• Korean Journal of Weed Science
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• v.30 no.3
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• pp.199-205
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• 2010

This study was conducted to investigate the phytotoxic effects of 19 essential oils on seed germination and initial growth of rapeseed (Brassica napus L.). We found that anise, cinnamon, citronella, clove, geranium, lemongrass, mustard and pine oils completely inhibited germination of rapeseed at $100{\times}$ dilute solution. Based on the inhibition rates of rapeseed emergence and initial growth, three essential oils (cinnamon, clove, and geranium) were selected as potential bio-herbicides. Under pre-emergence applications of cinnamon, clove, and geranium oils at 90 kg ai $ha^{-1}$, rates of rapeseed emergence were 7.1, 25.0, and 3.6% and its initial growth were 22.0, 9.9 and 11.0%, respectively.

• Korean Journal of Weed Science
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• v.17 no.1
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• pp.31-43
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• 1997

This study was carried out to investigate classfication of weedy rice (Oryza sativa) based on isozymes esterase and peroxidase, growth and developmental difference of weedy rices and rices grown under dry and water condition, and weedy rice control and tolerant difference of weedy rices in various herbicides using weedy rices collected from thirteen strains of Chonnam, one Chonbuk, two Kyeongki and two rice cultivars. 1. The collected weedy rices were classified into three groups based on isozyme esterase and peroxidase using polyacrylamide gel electrophoresis(PAGE) method. The classified groups were not same each other. 2. Plant height was taller in collected weedy rices than rice cultivars at 18 days after seeding under dry and water conditions, but number of leaves, shoot fresh weight, root fresh weight and root length were not significantly different between collected weedy rices and rice cultivars. In addition, growths of collected weedy rices were greater in dry- than water-condition. 3. After thiobencarb(S-4-chlorobenzyl diethythiocarbamate), molinate(S-ethyl hexahydro-1H-azepine-1-carbothioate) and oxadiazon(5-tert-butyl-3(2,4-dichloro-5-isopropoxyphenyl)-1,3,4-oxadiazol-2-one) were applied at 6 days before seeding, the weedy rices controlled 100% by thiobencarb at 2.1kg ai/ha and 024kg ai/ha oxadiazon treatment but controlled 26% to 67% by molinate at 6.5kg ai/ha. Rice due to the herbicides was injured severely(25% to 100%) in flood condition at time of rice seeding after oxadiazon at 0.48kg ai/ha and 2.1kg ai/ha thiobencarb application, except for molinate which injured rice slightly(4% to 13%) in drain condition. The collected weedy rices to all experimented herbicides showed slight intraspecific variations. The intraspecific variations of weedy rices decreased in the order of thiobencarb>molinate>oxadiazon.

• Korean Journal of Poultry Science
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• v.50 no.1
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• pp.41-50
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• 2023

Over the last decade, avian influenza (AI) has been considered an emerging disease that would become the next pandemic, particularly in countries like South Korea, with continuous animal outbreaks. In this situation, risk assessment is highly needed to prevent and prepare for human infection with AI. Thus, we developed the risk assessment matrix for a high-risk area of human infection with AI in South Korea based on the notion that risk is the multiplication of hazards with vulnerability. This matrix consisted of highly pathogenic avian influenza (HPAI) in poultry farms and the number of poultry-associated production facilities assumed as hazards of avian influenza and vulnerability, respectively. The average number of HPAI in poultry farms at the 229-municipal level as the hazard axis of the matrix was predicted using a negative binomial regression with nationwide outbreaks data from 2003 to 2018. The two components of the matrix were classified into five groups using the K-means clustering algorithm and multiplied, consequently producing the area-specific risk level of human infection. As a result, Naju-si, Jeongeup-si, and Namwon-si were categorized as high-risk areas for human infection with AI. These findings would contribute to designing the policies for human infection to minimize socio-economic damages.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.193-198
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• 2021

Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.144-156
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• 2023

Smart agriculture is a rapidly growing field that seeks to optimize crop yields and reduce risk through the use of advanced technology. A key challenge in this field is the need to create a comprehensive smart farm system that can effectively monitor and control the growth environment of crops, particularly when cultivating new varieties. This is where fuzzy theory comes in, enabling the collection and analysis of external environmental factors to generate a rule-based system that considers the specific needs of each crop variety. By doing so, the system can easily set the optimal growth environment, reducing trial and error and the user's risk burden. This is in contrast to existing systems where parameters need to be changed for each breed and various factors considered. Additionally, the type of house used affects the environmental control factors for crops, making it necessary to adapt the system accordingly. While developing such a framework requires a significant investment of labour and time, the benefits are numerous and can lead to increased productivity and profitability in the field of smart agriculture. We developed an AI platform for optimal control of facility houses by integrating data from mushroom crops and environmental factors, and analysing the correlation between optimal control conditions and yield. Our experiments demonstrated significant performance improvement compared to the existing system.

• Smart Media Journal
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• v.12 no.3
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• pp.112-119
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• 2023

Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.667-672
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• 2020

It is well known that intensive selection caused a decline in reproductive performance in dairy cattle. Interestingly, the reproductive performances including fertility and calving interval of Korean native beef cattle have declined in the last 20 years, suggesting that a breeding program focusing on carcass weight and intramuscular fat may affect the reproductive physiology in Korean native beef cattle, too. In this study, we analyzed the calving interval (CI) and selection index (SI) based on genome-wide association studies (GWAS) of Hanwoo cows for seven years (2013 - 2019). Multiparous cows (4.5 ± 0.11) were analyzed, which were bred by artificial insemination (AI). We first examined the distribution of the AIs and calving dates. About 40% of the AIs were carried out in May to June and October to December; subsequently, calving was observed from March to April and August to October, respectively, indicating the cows were seasonally bred. No correlation between CI and SI was found (y = 0.0459x - 17.64; R² = 0.0356), but the ratio of cows with a positive SI was higher in the longer CI group compared to the shorter group, suggesting that the selection for meat quality and quantity may affect the reproductive performances. In addition, the average value of SI was - 3.42 in the CI < 400 while + 5.79 in the CI > 400 although the values were not statistically significant. However, our results suggest that reproductive indices such as fertility and CI should be considered for sustainability in the Hanwoo breeding selection program.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• Steel and Composite Structures
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• v.49 no.2
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• pp.231-244
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• 2023

Many of the recent investigations in the field of geotechnical engineering focused on the bearing capacity theories of multilayered soil. A number of factors affect the bearing capacity of the soil, such as soil properties, applied overburden stress, soil layer thickness beneath the footing, and type of design analysis. An extensive number of finite element model (FEM) simulation was performed on a prototype slope with various abovementioned terms. Furthermore, several non-linear artificial intelligence (AI) models are developed, and the best possible neural network system is presented. The data set is from 3443 measured full-scale finite element modeling (FEM) results of a circular shallow footing analysis placed on layered cohesionless soil. The result is used for both training (75% selected randomly) and testing (25% selected randomly) the models. The results from the predicted models are evaluated and compared using different statistical indices (R² and RMSE) and the most accurate model BBO (R²=0.9481, RMSE=4.71878 for training and R²=0.94355, RMSE=5.1338 for testing) and TLBO (R²=0.948, RMSE=4.70822 for training and R²=0.94341, RMSE=5.13991 for testing) are presented as a simple, applicable formula.