• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.7-14
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• 2021

Tomato crops are highly affected by tomato diseases, and if not prevented, a disease can cause severe losses for the agricultural economy. Therefore, there is a need for a system that quickly and accurately diagnoses various tomato diseases. In this paper, we propose a system that classifies nine diseases as well as healthy tomato plants by applying various pretrained deep learning-based CNN models trained on an ImageNet dataset. The tomato leaf image dataset obtained from PlantVillage is provided as input to ResNet, Xception, and DenseNet, which have deep learning-based CNN architectures. The proposed models were constructed by adding a top-level classifier to the basic CNN model, and they were trained by applying a 5-fold cross-validation strategy. All three of the proposed models were trained in two stages: transfer learning (which freezes the layers of the basic CNN model and then trains only the top-level classifiers), and fine-tuned learning (which sets the learning rate to a very small number and trains after unfreezing basic CNN layers). SGD, RMSprop, and Adam were applied as optimization algorithms. The experimental results show that the DenseNet CNN model to which the RMSprop algorithm was applied output the best results, with 98.63% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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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.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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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)

• Proceedings of the Korean Society of Plant Pathology Conference
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• 1998.06a
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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.

• Research in Plant Disease
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• v.17 no.3
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• pp.265-271
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• 2011

Seven phytoplasma diseases have been occurred on floricultural crops in Korea : Ph-ch1 and Ph-ch2 of chrysanthemum, Ph-lily of lily, petunia flat stem-Korean (PFS-K) of petunia, poinsettia branch inducing- Korean (PoiBI-K) of poinsettia, statis witches' broom-Korean (SWB-K) of statis and azalea witches broom (AWB). Classification of the seven phytoplasmal diseases based on 16S ribosomal RNA (rRNA) sequences showed that floricultural crop phytoplasma disease were widespread in order of aster yellow (AY), stolbur and X-disease in Korea. In phenotypic characters, the fasciation was occurred in both monocotyledon plant of lily and dicotyledon plants of petunia and poinsettia. Besides, the fascination was occurred in Ph-lily of stolbur, petunia PFS-K of AY and PoiBI-K of X-disease. This result indicated that phytoplasma classification based on 16S rRNA and symptoms are not consistently related. The comparison of 16S rRNA sequence of the seven floricultural crop phytoplasma with five tree phytoplasmal diseases of jujube witches' broom, paulownia witches' broom, wild jujube witches' broom, mulberry dwarf, golden rain phytoplasma occurred in Korea showed as high as 88.5-99.9% homology. Among them, especially mulberry dwarf showed the highest homology with the seven floricultural crop phytoplasms. Based on this result, floricultural crop phytoplasmas were assumed to be transmitted by insect vectors from tree phytoplasmas in Korea.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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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.

• Korean Journal of Agricultural Science
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• v.39 no.1
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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.

• Korean Journal of Veterinary Research
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• v.55 no.2
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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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• v.13 no.4
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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.

• Korean journal of applied entomology
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• v.15 no.2 s.27
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• pp.61-68
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• 1976

Leading soybean cultivars such as Kwanggyo, Yugu No.3, Dongbugtae, Gangrim, and Eundaedu were heavily diseased by a virus in Korea. The disease was most severe in the northern provinces where soybean mosaic virus also occurrs, but the disease has also been observed in other provinces where soybean diseases are less prevalent. The disease symptoms were similar to bud blight caused by tobacco ringspot virus; but this was not confirmed in inoculation tests on indicator plants and serological experiments. There were some differences in varietal susceptibility to the disease, with symptom variation depending on the soybean cultivar and source of inoculm. Disease symptoms on infected soybean plants were mottling and necrosis. The present results, therefore, indicate some strains of SMV or a mixture of legume viruses may or may not be responsible for the disease.