• 한국산학기술학회논문지
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• 제22권5호
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• pp.7-14
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• 2021

토마토 작물은 병해충의 영향을 많이 받기 때문에 이를 예방하지 않으면 농업 경제에 막대한 손실을 초래할 수 있다. 따라서 토마토의 다양한 병해충의 진단을 빠르고 정확하게 진단하는 시스템이 요구된다. 본 논문에서는 ImageNet 데이터 셋 상에서 다양하게 사전 학습된 딥러닝 기반 CNN 모델을 적용하여 토마토의 9가지 병해충 및 정상인 경우의 클래스를 분류하는 시스템을 제안한다. PlantVillage 데이터 셋으로부터 발췌한 토마토 잎의 이미지 셋을 3가지 딥러닝 기반 CNN 구조를 갖는 ResNet, Xception, DenseNet의 입력으로 사용한다. 기본 CNN 모델 위에 톱-레벨 분류기를 추가하여 제안 모델을 구성하였으며, 훈련 데이터 셋에 대해 5-fold 교차검증 기법을 적용하여 학습시켰다. 3가지 제안 모델의 학습은 모두 기본 CNN 모델의 계층을 동결하여 학습시키는 전이 학습과 동결을 해제한 후 학습률을 매우 작은 수로 설정하여 학습시키는 미세 조정 학습 두 단계로 진행하였다. 모델 최적화 알고리즘으로는 SGD, RMSprop, Adam을 적용하였다. 실험 결과는 RMSprop 알고리즘이 적용된 DenseNet CNN 모델이 98.63%의 정확도로 가장 우수한 결과를 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2001년도 춘계 학술발표논문집
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• pp.93-96
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• 2001

Estimation of leaf wetness duration (LWD) facilitates assessment of the likelihood of outbreaks of many crop diseases. Models that estimate LWD may be more convenient and grower-friendly than measuring it with wetness sensors. Empirical models utilizing statistical procedures such as CART (Classification and Regression Tree; Gleason et al., 1994) have estimated LWD with accuracy comparable to that of electronic sensors.(omitted)

• 한국식물병리학회:학술대회논문집
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• 한국식물병리학회 1998년도 Proceedings of International symposium on Recent Technology of Chemical Control of Plant Diseases
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• pp.50-69
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• 1998

Dimethomorph is a novel fungicide with a high level of activity against diseases induced by certain Oomycetes, including fungal populations that are resistant to other products. In several ways, this fungicide illustrates the opportunities and challenges presented by many modern pesticides. The specific mode of action, which affects cell wall formation, is associated with a very high level of performance and low dose rates under field conditions. These low dose rates, combined with a low level of toxicity to non-target organisms present an outstanding safety profile. This same highly-specific mode of action, however, limits the spectrum of activity and suggests the need for a resistance management plan, both of which must be addressed in new product development. In addition, the biological and physiochemical properties of this, and other new products are not adequately described by the traditional classification of fungicides into“protectant”and“systemic”types. These unique profiles provide novel and useful products for disease control.

• 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.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• 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.

• 대한수의학회지
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• 제55권2호
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• pp.97-103
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• 2015

In this study, the Korean veterinary medical devices management system was evaluated relative to systems in the USA, EU, and Japan. Veterinary medical devices are regulated in Korea based on the Medical Appliance Act of 1997. This was initially supervised by the Ministry of Agriculture, Food and Rural Affairs and Korea Animal Health Products Association, and subsequently by the Animal and Plant Quarantine Agency (QIA) in 2000. These devices were classified approximately 1,400 categories as instruments, supplies, artificial insemination apparatus, and other categories. Each of these devices was assigned to four regulatory grades by the QIA in 2007. The ranking system for veterinary medical devices was implemented in 2014 with 820 products from 162 companies registered by that year. However, in vitro diagnostic devices (IVDDs) for animals were managed as medical devices and biological medicine. In vitro diagnostic reagents for treating infection diseases are not subjected to either a classification or grading system. Veterinary medical devices are currently exempt from good manufacturing practices (GMP) and device tracking requirements. Due to gradual growth of the domestic veterinary medical devices market since 2008, regulation of these devices should be improved with re-examination of IVDDs and GMP certification for the effective operating system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• 한국응용곤충학회지
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• 제15권2호
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• pp.61-68
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• 1976

광교, 동북태, 강림, 육우3호, 은대두등과 같은 대리장려품종이 바이러스에 의하여 심하게 이병되었다. 이 병은 주로 모자익병의 발생이 많은 강원, 경기지방에서 발병이 심하였으나 모자익병이 심하지 않은 전남 등 남부지방에서도 발병되고 있다. 병징으로 보아 tobacco ringspot virus에 의한 대두의 피해와 유사한 것으로 보였으나 지표식물검정과 혈청검정에 의하여 조사한 결과 모두 부정적이었으며 대두품종에 따른 이병정도의 상이, 품종과 접종원에 의한 병징의 변이가 많았다. 이병주에서 분리되는 병징형은 Mottling과 necrosis였으며 지금까지의 연구결과 이 대두병해는 모자익바이러스(SMV)의 계통 내지는 tobacco ringspot virus 이외의 두류바이러스의 복각감염에 의한 것으로 생각할 수 있으나 SMV의 계통에 의한 피해일 가능성이 더욱 유력시되고 있다.