• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.280-293
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• 2018

The conversion of an invisible thermal radiation pattern of an object into a visible image using infrared (IR) thermal technology is very useful to understand phenomena what we are interested in. Although IR thermal images were originally developed for military and space applications, they are currently employed to determine thermal properties and heat features in various applications, such as the non-destructive evaluation of industrial equipment, power plants, electricity, military or drive-assisted night vision, and medical applications to monitor heat generation or loss. Recently, IR imaging-based monitoring systems have been considered for application in agricultural, including crop care, plant-disease detection, bruise detection of fruits, and the evaluation of fruit maturity. This paper reviews recent progress in the development of IR thermal imaging techniques and suggests possible applications of thermal imaging techniques in agriculture.

• Plant Resources
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• v.7 no.3
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• pp.200-205
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• 2004

Sweet potato infected with a viral disease (SPFMV) showed irregular chlorotic patterns, so called feathering associated with faint or distinct ring spots that have purple-pigmented borders. SPFMV was eliminated from sweet potato plants using meristem tip culture. MS medium supplemented with BAP (2mg/L) and NAA (0.05 mg/L) was used for shoot proliferation and 1/2 MS medium for rooting of the plants. Highest percentage of regenerated plants (60%) was obtained from the optimum size (0.3-0.5mm) meristem tips. Of these, 60% plants were found negative for SPFMV by RT-PCR. Virus detection by RT-PCR was found to be a reliable method. Meristem-tip culture to produce SPFMV-free quality sweet potato and virus detection by RT-PCR is an efficient, time saving and reliable method for production of SPFMV-free tissue culture raised plants.

• Smart Media Journal
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• v.12 no.7
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• pp.9-17
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• 2023

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

• Korean Journal of Poultry Science
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• v.44 no.2
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• pp.93-102
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• 2017

A duplex RT-PCR (dRT-PCR) assay was developed for the simultaneous detection and discrimination of non-virulent and virulent Newcastle disease virus (NDV) in a single PCR tube. Primers targeting the large polymerase protein (L) gene and the fusion protein (F) gene of NDV were designed to detect all NDVs (by common type PCR primers) and virulent NDVs (by pathotype PCR primers), respectively and evaluated experimentally with reference NDV strains and other poultry viral pathogens. PCR products of the expected size of 386 bp were amplified from all NDV samples whereas PCR products of the expected size of 229 bp were amplified from virulent NDV samples alone. Cross reaction was not observed with other avian viral pathogens. The detection limit of NDV by the dRT-PCR was estimated to be $10^3$ 50% egg infectious dose/0.1 mL. In the dRT-PCR using field isolates of NDV, the pathotype PCR primers detected specifically all of virulent field isolates of NDV from Malaysia, Pakistan and China whereas common type PCR primers detected 94.4% (51/54) of field isolates of NDV from China. Three Chinese NDV isolates with false negative result were non-virulent viruses. Our results indicate that the dRT-PCR might provide a rapid and simple tool for rapid simultaneous detection and discrimination of non-virulent and virulent NDVs. Therefore the developed dRT-PCR assay provides a powerful novel means for the rapid diagnosis of Newcastle disease.

• The Plant Pathology Journal
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• v.38 no.6
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• pp.629-636
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• 2022

Colletotrichum species is known as the major causal pathogen of red pepper anthracnose in Korea and various groups of fungicides are registered for the management of the disease. However, the consistent use of fungicides has resulted in the development of resistance in many red pepper-growing areas of Korea. Effective management of the occurrence of fungicide resistance depends on constant monitoring and early detection. Thus, in this study, various methods such as agar dilution method (ADM), gene sequencing, allele-specific polymerase chain reaction (PCR), and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) were applied for the detection of benzimidazole resistance among 24 isolates of Colletotrichum acutatum s. lat. and Colletotrichum gloeosporioides s. lat. The result of the ADM showed that C. gloeosporioides s. lat. was classified into sensitive and resistant isolates to benomyl while C. acutatum s. lat. was insensitive at ≥1 ㎍/ml of benomyl. The sequence analysis of the β-tubulin gene showed the presence of a single nucleotide mutation at the 198th amino acid position of five isolates (16CACY14, 16CAYY19, 15HN5, 15KJ1, and 16CAYY7) of C. gloeosporioides s. lat. Allele-specific PCR and PCR-RFLP were used to detect point mutation at 198th amino acid position and this was done within a day unlike ADM which usually takes more than one week and thus saving time and resources that are essential in the fungicide resistance management in the field. Therefore, the molecular techniques established in this study can warrant early detection of benzimidazole fungicide resistance for the adoption of management strategies that can prevent yield losses among farmers.

• Journal of Radiation Industry
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• v.3 no.1
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• pp.25-29
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• 2009

Microarrays can be used to screen thousands of binding events in a parallel and high throughput fashion and are of major importance in disease diagnosis and drug discovery. The use of radioisotope is conventionally regarded as one of the most sensitive detection methods. Atherosclerosis is a common disorder affecting arterial blood vessels. It happens when fat, cholesterol, and other substances made in the arterial blood vessels form a hard substances called plaque. Lipoprotein-associated phospholipase $A_2$ ($Lp-PLA_2$), a phospholipase $A_2$ enzyme, is used as a marker for cardiac disease. The detection of $Lp-PLA_2$ was accomplished by using radioactive [$^3H-acetyl$] PAF as a substrate and a feasibility study on RI biochip application to detection of $Lp-PLA_2$, a risk factors of atherosclerosis was performed. Inhibitive activity of a native plant extract was also determined by using the RI biochip. It was found to be applicable to a high-throughput screening of inhibitors for developing atherosclerosis therapeutic agents.

• Journal of Plant Biotechnology
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• v.47 no.2
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• pp.150-156
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• 2020

Weeds play an important role in the survival of viruses and are potential inoculum sources of viral diseases for crop plants. In this study, specimens of Pseudostellaria heterophylla exhibiting symptoms of the cucumber mosaic virus (CMV) were collected in Bonghwa, Korea. The characteristics of the disease were described and leaf RNA was extracted and sequenced to identify the virus. Three CMV contigs were obtained and PCR was performed using specific primer pairs. RNA from positive samples exhibiting CMV leaf symptoms was amplified to determine the coat protein. A sequence comparison of the coat protein gene from the CMV BH isolate shared the highest nucleotide identity (99.2%) with the CMV ZM isolate. Phylogenetic analysis showed that CMV-BH belonged to subgroup IA and that the most closely-related isolate was CMV-ZM. All test plants used for the biological assay were successfully infected with CMV and exhibited CMV disease symptoms such as blistering, mosaic, and vein yellowing. To our knowledge, this is the first report of CMV infection in P. heterophylla from Korea.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Korean Journal of Veterinary Service
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• v.39 no.2
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• pp.107-116
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• 2016

In the study, we developed and evaluated a uracil N-glycosylase (UNG)-supplemented single-tube nested reverse transcription-polymerase chain reaction (UsnRT-PCR) assay that can carried out first-round RT-PCR and second-round nested PCR in a reaction tube without reaction tube opening and can simultaneously detect EU- and NA-PRRSV. The UsnRT-PCR confirmed to have a preventing ability of mis-amplification by contamination of pre-amplified PRRSV DNA from previous UsnRT-PCR. Primer specificities were evaluated with RNAs extracted from 8 viral strains and our results revealed that the primers had a high specificity for both genotypes of PRRSV. The sensitivity of the UsnRT-PCR was 0.1 $TCID_{50}$/0.1 mL for EU- or NA-PRRSV, respectively, which is comparable to that of previously reported real time RT-PCR (RRT-PCR). Clinical evaluation on 110 field samples (60 sera and 50 lung tissues) by the UsnRT-PCR and the RRT-PCR showed that detection rates of the UsnRT-PCR was 70% (77/110), and was relatively higher than that of the RRT-PCR (69.1%, 76/110). The percent positive or negative agreement of the UsnRT-PCR compared to RRT-PCR was 96.1% (73/76) or 90.9% (30/33), showing that the test results of both assays may be different for some clinical samples. Therefore, it is recommend that diagnostic laboratory workers use the two diagnostic assays for the correct diagnosis for the relevant samples in the swine disease diagnostic laboratories. In conclusion, the UsnRT-PCR assay can be applied for the rapid, and reliable diagnosis of PRRSV without concerns about preamplified DNA carryover contamination that can occurred in PCR process in the swine disease diagnostic laboratories.