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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. [ABSTRACT FROM AUTHOR]

Gamma irradiation is used in plant mutation breeding. This method was also applied to turfgrass breeding. Hallagreen 07, 09, and 10 are irradiated and confirmed by image analysis that objective basis on canopy cover. The canopy cover means it has drought tolerance than relatively smaller canopy cover turfgrass. Hallagreen 07 represents a low canopy cover, Hallagreen 09 is a medium, and Hallagreen 10 is excellent at the untreated control. However, after the radiation treatment of 30 entries per cultivar, the trait improved than the control appeared at Hallagreen 07, which showed low canopy cover. In contrast, Hallagreen 10, the excellent cultivar, has no entries at improving the trait by irradiation. So, by irradiation, we could find better drought tolerance entries than control. In this study, we found the potential of radiation treatment to turfgrass to improve drought tolerance, and the efficiency of finding trait improvement is effective at not an excellent cultivar. And despite the canopy cover analysis by the vertical image being low tech, it is an efficient solution for screening bottlenecks. [ABSTRACT FROM AUTHOR]

Polyunsaturated fatty acids usually undergo lipid peroxidation induced by reactive oxygen species (ROS). Calcium-independent cellular phospholipase A 2 (iPLA 2 ) can maintain fatty acid compositions in phospholipids depending on physiological conditions. An insect iPLA 2 (Pi-iPLA 2 ) was predicted from the transciptome of the Indianmeal moth, Plodia interpunctella . It encodes 835 amino acids. It possesses five ankyrin repeats in the N terminal and patatin lipase domain in the C terminal. Pi-iPLA 2 was expressed in all developmental stages of the Indianmeal moth. In the larval stage, it was expressed in all tissues tested. RNA interference (RNAi) specific to Pi-iPLA 2 was performed using specific double-stranded RNA (dsRNA). It resulted in almost 70% of reduction in gene expression. Under such RNAi condition, P. interpunctella exhibited significant accumulation of lipid peroxidation based on the amount of malondialdehyde. RNAi of Pi-PLA 2 expression also impaired cellular immune response of P. interpunctella . Chlorine dioxide (ClO 2 ), an insecticidal agent by generating ROS, increased lipid peroxidation in a dose-dependent manner. However, the addition of vitamin E (an antioxidant) reduced the formation of lipid peroxidation. ClO 2 treatment significantly reduced expression of Pi-iPLA 2 but increased lipid peroxidation in larval fat body of P. interpunctella . Furthermore, larvae treated with dsRNA specific to Pi-iPLA 2 were significantly susceptible to ClO 2 treatment. These results suggest that Pi-iPLA 2 plays a crucial role in repairing damaged fatty acids from phospholipids. Our results also suggest that ClO 2 can elevate lipid peroxidation through inhibiting Pi-iPLA 2 expression in addition to direct ROS production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Common buckwheat is widely cultivated worldwide. Due to its bioactive compounds, there are vigorous studies on protein and metabolism. However, there are not many studies using genetic approaches. Especially researches on genetic diversity in common buckwheat germplasm are rare, although it is crucial for breeding better cultivars. Thus, the current study examined the genetic diversity of the randomly selected common buckwheat germplasm found in the Republic of Korea. The results show that there are not many genetic variances among those wild common buckwheat germplasm that were used in the current study. Although it needs to test more number of germplasm, it seems that more diversity is required to be able to breed cultivars for the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-throughput phenotyping (HTP) enables breeders and researchers to have massive data sets accurately and objectively. It could be applied to plant breeding for screening stress tolerance and biodiversity among wild species in the gene bank, which can be a breakthrough in the phenotyping bottleneck. However, there are many factors to be considered. Thus, this study is designed to show an example of phenotyping traits using yield and image data in citrus using the Normalized Difference Vegetation Index (NDVI) and Red, Green, and Blue (RGB) images. The results using image analysis showed that R2 in linear regression ranged from 0.79 to 0.91, depending on the methods which were used in the current study. However, the results from NDVI were proven to be false, unlike those of RGB images. This means that researchers and breeders must be very cautious when dealing with new technologies to avoid being misled to the wrong conclusion when they try to associate this data with genomic data. [ABSTRACT FROM AUTHOR]

Global warming is not an expectation but a reality in the "oreums" (common local name for rising, small defunct volcanoes on Jeju Island, Republic of Korea). The oreums exhibit wide biodiversity. However, their ecology is threatened by its associated climate change and their ecological changes have rarely been monitored or recorded. We used three years of Sentinel-2 image data to generate a normalized difference vegetation index (NDVI) map of the Geum-oreum area. We found that the NDVI was highly associated with temperature, implying that Sentinel-2 images could be utilized to monitor the temperature variation in the oreums to assist in planning and preparation to conserve their ecosystems before they are jeopardized. The results indicated that the NDVI maps derived from Sentinel-2 images were highly associated with temperature in Geum-oreum. We expect this method could be applied in other regions to detect temperature variation for ecological management planning in large areas (such as forests). [ABSTRACT FROM AUTHOR]