• Korean journal of applied entomology
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• v.50 no.3
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• pp.247-251
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• 2011

Wood products are sometimes infested with cossonid weevils. A cossonid weevil, Hexarthrum brevicorne Wollaston has been found on wood boards used for printing books in the Jeonju Confucian temple and school (2004), a wood cabinet in the Museum of Milyang (2007), a wood wardrobe in the Museum of Seoul (2008) and on wood boards used for printing Buddhist scriptures in the temple of Suncheon (2008). Wood utensils for living in the Museum of Seoul were found to be infested with another cossonid weevil, Rhyncolus sculpturatus (Waltl) in 2008. To protect the cultural property from insect pests in the field of conservation science, more comprehensive insect pest management (IPM) programs are required.

• International Journal of Industrial Entomology and Biomaterials
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• v.38 no.2
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• pp.38-50
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• 2019

We investigated the developmental characteristics and life cycle of the lawn cutworm, Spodoptera depravata (Lepidoptera: Noctuidae), which is one of the most important pests causing economic damage in grass production. For larval culture, we provided the zoysiagrass at $25^{\circ}C$ and $60{\pm}5%$ humidity. The durations of the developmental stages were as follows: $4.11{\pm}0.19$ days for eggs, $25.17{\pm}3.02$ for larvae, $8.80{\pm}0.28$ for pupae, and $7.57{\pm}0.95$ for adults. We grew the larvae to the 7th instar stage, unlike previous studies, in which it was assumed that the 6th instar was the final age. There was a significant positive correlation between the body length and head capsule width of each instar larvae. In terms of morphology, the eggs changed from light green immediately following oviposition to black as they developed, and the grass-fed larvae changed from light yellow immediately after hatching to green as development continued. We observed a pattern of black spots at regular intervals on the dorsal sides of the abdomens of the final instar larvae. Furthermore, we detected two notable designs on the dorsal side of the front of the head. The pupal colors changed from light brown and green immediately after pupation, to dark brown as the pupal cuticle hardened. The wingspans of the adults were similar in both sexes. However, the forewings of the males had obvious outer lines and eyespots with dark gray-brown backgrounds, whereas the corresponding features on the female forewings were less obvious. The oviposition preperiod was 2.11 days, the oviposition period was 4.2 days, the average fecundity per female was approximately 341 eggs, and the hatching rate was approximately 76.1%.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.324-332
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• 2018

BACKGROUND: Insects are ectothermic organisms in terrestrial ecosystems and play various roles such as controlling plant biomass and maintaining species diversity. Because insects are ectothermic, their physiological responses are very sensitive to environmental temperature which determines survival and distribution of insect population and that affects climate change. This study aimed to identification of genes contributing to fitness under high temperature. METHODS AND RESULTS: To identify genes contributing to fitness under high temperature, the transcriptomes of fat body in Plutella xyostella larva have been analyzed via next generation sequencing. From the fat body transcriptomes, structure-related proteins, heat shock proteins, antioxidant enzymes and detoxification proteins were identified. Genes encoding proteins such as structural proteins (cuticular proteins, chitin synthase and actin), stress-related protein (cytochrome P450), heat shock protein and antioxidant enzyme (catalase) were up-regulated at high temperature. In contrast expression of glutathione S transferase was down-regulated. CONCLUSION: Identifications of temperature-specific up- or down-regulated genes can be useful for detecting temperature adaptation and understanding physiological responses in insect pests.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.295-298
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• 2014

In recently, a climatic change(such as subtropical climate and frequent unusual high temperature) and the open-trade policies of agricultural & livestock products are increasing the outbreak risk of highly pathogenic avian influenza(HPAI) and foot and mouth disease(FMD), and accordingly the socio-economic damage and impacts are also increasing due to the cases such as damage from the last 5 times of FMD outbreak(3,800 billion won), from 10 years public control cost of Pine Wilt Disease (PWD)(238.3 billion won), and from the increased invasive pests of exotic plant like isoptera. Therefore, the establishment of new operation strategy of IoT(Internet of Things) based satellite early warning system(SEWS) for plants and animals as a subsystem of national disaster response and management system is being required, where the forensic technology & measures should be applied as a government policy to estimate the post compensation and to carry out the legal responsibility.

• Korean Journal of Environmental Biology
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• v.39 no.3
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• pp.336-343
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• 2021

Fumigation of fruits and vegetables during quarantine and pre-shipment(QPS) treatment should be effective with a shorter fumigation time to minimize phytotoxicity. In this research study, a shorter fumigation time, 2 hours exposure which is shorter than that of the current commercial fumigation procedures using a lower dose of ethyl formate (EF) mixed with phosphine (PH₃) on strawberry was investigated. The reciprocal effect between EF and PH₃ against nymphs and adult Myzus persicae (Sulzer) and Tetranychus urticae (Koch) was evaluated. In addition, L(Ct)₅₀ and L(Ct)₉₉ of EF only and EF mixed with PH₃ were analyzed at 5℃ and 20℃. The synergistic ratio (SR) of L(Ct)₅₀ and L(Ct)₉₉ for the nymph and adult stages of M. persicae were >1.0, which indicated a synergistic effect between EF and PH₃. However, the SR values of L(Ct)₅₀ and L(Ct)₉₉ of the nymph and adult stages of T. urticae were ≤1.0 indicating that there was no synergistic effect between the two fumigants against T. urticae. Our results showed that the reciprocal effect between EF and PH₃ has different effects on M. persicae and T. urticae. This could be attributed to the biological and physical differences between the class Arachnida and Insecta. The synergistic effect between EF and PH₃ against M. persicae within a shorter exposure period and without phytotoxicity on fruits and vegetables will significantly benefit the horticultural industry.

• Journal of Korean Society for Quality Management
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• v.48 no.4
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• pp.581-596
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• 2020

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying.

• Korean Journal of Environmental Agriculture
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• v.40 no.1
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• pp.1-12
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• 2021

BACKGROUND: G protein-coupled receptors (GPCRs) are widely distributed in various organisms. Insect GPCRs shown as in vertebrate GPCRs are membrane receptors that coordinate or involve in various physiological processes such as learning/memory, development, locomotion, circadian rhythm, reproduction, etc. This study aimed to identify GPCRs expressed in fat body and compare the expression pattern of GPCRs in different temperature conditions. METHODS AND RESULTS: To identify GPCRs genes and compare their expression in different temperature conditions, total RNAs of fat body in Plutella xylostella larva were extracted and the transcriptomes have been analyzed via next generation sequencing method. From the fat body transcriptomes, genes that belong to GPCR Family A, B, and F were identified such as opsin, gonadotropin-releasing hormone receptor, neuropeptide F (NPF) receptor, muthuselah (Mth), diuretic hormone receptor, frizzled, etc. Under low temperature, expressions of GPCRs such as C-C chemokine receptor (CCR), opsin, prolactin-releasing peptide receptor, substance K receptor, Mth-like receptor, diuretic hormone receptor, frizzled and stan were higher than those at 25℃. They are involved in immunity, feeding, movement, odorant recognition, diuresis, and development. In contrast to the control (25℃), at high temperature GPCRs including CCR, gonadotropin-releasing hormone receptor, moody, NPF receptor, neuropeptide B1 receptor, frizzled and stan revealed higher expression whose biological functions are related to immunity, blood-brain barrier formation, feeding, learning, and reproduction. CONCLUSION: Transcriptome of fat body can provide understanding the pools of GPCRs. Identifications of fat body GPCRs may contribute to develop new targets for the control of insect pests.

• The Plant Pathology Journal
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• v.38 no.6
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• pp.603-615
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• 2022

Soybean (Glycine max (L) Merr.) provides plant-derived proteins, soy vegetable oils, and various beneficial metabolites to humans and livestock. The importance of soybean is highly underlined, especially when carbon-negative sustainable agriculture is noticeable. However, many diseases by pests and pathogens threaten sustainable soybean production. Therefore, understanding molecular interaction between diverse cultivated varieties and pathogens is essential to developing disease-resistant soybean plants. Here, we established a pathosystem of the Korean domestic cultivar Kwangan against Pseudomonas syringae pv. syringae B728a. This bacterial strain caused apparent disease symptoms and grew well in trifoliate leaves of soybean plants. To examine the disease susceptibility of the cultivar, we analyzed transcriptional changes in soybean leaves on day 5 after P. syringae pv. syringae B728a infection. About 8,900 and 7,780 differentially expressed genes (DEGs) were identified in this study, and significant proportions of DEGs were engaged in various primary and secondary metabolisms. On the other hand, soybean orthologs to well-known plant immune-related genes, especially in plant hormone signal transduction, mitogen-activated protein kinase signaling, and plant-pathogen interaction, were mainly reduced in transcript levels at 5 days post inoculation. These findings present the feature of the compatible interaction between cultivar Kwangan and P. syringae pv. syringae B728a, as a hemibiotroph, at the late infection phase. Collectively, we propose that P. syringae pv. syringae B728a successfully inhibits plant immune response in susceptible plants and deregulates host metabolic processes for their colonization and proliferation, whereas host plants employ diverse metabolites to protect themselves against infection with the hemibiotrophic pathogen at the late infection phase.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.638-651
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• 2023

The objective of this study was to quantitatively estimate the level of grazing area damage in outdoor free-range pig production using a Unmanned Aerial Vehicles (UAV) with an RGB image sensor. Ten corn field images were captured by a UAV over approximately two weeks, during which gestating sows were allowed to graze freely on the corn field measuring 100 × 50 m². The images were corrected to a bird's-eye view, and then divided into 32 segments and sequentially inputted into the YOLOv4 detector to detect the corn images according to their condition. The 43 raw training images selected randomly out of 320 segmented images were flipped to create 86 images, and then these images were further augmented by rotating them in 5-degree increments to create a total of 6,192 images. The increased 6,192 images are further augmented by applying three random color transformations to each image, resulting in 24,768 datasets. The occupancy rate of corn in the field was estimated efficiently using You Only Look Once (YOLO). As of the first day of observation (day 2), it was evident that almost all the corn had disappeared by the ninth day. When grazing 20 sows in a 50 × 100 m² cornfield (250 m²/sow), it appears that the animals should be rotated to other grazing areas to protect the cover crop after at least five days. In agricultural technology, most of the research using machine and deep learning is related to the detection of fruits and pests, and research on other application fields is needed. In addition, large-scale image data collected by experts in the field are required as training data to apply deep learning. If the data required for deep learning is insufficient, a large number of data augmentation is required.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.256-264
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• 2023

Smart farm technology using IoT is one of the technologies that can increase productivity and improve the quality of agricultural products in agriculture, which is facing difficulties due to the decline in rural population, lack of rural manpower due to aging, and increase in diseases and pests due to climate change. . Smart farms using existing IoT simply monitor farms, implement smart plant growers, and have automatic greenhouse opening and closing systems. This paper implements a smart farm system based on MQTT, an industry standard protocol for the Internet of Things, and Node-RED, a representative development middleware for the Internet of Things. First, data is extracted from Arduino sensors, and data is collected and transmitted from IoT devices using the MQTT protocol. Then, Node-RED is used to process MQTT messages and store the sensing data in real time in MongoDB, a representative NoSQL, to store the data. Through this smart farm system, farm managers can use a computer or mobile phone to check sensing information on the smart farm in real time, anytime, anywhere, without restrictions on time and space.