Search

Genetic resources are the cornerstone of our food supply and play a pivotal role in developing new crop varieties that ensure sustainable agricultural production amid the challenges of climate change [...]

In Korea, greenhouses are traditionally used for crop cultivation in the winter. However, due to diverse consumer demands, climate change, and advancements in agricultural technology, more farms are aiming for year-round production. Nonetheless, summer cropping poses challenges such as high temperatures, humidity from the monsoon season, and low light conditions, which make it difficult to grow crops. Therefore, this study aimed to determine the best planting time for summer tomato cultivation in a Korean semi-closed greenhouse that can be both air-conditioned and heated. The experiment was conducted in the Advanced Digital Greenhouse, built by the National Institute of Agricultural Sciences. The tomato seedlings were planted in April, May, and June 2022. Growth parameters such as stem diameter, flowering position, stem growth rate, and leaf shape index were measured, and harvesting was carried out once or twice weekly per treatment from 65 days to 265 days after planting. The light use efficiency and yield per unit area at each planting time was measured. Tomatoes planted in April showed a maximum of 42.9% higher light use efficiency for fruit production and a maximum of 33.3% higher yield. Furthermore, the growth form of the crops was closest to the reproductive growth type. Therefore, among April, May, and June, April is considered the most suitable planting time for summer cultivation, which is expected to contribute to reducing labor costs due to decreased workload and increasing farm income through increased yields. Future research should explore optimizing greenhouse microclimates and developing crop varieties tailored for summer cultivation to further enhance productivity and sustainability in year-round agricultural practices.

Vespa velutina is an ecosystem disruptor that causes annual damage worth KRW 170 billion (USD 137 million) to the South Korean beekeeping industry. Due to its strong fertility and high-lying habitat, it is difficult to control. This study aimed to develop a system for the control of V. velutina nests using drones for detection and tracking the real-time location of the nests. Vespa velutina nest image data were acquired in Buan-gun and Wanju-gun (Jeollabuk-do), and artificial intelligence learning was conducted using YOLO-v5. Drone image resolutions of 640, 1280, 1920, and 3840 pixels were compared and analyzed. The 3840-pixel resolution model was selected, as it had no false detections for the verification image and showed the best detection performance, with a precision of 100%, recall of 92.5%, accuracy of 99.7%, and an F1 score of 96.1%. A computer (Jetson Xavier), real-time kinematics module, long-term evolution modem, and camera were installed on the drone to acquire real-time location data and images. Vespa velutina nest detection and location data were delivered to the user via artificial intelligence analysis. Utilizing a drone flight speed of 1 m/s and maintaining an altitude of 25 m, flight experiments were conducted near Gyeongcheon-myeon, Wanju-gun, Jeollabuk-do. A total of four V. velutina nests were successfully located. Further research is needed on the detection accuracy of artificial intelligence in relation to objects that require altitude-dependent variations in drone-assisted exploration. Moreover, the potential applicability of these research findings to diverse domains is of interest.

Honeybees pollinate over 75% of the total food resources produced annually, and they produce valuable hive products, such as bee pollen, propolis, and royal jelly. However, species such as the Asian hornet (Vespa velutina) feed on more than 85% of honeybees, causing a decline in their population and considerable damage to beekeepers in Korea. To prevent damage to honeybees, a portable real-time monitoring system was developed that detects V. velutina individuals and notifies users of their presence. This system was designed with a focus on portability and ease of installation, as V. velutina can be found in various areas of apiary sites. To detect V. velutina, an improved convolutional neural network YOLOv5s was trained on 1960 high-resolution (3840×2160) image data. At the confidence threshold of ≥0.600 and intersection over the union of ≥0.500, the performance of the system in terms of detection accuracy, precision, recall, F1 score, and mean average precision was high. A distance-based performance comparison showed that the system was able to detect V. velutina individuals while monitoring three beehives. During a field test of monitoring three beehives, the system could detect 83.3% of V. velutina during their hunting activities and send alarms to registered mobile application users.

Temperate japonica rice varieties exhibit wide variation in the phenotypes of several important agronomic traits, including disease resistance, pre-harvest sprouting resistance, plant architecture, and grain quality, indicating the presence of genes contributing to favorable agronomic traits. However, gene mapping and molecular breeding has been hampered as a result of the low genetic diversity among cultivars and scarcity of polymorphic DNA markers. Single nucleotide polymorphism (SNP)-based kompetitive allele-specific PCR (KASP) markers allow high-throughput genotyping for marker-assisted selection and quantitative trait loci (QTL) mapping within closely related populations. Previously, we identified 740,566 SNPs and developed 771 KASP markers for Korean temperate japonica rice varieties. However, additional markers were needed to provide sufficient genome coverage to support breeding programs. In this study, the 740,566 SNPs were categorized according to their predicted impacts on gene function. The high-impact, moderate-impact, modifier, and low-impact groups contained 703 (0.1%), 20,179 (2.7%), 699,866 (94.5%), and 19,818 (2.7%) SNPs, respectively. A subset of 357 SNPs from the high-impact group was selected for initial KASP marker development, resulting in 283 polymorphic KASP markers. After incorporation of the 283 markers with the 771 existing markers in a physical map, additional markers were developed to fill genomic regions with large gaps between markers, and 171 polymorphic KASP markers were successfully developed from 284 SNPs. Overall, a set of 1225 KASP markers was produced. The markers were evenly distributed across the rice genome, with average marker density of 3.3 KASP markers per Mbp. The 1225 KASP markers will facilitate QTL/gene mapping and marker-assisted selection in temperate japonica rice breeding programs.

Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.

Early seedling establishment in rice (Oryza sativa L.), which is measured by primary/secondary tiller, shoot length, biomass, root-related traits, and leaf area index, is an important trait because it helps to compete for light, air, and water for better tolerating various abiotic stresses. Consequently, it can affect the yield. However, there are not many research studies on this subject. Furthermore, previous studies have only measured the target traits once. However, this does not reflect the variation of growth rate during the seedling stage. Thus, two data points, two weeks and four weeks after planting, were used in the current study. As a result, two QTL regions were detected for the growth differences via plant height and green area (reflecting tillering). We expect that these results can be utilized by breeders to evaluate and select vigorous seedlings for their breeding programs.