• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.94-98
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• 2021

As the world's population grows, the food industry becomes increasingly important. Among them, agriculture is an industry that produces stocks of people all over the world, which is very important food industry. Despite the growing importance of agriculture, however, a large number of crops are lost every year due to pests and malnutrition. So, we propose a plant anomaly detection system for managing crops incorporating deep learning and drones with various possibilities. In this paper, we develop a system that analyzes images taken by drones and GPS of the drone's movement path and visually displays them on a map. Our system detects plant anomalies with 97% accuracy. The system is expected to enable efficient crop management at low cost.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.185-186
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• 2023

작물의 병해 감지는 주관적인 관찰과 개인의 경험에 의존하는 전통적인 방법을 사용해왔다. 하지만, 이는 많은 시간이 소요되는 등의 한계를 가지고 있다. 본 논문에서는 모바일 카메라를 활용하여 촬영된 사진을 클라우드와 연동한 객체 검출 기반의 어플리케이션을 제안한다. 따라서, 휴대폰만 있다면 시공간적 제약을 받지 않고, 신속하고 정확하게 병해 검출 결과를 확인할 수 있는 장점이 있다.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• The Plant Pathology Journal
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• v.40 no.2
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• pp.125-138
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• 2024

Citrus yellow vein clearing virus (CYVCV) is a member of the Alphaflexiviridae family that causes yellow vein clearing symptoms on citrus leaves. A total of 118 leaf samples from nine regions of six provinces in Korea were collected from various citrus species in 2020 and 2021. Viral diagnosis using next-generation sequencing and reverse transcription polymerase chain reaction (RT-PCR) identified four viruses: citrus tristeza virus, citrus leaf blotch virus, citrus vein enation virus, and CYVCV. A CYVCV incidence of 9.3% was observed in six host plants, including calamansi, kumquat, Persian lime, and Eureka lemon. Among the citrus infected by CYVCV, only three samples showed a single infection; the other showed a mixed infection with other viruses. Eureka lemon and Persian lime exhibited yellow vein clearing, leaf distortion, and water-soak symptom underside of the leaves, while the other hosts showed only yellowing symptoms on the leaves. The complete genome sequences were obtained from five CYVCV isolates. Comparison of the isolates reported from the different geographical regions and hosts revealed the high sequence identity (95.2% to 98.8%). Phylogenetic analysis indicated that all the five isolates from Korea were clustered into same clade but were not distinctly apart from isolates from China, Pakistan, India, and Türkiye. To develop an efficient diagnosis system for the four viruses, a simultaneous detection method was constructed using multiplex RT-PCR. Sensitivity evaluation, simplex RT-PCR, and stability testing were conducted to verify the multiplex RT-PCR system developed in this study. This information will be useful for developing effective disease management strategies for citrus growers in Korea.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.959-968
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• 2019

In the agricultural industry, wireless sensor network technology has being applied by utilizing various sensors and embedded systems. In particular, a lot of researches are being conducted to diagnose diseases of crops early by using sensor network. There are some difficulties on traditional research how to diagnose crop diseases is not practical for agriculture. This paper proposes the algorithm which enables to investigate and analyze the crop leaf image taken by image camera and detect the infected area within the image. We applied the enhanced k-means clustering method to the images captured at horticulture facility and categorized the areas in the image. Then we used the edge detection and edge tracking scheme to decide whether the extracted areas are located in inside of leaf or not. The performance was evaluated using the images capturing tomato leaves. The results of performance evaluation shows that the proposed algorithm outperforms the traditional algorithms in terms of classification capability.

• Research in Plant Disease
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• v.23 no.3
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• pp.278-282
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• 2017

Maize is one of the most cultivated cereals as a staple food in the world. The harvested maize is mainly stored after drying, but its quality and nutrition could be debased by fungal spoilage and mycotoxin contamination. In this study, we surveyed mycotoxin contamination fungal infection of maize kernels that were stored for almost one year after harvest in 2015. The amount of deoxynivalenol and zearalenone detected were higher than the other mycotoxin, such as aflatoxin, ochratoxin, fumonisin and T-2 toxin. In particular, level of deoxynivalenol was detected as $1200{\pm}610{\mu}g/kg$ in small size kernels, which was four to six times higher than the large and the medium size kernels. Moreover, the amount of deoxynivalenol, zearalenone, and fumonisin were increased with discolored kernels. 10 species including Fusarium spp., Aspergillus spp. and Penicillium spp. were isolated from the maize kernels. F. graminearum was predominant in the discolored kernels with detection rates of 60% (red) and 40% (brown). Our study shows that the mycotoxin contents of stored maize can be increased by discolored maize kernels mixed. Therefore elimination of the contaminated maize kernels will help prevent fungal infection and mycotoxin contamination in stored maize.

• International Journal of Industrial Entomology and Biomaterials
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• v.6 no.1
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• pp.27-31
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• 2003

Infectious flacherie of silkworm Bombyx mori is caused by B. mori infectious flacherie virus (BmIFV) and causes severe crop loss to sericulturists. In the present study, a colloidal textile dye-based dipstick immunoassay is developed for the detection of infectious flacherie in silkworms. Colloidal textile dye (blue D2R) with Aλ$_{max}$ at 620 nm was sensitised with 500 $\mu\textrm{g}$/ml of purified anti-BmIFV IgG. The dye-antibody reagent detects purified antigen up to 10 ng/ml and BmIFV infection in diseased larval extracts $(up to a dilution of {10^-5})$ and faecal matter extracts $(up to a dilution of {10^-2})$ by forming clear blue dot within 30 min. It was observed to be stable for three months period at $4^{\circ}C$. The efficacy of textile dye-based dipstick immunoassay was on pay with HRP-based dipstick immunoassay and fluorescent antibody test, and better than latex agglutination and ouchterlony tests in the detection of BmIFV The dye-based dipstick immunoassay method provides a simple, sensitive and less expensive test for the detection of BmIFV infection in silkworms.s.

• Journal of Microbiology and Biotechnology
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• v.24 no.6
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• pp.732-739
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• 2014

We describe the development of a polymerase chain reaction method for the rapid, precise, and specific detection of the Xanthomonas oryzae pv. oryzae (Xoo) K3a race, the bacterial blight pathogen of rice. The specific primer set was designed to amplify a genomic locus derived from an amplified fragment length polymorphism specific for the K3a race. The 1,024 bp amplicon was generated from the DNA of 13 isolates of Xoo K3a races out of 119 isolates of other races, pathovars, and Xanthomonas species. The assay does not require isolated bacterial cells or DNA extraction. Moreover, the pathogen was quickly detected in rice leaf 2 days after inoculation with bacteria and at a distance of 8 cm from the rice leaf 5 days later. The results suggest that this PCR-based assay will be a useful and powerful tool for the detection and identification of the Xoo K3a race in rice plants as well as for early diagnosis of infection in paddy fields.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.1 no.1
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• pp.83-89
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• 2020

Methods for detecting the presence of genetically modified (GM) crops are evolving to comply with legislation and to enhance monitoring by biotechnology companies and regulators. In order to cover a broad range of detection methods for a new GM crop, conventional multiplex PCR methods are required. Based on the genetic information on three GM alfalfa varieties (J101, J163, and KK179), which were recently approved in South Korea, we developed a fast, reliable, and highly specific multiplex polymerase chain reaction (PCR) method with basic PCR equipment and inexpensive reagents. To validate and verify the newly developed multiplex PCR method, we applied a limit of detection assay and random reference material analysis. We also monitored the unintentional environmental release of GM alfalfa in South Korea by performing the multiplex PCR analysis with 91 feral alfalfa specimens collected from 2000 to 2018. Our methodology is a sensitive, simple, quick, and inexpensive tool for detecting and identifying three GM alfalfa varieties.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.