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.

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.

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.

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.

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.

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.