• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.285-292
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• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• Journal of Forest and Environmental Science
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• v.40 no.1
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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.

• 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/.

• The Plant Pathology Journal
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• v.34 no.4
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• pp.327-334
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• 2018

Northern corn leaf spot and southern corn leaf blight caused by Cochliobolus carbonum (anamorph, Bipolaris zeicola) and Cochliobolus heterostrophus (anamorph, Bipolaris maydis), respectively, are common maize diseases in Korea. Accurate detection of plant pathogens is necessary for effective disease management. Based on the polyketide synthase gene (PKS) of Cochliobolus carbonum and the nonribosomal peptide synthetase gene (NRPS) of Cochliobolus heterostrophus, primer pairs were designed for PCR to simultaneously detect the two fungal pathogens and were specific and sensitive enough to be used for duplex PCR analysis. This duplex PCR-based method was found to be effective for diagnosing simultaneous infections from the two Cochliobolus species that display similar morphological and mycological characteristics. With this method, it is possible to prevent infections in maize by detecting infected seeds or maize and discarding them. Besides saving time and effort, early diagnosis can help to prevent infections, establish comprehensive management systems, and secure healthy seeds.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.2
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• pp.142-148
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• 2013

Bacterial diseases in soybean are bacterial pustule by Xanthomonas axonopodis pv. glycines, wildfire by Pseudomonas syringae pv. tabaci, bacterial blight by Pseudomonas savastanoi pv. glycines and bacterial brown spot by Pseudomonas syringae pv. syringae in Korea. It is difficult to identify each disease by early symptoms in fields, because the initial symptoms of these diseases are very similar to each other. In this study, we developed multiplex PCR detection method for rapid and accurate diagnosis of bacterial diseases. The glycinecin A of X. axonopodis pv. glycines, the tabtoxin of P. syringae pv. tabaci, the coronatine of P. savastanoi pv. glycines and the syringopeptin of P. syringae pv. syringae have been reported previously. These bacteriocin or phytotoxin producing genes were targeted to design the specific diagnostic primers. The primer pairs for diagnosis of each bacterial diseases were selected without nonspecific reactions. The studies on simultaneous diagnosis method were also conducted with primarily selected 21 primers. As a result, we selected PCR primer sets for multiplex PCR. Sizes of the amplified PCR products using the multiplex PCR primer set consist of 280, 355, 563 and 815 bp, respectively. This multiplex PCR method provides a efficient, sensitive and rapid tool for the diagnosis of the bacterial diseases in soybean.

• Korean Journal Plant Pathology
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• v.10 no.2
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• pp.99-104
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• 1994

A computer-based diagnosing system for diseases of grasses, ornamental plant and fruit trees was developed using a 16 bit personal computer (Model Acer 900) and BASIC was used as a programing language. the developed advisory system was named as Korean Plant Disease Advisory System (KOPDAS). The diagraming system files were composed of a system operation file and several database files. The knowledge-base files are composed of text files, code files and implement program files. The knowledge-base of text files are composed of 79 files of grasses diseases, 122 files of ornamental plant diseases and 67 files of fruit tree diseases. The information of each text file include disease names, causal agents, diseased parts, symptoms, morphological characteristics of causal organisms and control methods for the diagnosing of crop diseases.

• Research in Plant Disease
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• v.21 no.4
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• pp.321-325
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• 2015

A rapid and simple RT-PCR diagnosis method for detection of Rice stripe virus (RSV), one of major virus infecting rice, was developed using porous ceramic cubes in this study. The porous ceramic cube can rapidly absorb biological molecules such as small-sized proteins and nucleic acid fragments into its pores. We examined whether this ability of porous ceramic cubes could be applied for isolating viral nucleic acids or particles from the RSV- infected plant tissues. In this study, we found that the porous ceramic cube was capable of absorbing a detection level of viruses from the rice tissues infected with RSV and established RT-PCR-based RNA diagnosis method using porous ceramic cubes.

• The Journal of Bigdata
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• v.1 no.1
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• pp.19-22
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• 2016

Many Studies have been actively conducted on the early diagnosis of the crop pest utilizing IT technology. The purpose of the paper is to discuss on the image processing method capable of detecting the crop leaf pest prematurely by analyzing the image of the leaf received from the camera sensor. This paper proposes an algorithm of diagnosing leaf infection by utilizing an improved K means clustering method. Leaf infection grouping test showed that the proposed algorithm illustrated a better performance in the qualitative evaluation.

• The Plant Pathology Journal
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• v.34 no.6
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• pp.459-469
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• 2018

Plants and microorganisms (microbes) use information from chemicals such as volatile compounds to understand their environments. Proficiency in sensing and responding to these infochemicals increases an organism's ecological competence and ability to survive in competitive environments, particularly with regard to plant-pathogen interactions. Plants and microbes acquired the ability to sense and respond to biogenic volatiles during their evolutionary history. However, these signals can only be interpreted by humans through the use of state-of the-art technologies. Newly-developed tools allow microbe-induced plant volatiles to be detected in a rapid, precise, and non-invasive manner to diagnose plant diseases. Beside disease diagnosis, volatile compounds may also be valuable in improving crop productivity in sustainable agriculture. Bacterial volatile compounds (BVCs) have potential for use as a novel plant growth stimulant or as improver of fertilizer efficiency. BVCs can also elicit plant innate immunity against insect pests and microbial pathogens. Research is needed to expand our knowledge of BVCs and to produce BVC-based formulations that can be used practically in the field. Formulation possibilities include encapsulation and sol-gel matrices, which can be used in attract and kill formulations, chemigation, and seed priming. Exploitation of biogenic volatiles will facilitate the development of smart integrated plant management systems for disease control and productivity improvement.

• Research in Plant Disease
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• v.26 no.4
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• pp.222-228
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• 2020

Erwinia amylovora, a causal bacterium of fire blight disease, is registered as a prohibited quarantine pathogen in Korea. To control the disease, the government should diagnose the disease, dig and bury the host trees when fire blight occurs. Fire blight was the first reported in 43 orchards of Anseong, Cheonan, and Jecheon in 2015, and 42.9 ha of host trees were eradicated. However, the disease spread to eleven cities, so that 348 orchards and 260.4 ha of host trees were eradicated until 2019. Fire blight of Asian pear occurred mainly in the southern part of Gyeonggi, and Chungnam province, on average of 29±9.2 orchards per year. And the age of the infected trees were mostly 20-30 years old. In apple trees, the disease occurred mainly in the northern part of Gyeonggi, Gangwon, and Chungbuk province, on average of 41±57.6 orchards per year, increased highly in 2018 and 2019. The age of infected apple trees were under 20 years old. Therefore, because the disease spread rapidly in young apple trees, spraying control agents to the trees in a timely manner and removing infected trees quickly are important to prevent the spread of fire blight in the orchard of immature trees.