• The Plant Pathology Journal
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• v.28 no.3
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• pp.239-247
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• 2012

Phytoplasmas have been associated with more than 46 plant species in Korea. Several vegetables, ornamentals, fruit trees and other crop species are affected by phytoplasma diseases. Six 16Sr groups of phytoplasmas have been identified and these phytoplasmas are associated with 63 phytoplasma diseases. Aster yellows phytoplasmas are the most prevalent group and has been associated with more than 25 diseases in Korea. Jujube witches' broom, paulownia witches' broom and mulberry dwarf diseases cause economic losses to host trees throughout the country. So far, Korean phytoplasmas belong to six species of 'Candidatus Phytoplasma'; 'Ca. P. asteris', 'Ca. P. pruni$^*$', 'Ca. P. ziziphi', 'Ca. P. trifolii', 'Ca. P. solani$^*$' and 'Ca. P. castaneae'. The diseases are distributed throughout the country and most of them were observed in Gyeongbuk and Chonbuk provinces. At least four insect vectors; Cyrtopeltis tenuis, Hishimonus sellatus, Macrosteles striifrons and Ophiola flavopicta have been identified for phytoplasma transmission.

• Research in Plant Disease
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• v.30 no.1
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• pp.99-102
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• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Mycobiology
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• v.48 no.3
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• pp.169-183
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• 2020

Nigrospora is a monophyletic genus belonging to Apiosporaceae. Species in this genus are phytopathogenic, endophytic, and saprobic on different hosts. In this study, leaf specimens with disease symptoms were collected from host plants from the Shandong Peninsula, China. The fungal taxa associated with these leaf spots were studied using morphology and phylogeny based on ITS, TEF1, and TUB2 gene regions. In this article, we report on the genus Nigrospora with N. gorlenkoana, N. oryzae, N. osmanthi, N. rubi, and N. sphaerica identified with 13 novel host associations including crops with economic importance such as bamboo and Chinese rose.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2004.10a
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• pp.42-43
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• 2004

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• Journal of Microbiology and Biotechnology
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• v.20 no.1
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• pp.193-201
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• 2010

A specific and rapid real-time PCR assay for detecting Ralstonia solanacearum in horticultural soil and plant tissues was developed in this study. The specific primers RSF/RSR were designed based on the upstream region of the UDP-3-O-acyl-GlcNAc deacetylase gene from R. solanacearum, and a PCR product of 159 bp was amplified specifically from 28 strains of R. solanacearum, which represent all genetically diverse AluI types and all 6 biovars, but not from any other nontarget species. The detection limit of $10^2\;CFU/g$ tomato stem and horticultural soil was achieved in this real-time PCR assay. The high sensitivity and specificity observed with field samples as well as with artificially infected samples suggested that this method might be a useful tool for detection and quantification of R. solanacearum in precise forecast and diagnosis.

• 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.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2003.10a
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• pp.149.2-150
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• 2003

Occurrences of virus diseases on paprika ( Capsicum annuum var. grossum) were surveyed in Joennam province from 1999 to 2003 and the collected samples showing virus-like symptoms were tested using ELISA. Virus diseases appeared 4.5％, 17.5％, and 4.9％ in 2000, 2002, and 2003, respectively. As the results of investigation of the seasonal incidence with the growing stages of plant, virus was not occurred at seedling stage and was slightly from the planting time to the first harvesting time, but was dramatically increased at the second harvesting time. Virus diseases were more severe on the vinyl house than on the green house. Pepper mild mottle virus (PMMoV) was severely occurred in 2000 but not after that year. Comparing the virus species, Pepper mottle virus (PepMoV) was 35.9％, Broad bean wilt virus (BBWV) was 14.1％, and Cucumber mosaic virus (CMV) was 10.9％ in 2002, and 76.0％, 11.1％, and 2.4％ in 2003, respectively.

• Proceedings of the Korean Society of Applied Entomolohy Conference
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• 2005.05a
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• pp.85-85
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• 2005