• 식물병연구
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• 제26권4호
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• pp.195-201
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• 2020

Xanthomonas oryzae pv. oryzae는 벼 흰잎마름병을 일으키는 세균이며, 벼 흰잎마름병은 벼 주요 재배지에서 꾸준하게 발생하고 있다. 본 연구에서는 벼흰잎마름병균을 신속하고 간편하게 검출하기 위해 등온증폭법 중 하나인 recombinase polymerase amplification (RPA)를 적용하여 현장진단 등을 위한 유전자기반 진단법을 개발하였다. RPA법은 짧은 시간의 등온 조건에서 유전자 증폭이 가능하다는 장점이 있다. 본 연구에서 개발한 벼 흰잎마름병 RPA 진단법은 39℃에서 5분간만 반응하면 목표유전자의 증폭이 이루어지므로 기존 진단법보다 신속하고 간편하며 우리나라에 존재하는 K1-K3a의 4종 레이스에 모두 적용가능하며, DNA 추출 없이 식물체 잎의 즙액으로 증폭반응 수행이 가능하며 기존 Taq 기반 PCR보다 약 10배 검출 민감도가 높고 고가의 thermal cycler 없이도 항온수조, 발열블록 혹은 체온을 이용한 증폭반응 수행이 가능하다. 벼흰잎마름병은 벼의 농작물재해보험 대상재해 병충해 중의 하나이므로 과학적 진단결과가 요구되나, 일선 농촌지도 현장에서는 유전자기반 진단을 위한 장비, 기술 등의 부족으로 분자진단이 어려웠다. 본 연구에서 개발한 벼 흰잎마름병 RPA 진단을 활용한다 면 병징 의심부위 마쇄액을 주형으로, 손으로 5분 동안 반응시키는 것만으로도 신속하고 간편하게 벼 흰잎마름병의 목표유전자 증폭산물을 형성하므로 벼 흰잎마름병 진단의 최일선에서 활용할 수 있을 것이다.

• 한국작물학회지
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• 제49권5호
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• pp.415-418
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• 2004

The effects of sonication and vacuum infiltration on transformation efficiency was investigated by using immature embryos of Korean wheat as explants. Two Agrobacterium tumefaciens strains, KYRT1 and EHA105, carrying pCAMBIA 1305.1 were used. Transformation efficiency was demonstrated by the detection of $\beta-glucu-ronidase$ (GUS) activity. GUS expression showed clear difference among Korean wheat cultivars. Geurumil showed higher GUS expression efficiency $79.1\%$ compared with other cultivars. The effects of the duration of vacuum infiltration and sonication treatment showed a tendency high GUS expression efficiency by their combination. In comparison with other Agrobacterium strains, KYRT1 showed high efficiency in most Korean cultivars.

• Journal of Crop Science and Biotechnology
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• 제10권3호
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• pp.123-132
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• 2007

In this review, we discuss the ways in which our understanding of the controls of nitrogen use efficiency applied to crop improvement has been increased through the development of molecular physiology studies using transgenic plants or mutants with modified capacities for nitrogen uptake, assimilation and recycling. More recently, exploiting crop genetic variability through quantitative trait loci and candidate gene detection has opened new perspectives toward the identification of key structural or regulatory elements involved in the control of nitrogen metabolism for improving crop productivity. All together these studies strongly suggest that in the near future nitrogen use efficiency can be improved both by marker-assisted selection and genetic engineering, thus having the most promise for the practical application of increasing the capacity of a wide range of economically important species to take up and utilize nitrogen more efficiently.

• Plant Breeding and Biotechnology
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• 제2권3호
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• pp.224-230
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• 2014

Amplified fragment length polymorphism (AFLP) is a molecular marker technique based on DNA and is extremely useful in detection of high polymorphism between closely related genotypes like Korean wheat cultivars. Six sequence characterized amplified regions (SCARs) have been developed from inter simple sequence repeat (ISSR) analysis which enabled the identification and differentiation of 13 Korean wheat cultivars from the other cultivars. We used six combinations of primer sets in our AFLP analysis for developing additional cultivar-specific markers in Korean wheat. Fifty-eight of the AFLP bands were isolated from EA-ACG/MA-CAC, EA-AGC/MA-CTG and EA-AGG/MA-CTA primer combinations. Of which 40 bands were selected to design SCAR primer pairs for Korean wheat cultivar identification. Three of 58 amplified primer pairs, KWSM006, KWSM007 and JkSP, enabled wheat cultivar identification. Consequently, 23 of 32 Korean wheat cultivars were classified by eight SCAR marker sets.

• 센서학회지
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• 제27권5호
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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.

• 산업경영시스템학회지
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• 제44권3호
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.57-66
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• 2023

Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.

• The Plant Pathology Journal
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• 제39권4호
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2001년도 춘계 학술대회지
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• pp.236-237
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• 2001

• 한국작물학회:학술대회논문집
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• 한국작물학회 2019년도 추계학술대회
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• pp.153-153
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• 2019