Comparative transcriptomic analysis of the nodulation-competent zone and inference of transcription regulatory network in silicon applied Glycine max [L.]-Merr. Roots.

KEY MESSAGE: The study unveils Si's regulatory influence by regulating DEGs, TFs, and TRs. Further bHLH subfamily and auxin transporter pathway elucidates the mechanisms enhancing root development and nodulation. Soybean is a globally important crop serving as a primary source of vegetable protein for millions of individuals. The roots of these plants harbour essential nitrogen fixing structures called nodules. This study investigates the multifaceted impact of silicon (Si) application on soybean, with a focus on root development, and nodulation employing comprehensive transcriptomic analyses and gene regulatory network. RNA sequence analysis was utilised to examine the change in gene expression and identify the noteworthy differentially expressed genes (DEGs) linked to the enhancement of soybean root nodulation and root development. A set of 316 genes involved in diverse biological and molecular pathways are identified, with emphasis on transcription factors (TFs) and transcriptional regulators (TRs). The study uncovers TF and TR genes, categorized into 68 distinct families, highlighting the intricate regulatory landscape influenced by Si in soybeans. Upregulated most important bHLH subfamily and the involvement of the auxin transporter pathway underscore the molecular mechanisms contributing to enhanced root development and nodulation. The study bridges insights from other research, reinforcing Si's impact on stress-response pathways and phenylpropanoid biosynthesis crucial for nodulation. The study reveals significant alterations in gene expression patterns associated with cellular component functions, root development, and nodulation in response to Si.

Effect of LED Lights on Secondary Metabolites and Antioxidant Activities in Red Pakchoi Baby Leaves.

Pakchoi (Brassica rapa subsp. chinensis) is one of the most widely consumed vegetables in Asian countries, and it is high in secondary metabolites. The availability, quantity, and quality of light play a critical role in the growth and development of plants. In this study, we investigated the effect of LEDs (light-emitting diodes; white, blue, red, and red + blue) on anthocyanin, glucosinolates, and phenolic levels in red pakchoi baby leaves. On the 24th day after sowing (DAS), red baby pakchoi leaves were harvested, and shoot length, root length, and fresh weight were measured. Among the different LED treatments, there was no significant difference in shoot length, whereas the highest root length was achieved in the red + blue LED treatment (23.8 cm). The fresh weight also showed a significant difference among the different LED treatments. In total, 12 phenolic and 7 glucosinolate individual compounds were identified using high-performance liquid chromatography (HPLC) analysis. The highest total glucosinolate (2937 µg/g dry wt) and phenolic (1589 µg/g dry wt) contents were achieved in baby leaves exposed to red + blue light. Similarly, the highest contents of total anthocyanins (1726 µg/g dry wt), flavonoids (4920 µg/g dry wt), and phenolics (5900 µg/g dry wt) were achieved in the red + blue treatment. Plants exposed to red + blue LED light showed the highest accumulation of anthocyanin, glucosinolates, and phenolic compounds. For antioxidant activity, DPPH (2,2-diphenyl-1-picrylhydrazylradical) free radical scavenging, ABTS (2,2-azinobis (3-ethylbenzothiazoline)-6-sulfonic acid) radical scavenging, and reducing power assays were performed, and the antioxidant activity of red pakchoi baby leaves grown under red + blue LED light was found to be the best. The metabolic profiling of the identified metabolites revealed distinct separation based on the secondary metabolites. This research will be helpful for farmers to choose the best LED light combination to increase the secondary metabolic content in pakchoi plants.

Enhancing citrus fruit yield investigations through flight height optimization with UAV imaging.

Citrus fruit yield is essential for market stability, as it allows businesses to plan for production and distribution. However, yield estimation is a complex and time-consuming process that often requires a large number of field samples to ensure representativeness. To address this challenge, we investigated the optimal altitude for unmanned aerial vehicle (UAV) imaging to estimate the yield of Citrus unshiu fruit. We captured images from five different altitudes (30 m, 50 m, 70 m, 90 m, and 110 m), and determined that a resolution of approximately 5 pixels/cm is necessary for reliable estimation of fruit size based on the average diameter of C. unshiu fruit (46.7 mm). Additionally, we found that histogram equalization of the images improved fruit count estimation compared to using untreated images. At the images from 30 m height, the normal image estimates fruit numbers as 73, 55, and 88. However, the histogram equalized image estimates 88, 71, 105. The actual number of fruits is 124, 88, and 141. Using a Vegetation Index such as IPCA showed a similar estimation value to histogram equalization, but I1 estimation represents a gap to actual yields. Our results provide a valuable database for future UAV field investigations of citrus fruit yield. Using flying platforms like UAVs can provide a step towards adopting this sort of model spanning ever greater regions at a cheap cost, with this system generating accurate results in this manner.

Strigolactones as promising biomolecule for oxidative stress management: A comprehensive review.

Strigolactones, which are a group of plant hormones, have emerged as promising biomolecules for effectively managing oxidative stress in plants. Oxidative stress occurs when the production of reactive oxygen species (ROS) exceeds the plant's ability to detoxify or scavenge these harmful molecules. An elevation in reactive oxygen species (ROS) levels often occurs in response to a range of stressors in plants. These stressors encompass both biotic factors, such as fungal, viral, or nematode attacks, as well as abiotic challenges like intense light exposure, drought, salinity, and pathogenic assaults. This ROS surge can ultimately lead to cellular harm and damage. One of the key ways in which strigolactones help mitigate oxidative stress is by stimulating the synthesis and accumulation of antioxidants. These antioxidants act as scavengers of ROS, neutralizing their harmful effects. Additionally, strigolactones also regulate stomatal closure, which reduces water loss and helps alleviate oxidative stress during conditions of drought stress or water deficiencies. By understanding and harnessing the capabilities of strigolactones, it becomes possible to enhance crop productivity and enable plants to withstand environmental stresses in the face of a changing climate. This comprehensive review provides an in-depth exploration of the various roles of strigolactones in plant growth, development, and response to various stresses, with a specific emphasis on their involvement in managing oxidative stress. Strigolactones also play a critical role in detoxifying ROS while regulating the expression of genes related to antioxidant defense pathways, striking a balance between ROS detoxification and production.

Alleviation of banded leaf and sheath blight disease incidence in maize by bacterial volatile organic compounds and molecular docking of targeted inhibitors in Rhizoctonia solani.

Rhizoctonia solani (RS) is a pathogenic fungus that affects maize (Zea mays L.) plants and causes banded leaf and sheath blight (BLSB) with severe consequences leading to significant economic losses. Contrarily, rhizobacteria produce numerous volatile organic compounds (VOCs) that help in devising the environment-friendly mechanism for promoting plant growth and stress alleviation without having physical contact with plants. In the present study, 15 rhizobacterial strains were tested for their antagonism against RS. The antagonistic potential of VOCs of the tested plant growth-promoting rhizobacteria (PGPR) strains ranged from 50% to 80% as compared to the control (without PGPR). Among these 15 strains, the maximum (80%) antagonistic activity was exhibited by Pseudomonas pseudoalcaligenes SRM-16. Thus, the potential of VOCs produced by P. pseudoalcaligenes SRM-16 to alleviate the BLSB disease in maize was evaluated. A pot experiment was conducted under greenhouse conditions to observe the effect of VOCs on disease resistance of BLSB-infected seedlings. Overall, maize seedlings exposed to VOCs showed a significant increase in disease resistance as indicated by a reduced disease score than that of unexposed infected plants. The VOCs-exposed maize exhibited lower (11.6%) disease incidence compared to the non-inoculated maize (14.1%). Moreover, plants exposed to VOCs displayed visible improvements in biomass, photosynthetic pigments, osmoregulation, and plant antioxidant and defense enzyme activities compared to the healthy but unexposed seedlings. Simultaneous application of RS and VOCs enhanced superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), phenylalanine ammonia lyase (PAL), ascorbate peroxidase (APX), and polyphenol oxidase (PPO) activities by 96.7%, 266.6%, 313.7%, 246.6%, 307%, and 149.7%, respectively, in the roots and by 81.6%, 246.4%, 269.5%, 269.6%, 329%, and 137.6%, respectively, in the shoots, relative to those of the control plants. The binding affinity of the VOCs (2-pentylfuran, 2,3-butanediol, and dimethyl disulfide) with CRZ1 and S9 protein receptors of RS was assessed by deploying in silico methods. Overall, 2-pentylfuran exhibited a binding affinity with both the selected receptors of RS, while 2,3-butanediol and dimethyl disulfide were able to bind S9 protein only. Hence, it can be deduced that S9 protein receptors are more likely the target RS receptors of bacterial VOCs to inhibit the proliferation of RS.

Automatic Evaluation of Soybean Seed Traits Using RGB Image Data and a Python Algorithm.

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.

Plant Growth Hormones and Micro-Tuberization in Breaking the Seed Dormancy of Bunium persicum (Boiss.) Fedts.

Bunium persicum is a valuable medicinal plant with limited production but high market demand. It thrives predominantly in high-altitude regions. The main challenges hindering its widespread cultivation are seed dormancy and a lengthy seed-to-seed cycle, making its large-scale cultivation difficult. Six genotypes of Bunium persicum were collected from different altitudes to evaluate its germination behavior and seed dormancy. The study was conducted during 2020-23 and comprised three experiments (viz., seed germination under an open field, controlled conditions, and micro-tuberization). Under open field conditions, germination percent was genotype dependent, and the highest germination percentage, root length, and shoot length were recorded in Shalimar Kalazeera-1. Germination behavior assessment of the Bunium persicum revealed that treatment T9 (GA3 (25 ppm) + TDZ (9 µM/L)) is effective in breaking the dormancy of Bunium persicum as well as in obtaining a higher germination percent for early development of the tubers. Similarly, with regard to the effect of temperature and moisture conditions, stratification under moist chilling conditions showed effectiveness in breaking seed dormancy as the germination percentage in stratified seeds was at par with the most efficient growth hormone. With regard to the in vitro micro-propagation, direct regeneration showed multiple shoot primordia at the base of the tubers without intervening callus phase from the MS medium supplemented with BA (22.2 µM) and NAA (13.95 µM) 4 weeks after sub-culturing. Similarly, medium supplemented with JA (8.0 mg/L) and BA (22.2 µM) produced well-organized somatic embryos with shiny surfaces, which appeared at the swelled basal portion of apical stems. Further, the combination of JA (6.0 mg/L) and BA (22.2 M) was effective in developing the micro-tubers and also enhanced the weight and length of Bunium persicum micro-tubers.

A novel 3D insect detection and monitoring system in plants based on deep learning.

Insects can have a significant impact on biodiversity, ecology, and the economy. Certain insects, such as aphids, caterpillars, and beetles, feed on plant tissues, including leaves, stems, and fruits. They can cause direct damage by chewing on the plant parts, resulting in holes, defoliation, or stunted growth. This can weaken the plant and affect its overall health and productivity. Therefore, the aim of this research was to develop a model system that can identify insects and track their behavior, movement, size, and habits. We successfully built a 3D monitoring system that can track insects over time, facilitating the exploration of their habits and interactions with plants and crops. This technique can assist researchers in comprehending insect behavior and ecology, and it can be beneficial for further research in these areas.

Genome-Wide Identification and Expression Analysis of MTP (Metal Ion Transport Proteins) Genes in the Common Bean.

MTP/CDF carriers, called metal ion transport proteins, act as substrates for the transmission of micronutrients such as iron (Fe), zinc (Zn), and manganese (Mn) to membrane carriers in plants. In this study, genome-wide analysis of the MTP gene family in the common bean genome, expression analysis of the PvMTP4, PvMTP5, and PvMTP12 genes after Fe and Zn treatments, and the effects of Fe and Zn applications on iron and zinc content were investigated. This study used common bean genotypes assumed to have high or low Fe and Zn accumulation ability. PvMTP genes were defined as containing conserved catalytic domains with molecular weights and protein lengths ranging from 41.35 to 91.05 kDa and from 369 to 813 amino acids (aa), respectively. As a result of the phylogenetic analysis, three main clusters containing seven subgroups were formed. In this study, the first characterization of the MTP gene family of beans was performed, and the responses of three different PvMTP genes in the Zn-CDF group to Fe and Zn applications were revealed. The obtained findings are thought to constitute pioneering resources for future research on common bean biofortification studies, plant breeding related to Fe and Zn, and the functional characterization of the MTP gene family.

Heavy Metal Induced Oxidative Stress Mitigation and ROS Scavenging in Plants.

Although trace elements are essential for life, environmental contamination due to metal accumulation and overuse in various sectors, such as healthcare, agriculture, industry, and cosmetics, poses significant health concerns. Exposure of plants to heavy metals leads to the overproduction of reactive oxygen species (ROS) due to their ability to change mitochondrial membrane permeability and restrict the action of ROS clearance enzymes in the cellular antioxidant system. The interaction of ROS with cellular membranes, heavy-metal-induced interactions directly or indirectly with different macromolecules, and signaling pathways leads to the accumulation of environmental pollutants and oxidative stress in exposed organisms. The heavy metal-ROS-cell signaling axis affects various pathological processes such as ATP depletion, excess ROS production, mitochondrial respiratory chain damage, decoupling of oxidative phosphorylation, and mitochondrial death. This review focuses on discussing the toxic effects of different heavy metals on plants, with particular emphasis on oxidative stress, its consequences, and mitigation strategies.

Genetic diversity for drought tolerance in the native forage grass Trichloris crinita and possible morpho-physiological mechanisms involved.

Introduction: The use of drought tolerant genotypes is one of the main strategies proposed for coping with the negative effects of global warming in dry lands. Trichloris crinita is a native forage grass occupying extensive arid and semi-arid regions in the American continent, and used for range grazing and revegetation of degraded lands. Methods: To identify drought-tolerant genotypes and possible underlying physiological mechanisms, this study investigated drought tolerance in 21 genetically diverse T. crinita genotypes under natural field conditions. The accessions were grown under irrigated (control) and drought conditions for 84 days after initiation of the drought treatment (DAIDT), which coincided with flowering initiation. Various morpho-physiological traits were monitored, including total-, foliage-, and root biomass yield, dry matter partitioning to individual plant organs (roots, leaves, stems, and panicles), total leaf area, chlorophyll content, photochemical efficiency of photosystem II, stomatal conductance, and number of panicles per plant. Results and discussion: Broad and significant variation (p<0.001) was found among the accessions for all the traits. Three highly tolerant and three very sensitive accessions were identified as the most contrasting materials, and their responses to drought stress were confirmed over two years of experiments. Under prolonged drought conditions (84 DAIDT), the tolerant accessions were generally more productive than the rest for all the biomass yield components analyzed, and this was associated with a postponed and more attenuated decrease in variables related to the plant photosynthetic activity, such as stomatal conductance, chlorophyll content, and photochemical efficiency. In contrast to previous findings, our data indicate no direct relationship between drought tolerance and the level of aridity in the accessions natural habitats, but rather suggest genetic heterogeneity and ample variation for drought tolerance in T. crinita natural populations derived from a particular location or environment. Also, having low total and forageable biomass yield, or increased biomass allocation to the roots (i.e., lower foliage/root ratio), under optimal water availability, were not associated with greater drought tolerance. The drought-tolerant accessions identified are of value for future genetic research and breeding programs, and as forage for range grazing and revegetation in arid regions.

Identification of new cold tolerant Zoysia grass species using high-resolution RGB and multi-spectral imaging.

Zoysia grass (Zoysia spp.) is the most widely used warm-season turf grass in Korea due to its durability and resistance to environmental stresses. To develop new longer-period greenness cultivars, it is essential to screen germplasm which maintains the greenness at a lower temperature. Conventional methods are time-consuming, laborious, and subjective. Therefore, in this study, we demonstrate an objective and efficient method to screen maintaining longer greenness germplasm using RGB and multispectral images. From August to December, time-series data were acquired and we calculated green cover percentage (GCP), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Soil-adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) values of germplasm from RGB and multispectral images by applying vegetation indexs. The result showed significant differences in GCP, NDVI, NDRE, SAVI, and EVI among germplasm (p < 0.05). The GCP, which evaluated the quantity of greenness by counting pixels of the green area from RGB images, exhibited maintenance of greenness over 90% for August and September but, sharply decrease from October. The study found significant differences in GCP and NDVI among germplasm. san208 exhibiting over 90% GCP and high NDVI values during 153 days. In addition, we also conducted assessments using various vegetation indexes, namely NDRE, SAVI, and EVI. san208 exhibited NDRE levels exceeding 3% throughout this period. As for SAVI, it initially started at approximately 38% and gradually decreased to around 4% over the course of these days. Furthermore, for the month of August, it recorded approximately 6%, but experienced a decline from about 9% to 1% between September and October. The complementary use of both indicators could be an efficient method for objectively assessing the greenness of turf both quantitatively and qualitatively.

Comparison of Various Drought Resistance Traits in Soybean (Glycine max L.) Based on Image Analysis for Precision Agriculture.

Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories-area, boundary, and color-and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.

Recent advancements in the breeding of sorghum crop: current status and future strategies for marker-assisted breeding.

Sorghum is emerging as a model crop for functional genetics and genomics of tropical grasses with abundant uses, including food, feed, and fuel, among others. It is currently the fifth most significant primary cereal crop. Crops are subjected to various biotic and abiotic stresses, which negatively impact on agricultural production. Developing high-yielding, disease-resistant, and climate-resilient cultivars can be achieved through marker-assisted breeding. Such selection has considerably reduced the time to market new crop varieties adapted to challenging conditions. In the recent years, extensive knowledge was gained about genetic markers. We are providing an overview of current advances in sorghum breeding initiatives, with a special focus on early breeders who may not be familiar with DNA markers. Advancements in molecular plant breeding, genetics, genomics selection, and genome editing have contributed to a thorough understanding of DNA markers, provided various proofs of the genetic variety accessible in crop plants, and have substantially enhanced plant breeding technologies. Marker-assisted selection has accelerated and precised the plant breeding process, empowering plant breeders all around the world.

A novel study on bean common mosaic virus accumulation shows disease resistance at the initial stage of infection in Phaseolus vulgaris.

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.

Heterogeneity Assessment of Kenaf Breeding Field through Spatial Dependence Analysis on Crop Growth Status Map Derived by Unmanned Aerial Vehicle.

The investigation of quantitative phenotypic traits resulting from the interaction between targeted genotypic traits and environmental factors is essential for breeding selection. Therefore, plot-wise controlled environmental factors must be invariable for accurate identification of phenotypes. However, the assumption of homogeneous variables within the open-field is not always accepted, and requires a spatial dependence analysis to determine whether site-specific environmental factors exist. In this study, spatial dependence within the kenaf breeding field was assessed in a geo-tagged height map derived from an unmanned aerial vehicle (UAV). Local indicators of spatial autocorrelation (LISA) were applied to the height map using Geoda software, and the LISA map was generated in order to recognize the existence of kenaf height status clusters. The spatial dependence of the breeding field used in this study appeared in a specific region. The cluster pattern was similar to the terrain elevation pattern of this field and highly correlated with drainage capacity. The cluster pattern could be utilized to design random blocks based on regions that have similar spatial dependence. We confirmed the potential of spatial dependence analysis on a crop growth status map, derived by UAV, for breeding strategy design with a tight budget.

Tissue and Time Optimization for Real-Time Detection of Apple Mosaic Virus and Apple Necrotic Mosaic Virus Associated with Mosaic Disease of Apple (Malus domestica).

Besides apple mosaic virus (ApMV), apple necrotic mosaic virus (ApNMV) has also been found to be associated with apple mosaic disease. Both viruses are unevenly distributed throughout the plant and their titer decreases variably with high temperatures, hence requiring proper tissue and time for early and real-time detection within plants. The present study was carried out to understand the distribution and titer of ApMV and ApNMV in apple trees from different plant parts (spatial) during different seasons (temporal) for the optimization of tissue and time for their timely detection. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) was carried out to detect and quantify both viruses in the various plant parts of apple trees during different seasons. Depending on the availability of tissue, both ApMV and ApNMV were detected in all the plant parts during the spring season using RT-PCR. During the summer, both viruses were detected only in seeds and fruits, whereas they were detected in leaves and pedicel during the autumn season. The RT-qPCR results showed that during the spring, the ApMV and ApNMV expression was higher in leaves, whereas in the summer and autumn, the titer was mostly detected in seeds and leaves, respectively. The leaves in the spring and autumn seasons and the seeds in the summer season can be used as detection tissues through RT-PCR for early and rapid detection of ApMV and ApNMV. This study was validated on 7 cultivars of apples infected with both viruses. This will help to accurately sample and index the planting material well ahead of time, which will aid in the production of virus-free, quality planting material.

Evaluation of Soybean Wildfire Prediction via Hyperspectral Imaging.

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.

Implication of high variance in germplasm characteristics.

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.