• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.4
• /
• pp.210-219
• /
• 2023

The decline in the rural populace and an aging workforce have led to fatalities due to worsening environments and hazards within vinyl greenhouses. Therefore, it is necessary to automate crop cultivation and disease detection system in greenhouses to prevent labor loss. In this paper, an object detection-based model is used to detect diseased crop in greenhouses. In addition, the system proposed configures the environment of the artificial intelligence model in the cloud to ensure stability. The system captures images taken inside the vinyl greenhouse and stores them in a database, and then downloads the images to the cloud to perform inference based on Yolo-v4 for detection, generating JSON files for the results. Analyze this file and send it to the database for storage. From the experimental results, it was confirmed that disease detection through object detection showed high performance in real environments like vinyl greenhouses. It was also verified that efficient monitoring is possible through the database

• The Plant Pathology Journal
• /
• v.22 no.4
• /
• pp.314-317
• /
• 2006

A phage technique for detection of Xanthomonas axonopodis pv. citri, a causal bacterium of canker on Sastuma mandarin fruits was developed. Phage and ELISA techniques were compared for their sensitivity for detection of Xanthomonas axonopodis pv. citri on orange fruits. Both of techniques revealed a similar efficiency for the bacterial detection; the pathogenic bacteria were observed in pellet from the fruits with over one canker spot with below 2 mm in diameter. In field assays, the increase of phage population(120%) on surface of the fruits related to the disease development one month later indicated that the bacterial pathogens inhabit on the surface. The procedure will be effectively used for detection of only living bacterial pathogen on fruit surfaces of Satsuma mandarin and for the disease forecasting.

• Korean Journal of Veterinary Service
• /
• v.38 no.3
• /
• pp.145-153
• /
• 2015

In this study, we developed a loop-mediated isothermal amplification (LAMP) with hydroxynaphtol blue dye (HNB) for rapid and direct visual detection of porcine circovirus 2 DNA with high sensitivity and specificity. The LAMP was completed in 40 min at $63^{\circ}C$, and the results of the LAMP can be confirmed by naked eye without any detection process. The sensitivity of the LAMP was 10-fold higher than that of the commercial PCR (cPCR) and real time PCR (rPCR) previously reported. In clinical application, the PCV2 detection rate of the LAMP was the same on porcine tissue samples (75.0%, 36/48) between porcine blood samples (75.0%, 39/52). The PCV2 detection rate (75.0%) of LAMP was higher than those of the cPCR and rPCR (67.3%, 35/52) in blood samples. In conclusion, the LAMP developed in the study could be an useful alternative method for the detection of PCV2 in the swine disease diagnostic laboratories.

• Journal of Microbiology and Biotechnology
• /
• v.31 no.12
• /
• pp.1716-1721
• /
• 2021

Chikungunya fever is an arboviral disease caused by the Chikungunya virus (CHIKV). The disease has similar clinical manifestations with other acute febrile illnesses which complicates differential diagnosis in low-resource settings. We aimed to develop a rapid test for CHIKV detection based on the nucleic acid lateral flow immunoassay technology. The system consists of a primer set that recognizes the E1 region of the CHIKV genome and test strips in an enclosed cassette which are used to detect amplicons labeled with FITC/biotin. Amplification of the viral genome was done using open-source PCR, a low-cost open-source thermal cycler. Assay performance was evaluated using a panel of RNA isolated from patients' blood with confirmed CHIKV (n = 8) and dengue virus (n = 20) infection. The open-source PCR-NALFIA platform had a limit of detection of 10 RNA copies/ml. The assay had a sensitivity and specificity of 100% (95% CI: 67.56% - 100%) and 100% (95% CI: 83.89% - 100%), respectively, compared to reference standards of any positive virus culture on C6/36 cell lines and/or qRT-PCR. Further evaluation of its performance using a larger sample size may provide important data to extend its usefulness, especially its utilization in the peripheral healthcare facilities with scarce resources and outbreak situations.

• Korean Journal of Radiology
• /
• v.21 no.11
• /
• pp.1276-1279
• /
• 2020

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.51-62
• /
• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Genomics & Informatics
• /
• v.7 no.1
• /
• pp.32-37
• /
• 2009

The quality assurance (QA) is of utmost importance in biobanks when archived biomaterials are distributed to biomedical researchers. For sample authentication and cross-contamination detection, the two fundamental elements of QA, STR genotyping is usually utilized. However, the incorporated number of STR markers is highly redundant for biobanking purposes, resulting in time and cost inefficiency. An index to measure the cross-contamination detection capability of an STR marker, the mixture probability (MP), was developed. MP as well as other forensic parameters for STR markers was validated using STR genotyping data on 2328 normal Koreans with the commercial AmpFlSTR kit. For Koreans, 7 STR marker (D2S1338, FGA, D18S51, D8S1179, D13S317, D21S11, vWA) set was sufficient to provide discrimination power of ${\sim}10^{-10}$ and cross-contamination detection probability of ${sim}1$. Interestingly, similar marker sets were obtained from African Americans, Caucasian Americans, and Hispanic Americans under the same level of discrimination power. Only a small subset of commonly used STR markers is sufficient for QA purposes in biobanks. A procedure for selecting optimal STR markers is outlined using STR genotyping results from normal Korean population.

• Journal of Forest and Environmental Science
• /
• v.40 no.1
• /
• pp.1-8
• /
• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• Korean Journal of Veterinary Service
• /
• v.31 no.1
• /
• pp.43-55
• /
• 2008

Recently, the major viral diseases, Newcastle disease (ND), infectious bronchitis (IB), low pathogenic avian influenza (LPAI), avian pneumovirus infection (APV), Marek's disease (MD) and infectious bursal disease (IBD), have led to huge economic losses in chicken industry of Korea. To evaluate prevalence of the major viral disease infections in broiler breeder and broiler farms, epidemiological survey has been conducted in Jeonbuk province from 2005 to 2007 by serological ELISA test for APV, PCR for MD, and RT-PCR for ND, IB, LPAI and IBD, respectively. A total of 424 cases was submitted to our laboratory for diagnosis of the major viral disease from broiler breeder and broiler farms in the above period. The diagnosed results were analysed for the detection rate of infections on basis of years, seasons and ages, respectively. This study was showed that the detection rates of ND and APV were considerably high for every years regardless of seasons and ages in both broiler breeder and commercial broiler. In comparison with detection rates of ND and APV, IB and LPAI were lower but detected around 10% for every years. Especially, detection rate of IB was significantly high in commercial broiler than in broiler breeder. Therefore, to minimize economic losses for broiler breeder and broiler farms, it will need for effective countermeasures to decrease detection rate of the viral respiratory diseases. Although the detection rates of MD and IBD were gradually decreased from 2005 to 2007 in both broiler breeder and commercial broiler, it will continually make an effort about disease control for increasing productivity in chicken industry.

• Journal of Mechanical Science and Technology
• /
• v.20 no.12
• /
• pp.2265-2283
• /
• 2006

There is tremendous interest of research which surrounds the concept of 'osteoporosis,' as shown by the intense and growing research activity in the field. The urgency to advance knowledge in this area is motivated by the need to understand not only the causes, diagnosis and treatment but also need for early identification or detection of this silent disease. Despite the various researches work is going on, important issues remain unresolved. In this paper, Osteoporosis has also been discussed with respect to biological, engineering, biochemical and physical aspects. The diagnostic and therapeutic techniques have been described for osteoporosis, for better health care. The novelty of the review paper lies in clarifying several myths, explaining the disease in details with biomedical engineering aspects and focuses on the several detection techniques, providing a new direction for early diagnosis of this deadly disease and gives new directions for the POCT device for Osteoporosis.