of Si on root morphology and root architecture in various crop species. Furthermore, it includes recommendations in regard to advanced methods and strategies to be employed to maintain sustainable plant growth rates and crop production in the currently predicted global climate change scenarios.

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

This study investigated the regulatory role of exogenous salicylic acid (SA) in rice and its effects on toxic reactive oxygen and nitrogen species during short-term salinity stress. SA application (0.5 and 1.0 mM) during salinity-induced stress (100 mM NaCl) resulted in significantly longer shoot length and higher chlorophyll and biomass accumulation than with salinity stress alone. NaCl-induced reactive oxygen species production led to increased levels of lipid peroxidation in rice plants, which were significantly reduced following SA application. A similar finding was observed for superoxide dismutase; however, catalase (CAT) and ascorbate peroxidase (APX) were significantly reduced in rice plants treated with SA and NaCl alone and in combination. The relative mRNA expression of OsCATA and OsAPX1 was lower in rice plants during SA stress. Regarding nitrogenous species, S-nitrosothiol (SNO) was significantly reduced initially (one day after treatment [DAT]) but then increased in plants subjected to single or combined stress conditions. Genes related to SNO biosynthesis, S-nitrosoglutathione reductase (GSNOR1), NO synthase-like activity (NOA), and nitrite reductase (NIR) were also assessed. The mRNA expression of GSNOR1 was increased relative to that of the control, whereas OsNOA was expressed at higher levels in plants treated with SA and NaCl alone relative to the control. The mRNA expression of OsNR was decreased in plants subjected to single or combination treatment, except at 2 DAT, compared to the control. In conclusion, the current findings suggest that SA can regulate the generation of NaCl-induced oxygen and nitrogen reactive species in rice plants.

Soybean (Glycine max L. Merr.) is a crucial oilseed cash crop grown worldwide and consumed as oil, protein, and food by humans and feed by animals. Comparatively, soybean seed yield is lower than cereal crops, such as maize, rice, and wheat, and the demand for soybean production does not keep up with the increasing consumption level. Therefore, increasing soybean yield per unit area is the most crucial breeding objective and is challenging for the scientific community. Moreover, yield and associated traits are extensively researched in cereal crops, but little is known about soybeans’ genetics, genomics, and molecular regulation of yield traits. Soybean seed yield is a complex quantitative trait governed by multiple genes. Understanding the genetic and molecular processes governing closely related attributes to seed yield is crucial to increasing soybean yield. Advances in sequencing technologies have made it possible to conduct functional genomic research to understand yield traits’ genetic and molecular underpinnings. Here, we provide an overview of recent progress in the genetic regulation of seed size in soybean, molecular, genetics, and genomic bases of yield, and related key seed yield traits. In addition, phytohormones, such as auxin, gibberellins, cytokinins, and abscisic acid, regulate seed size and yield. Hence, we also highlight the implications of these factors, challenges in soybean yield, and seed trait improvement. The information reviewed in this study will help expand the knowledge base and may provide the way forward for developing high-yielding soybean cultivars for future food demands. [ABSTRACT FROM AUTHOR]

Accurate and early diagnosis of bean common mosaic virus (BCMV) in Phaseolus vulgaris tissues is critical since the pathogen can spread easily and have long-term detrimental effects on bean production. The use of resistant varieties is a key factor in the management activities of BCMV. The study reported here describes the development and application of a novel SYBR Green-based quantitative real-time PCR (qRT-PCR) assay targeting the coat protein gene to determine the host sensitivity to the specific NL-4 strain of BCMV. The technique showed high specificity, validated by melting curve analysis, without cross-reaction. Further, the symptoms development of twenty advanced common bean genotypes after mechanical BCMV-NL-4 infection was evaluated and compared. The results showed that common bean genotypes exhibit varying levels of host susceptibility to this BCMV strain. The YLV-14 and BRS-22 genotypes were determined as the most resistant and susceptible genotypes, respectively, in terms of aggressiveness of symptoms. The accumulation of BCMV was analyzed in the resistant and susceptible genotypes 3, 6, and 9 days following the inoculation by the newly developed qRT-PCR. The mean cycle threshold (Ct) values showed that the viral titer was significantly lower in YLV-14, which was evident in both root and leaf 3 days after the inoculation. The qRT-PCR thus facilitated an accurate, specific, and feasible assessment of BCMV accumulation in bean tissues even in low virus titers, allowing novel clues in selecting resistant genotypes in the early stages of infection, which is critical for disease management. To the best of our knowledge, this is the first study of a successfully performed qRT-PCR to estimate BCMV quantification. [ABSTRACT FROM AUTHOR]