• 한국시뮬레이션학회논문지
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• 제29권4호
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• pp.1-7
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• 2020

농작물 질병에 대한 조기 진단은 질병의 확산을 억제하고 농업 생산성을 증대하는 데에 있어 중요한 역할을 하고 있다. 최근 합성곱신경망(convolutional neural network, CNN)과 같은 딥러닝 기법을 활용하여 농작물 잎사귀 이미지 데이터세트를 분석하여 농작물 질병을 진단하는 다수의 연구가 진행되었다. 이와 같은 연구를 통해 농작물 질병을 90% 이상의 정확도로 분류할 수 있지만, 사전 학습된 농작물 질병 외에는 진단할 수 없다는 한계를 갖는다. 본 연구에서는 미학습 농작물에 대해 효율적으로 질병 여부를 진단하는 모델을 제안한다. 이를 위해, 먼저 VGG16을 활용한 농작물 질병 분류기(CDC)를 구축하고 PlantVillage 데이터세트을 통해 학습하였다. 이어 미학습 농작물의 질병 진단이 가능하도록 수정된 질병 분류기(mCDC)의 구축방안을 제안하였다. 실험을 통해 본 연구에서 제안한 수정된 질병 분류기(mCDC)가 미학습 농작물의 질병진단에 대해 기존 질병 분류기(CDC)보다 높은 성능을 보임을 확인하였다.

• 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.

• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.1-8
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• 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.

• The Plant Pathology Journal
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• 제32권5호
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• pp.431-440
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• 2016

In 2004, bacterial spot-causing xanthomonads (BSX) were reclassified into 4 species-Xanthomonas euvesicatoria, X. vesicatoria, X. perforans, and X. gardneri. Bacterial spot disease on pepper plant in Korea is known to be caused by both X. axonopodis pv. vesicatoria and X. vesicatoria. Here, we reidentified the pathogen causing bacterial spots on pepper plant based on the new classification. Accordingly, 72 pathogenic isolates were obtained from the lesions on pepper plants at 42 different locations. All isolates were negative for pectolytic activity. Five isolates were positive for amylolytic activity. All of the Korean pepper isolates had a 32 kDa-protein unique to X. euvesicatoria and had the same band pattern of the rpoB gene as that of X. euvesicatoria and X. perforans as indicated by PCR-restriction fragment length polymorphism analysis. A phylogenetic tree of 16S rDNA sequences showed that all of the Korean pepper plant isolates fit into the same group as did all the reference strains of X. euvesicatoria and X. perforans. A phylogenetic tree of the nucleotide sequences of 3 housekeeping genes-gapA, gyrB, and lepA showed that all of the Korean pepper plant isolates fit into the same group as did all of the references strains of X. euvesicatoria. Based on the phenotypic and genotypic characteristics, we identified the pathogen as X. euvesicatoria. Neither X. vesicatoria, the known pathogen of pepper bacterial spot, nor X. perforans, the known pathogen of tomato plant, was isolated. Thus, we suggest that the pathogen causing bacterial spot disease of pepper plants in Korea is X. euvesicatoria.

• 농업과학연구
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• 제39권1호
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• pp.117-123
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• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

• 한국작물학회지
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• 제46권1호
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• pp.53-56
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• 2001

Barley Yellow Dwarf Virus (BYDV), an aphid-borne luteovirus, is a major plant pathogenic disease causing a huge economic loss in the grain production of a wide range of Gramineae species throughout the world. It has been recently reported that BYDV also occurred frequently in wheat field of Korea. Here, we performed to develop the detection and classification methods of BYDV strains that were accomplished by reverse transcription-polymerase chain reaction (RT-PCR). Since there are high variations among BYDV strains, three pairs of primers were designed to detect BYDV strains such as PAV (Vic-PAV and CN-PAV) and MAV (primer A) simultaneously, specifically Vic-PAV(primer B), and MAV (primer C) based on the genomic RNA sequences of BYDV strains previously published. The validity of the primers was confirmed using several BYDV strains obtained from CIMMYT. Though three BYDV strains were able to be detected using primer A, PCR products were not distinguished between two PAV strains. It was possible to separate them with a restriction enzyme, EcoRI, whose restriction site was present in the amplified DNA fragment from Vic-PAV, but not from CN-PAV.

• The Plant Pathology Journal
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• 제37권3호
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• pp.243-257
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• 2021

Bacillus pumilus is the causal agent of trunk bulges disease affecting rubber and rubberwood quality and yield production. In this study, B. pumilus and other closely related species were included in B. pumilus group, as they shared over 99.5% similarity from 16S rRNA analysis. Multilocus sequence analysis (MLSA) of five housekeeping genes and repetitive elements-based polymerase chain reaction (rep-PCR) using REP, ERIC, and BOX primers conducted to analyze the diversity and systematic relationships of 20 isolates of B. pumilus group from four rubber tree plantations in Peninsular Malaysia (Serdang, Tanah Merah, Baling, and Rawang). Multi rep-PCR results revealed the genetic profiling among the B. pumilus group isolates, while MLSA results showed 98-100% similarity across the 20 isolates of B. pumilus group species. These 20 isolates, formerly established as B. pumilus, were found not to be grouped with B. pumilus. However, being distributed within distinctive groups of the B. pumilus group comprising of two clusters, A and B. Cluster A contained of 17 isolates close to B. altitudinis, whereas Cluster B consisted of three isolates attributed to B. safensis. This is the first MLSA and rep-PCR study on B. pumilus group, which provides an in-depth understanding of the diversity of these rubber-pathogenic isolates in Malaysia.

• 식물병연구
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• 제17권3호
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• pp.265-271
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• 2011

우리나라에서 화훼류에 7종류의 파이토플라스마병이 발생하였다. 국화의 Ph-ch1과 Ph-ch2, 나리의 Ph-lily, 페튜니아의 petunia flat stem(PFS-K), 포인세티아의 poinsettia branch inducing(PoiBI-K), 스타티스의 statis witches' broom (SWB-K)과 아잘레아의 azalea witches broom(AWB) 등이다. 16S rRNA 유전자 염기서열을 기본으로 화훼류 파이토플라스마를 분류한 결과 우리나라에는 aster yellow(AY), stolbur와 X-disease 순으로 많이 발생하였다. 파이토플라스마의 특징적인 병징 가운데 하나인 대화증상은 단자엽 식물인 나리와 페튜니아, 포인세티아와 같은 쌍자엽식물에서 모두 발생하였다. 또한 대화증상은 stolbur 그룹의 Ph-lily, AY 그룹의 petunia PFS-K와 X-disease의 포인세티아 PoiBI-K에서 모두 나타났다. 이 결과는 16S rRNA 유전자 염기서열에 기초를 둔 파이토플라스마 분류와 증상과는 일관성있게 일치하지 않는다는 것을 알 수 있다. 우리나라 화훼류에서 발생한 7종의 파이토플라스마를 대추나무빗자루, 오동나무빗자루, 묏대추나무빗자루, 뽕나무 오갈 및 모감주나무파이토플라스마 등 5종의 수목 파이 토플라스마와 16S rRNA 유전자의 염기서열을 비교한 결과 88.5-99.9%의 매우 높은 상동성을 나타내었다. 특히 뽕나무오갈병 파이토플라스마는 PoiBI-K를 제외한 6종의 화훼류 파이토플라스마와 96.3-99.9% 가장 높은 상동성을 나타내었다. 이 결과로 우리나라 화훼류에 발생한 파이토플라스마병은 매개충을 통하여 수목으로부터 전염되었을 것으로 추정되었다.

• 식물병연구
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• 제24권2호
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• pp.87-98
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• 2018

곰팡이 종들은 유전체내에 대략 10% 정도의 다양한 전이인자와 함께 반복적인 염기서열을 갖는다. 이러한 전이인자들의 대부분은 유전체내에서 활발히 전이되며 곰팡이 병원균의 기주 범위와도 연관성을 갖으며 분포하는 것으로 알려져있다. 화본과 작물에 병을 일으키는 도열병에 분포하는 전이인자들은 활발히 전이하는 것으로 보이며, 특정 기주에 감염하는 개체군에 특이적으로 분포하는 경우가 많았다. 다수의 연구 보고에서도열병균의 전이인자가 비병원성 유전자의 기능을 상실하는데 작용하여, 이로인해 저항성 품종에 병을 일으켰다. 따라서, 도열병균의 전이인자들은 식물-곰팡이 사이의 상호 진화를 유도하는 원동력 중 하나일 수 있다. 본 총설에서는 도열병균에 존재하는 전이인자들의 종류와 생물학적인 기능에 관해 정리하였다.

• 스마트미디어저널
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• 제8권2호
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.