• Advanced Industrial SCIence
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• v.2 no.3
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• pp.1-7
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• 2023

Recently, it is increasing that a issue of concern about radiation exposure. It affects soil, water, air, crops, etc., and in the long term, environmental pollution and food pollution occur, and it is considered to cause social problems and economic damage. Radiation exposure causes diseases and health problems, but as a method for diagnosing diseases, nuclear medicine tests such as X-ray imaging, CT, and PET-CT are conducted, and radiation isotopes are exposed for the purpose of cancer treatment. A Hungarian case study on radiation in water, particularly drinking water, following the release of radioactive waste from Fukushima, and an examination of the Larsemann Hills area in Antarctica, found that it was within the prescribed radioactivity limits of drinking water recommended by the World Health Organization. We looked at radioprotective agents, focusing on DNA damage, cell and organ damage, and cancer, and also investigated various literatures on ACE inhibitors, antioxidants, and natural substances among restoration materials. Although exposed to radiation in everyday life, the reason why it can be safe is probably because there is a radiation protection material and a recovery material for radiation exposure, so we are trying to find possible materials.

• Microbiology and Biotechnology Letters
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• v.34 no.1
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• pp.63-69
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• 2006

We isolated a bacterium which produces antifungal substances from the Lake of Saimaa soils in Fin-land. The isolated strain was identified as Bacillus sp. and shown a strong antifungal activity on plant pathogenic fungi. Bacillus sp. KMU-1011 produced maximum level of antifungal substances under incubation aerobically at $24^{\circ}C$ for 48 hours in nutrient broth containing 1.0% glucose and 1.0% polypeptone at 180 rpm and initiated pH adjusted to 6.0. Precipitate of culture broth by $30{\sim}60%$ ammonium sulfate precipitation exhibited strong antifungal activity against Botrytis cinerea KACC 40573 by dry cell weight. Chloroform extract of cultured broth also shown fungal growth inhibitory activity against C. gloeosporioides KACC 40804, D. bryoniae KACC 40669, F. oxysporum KACC 40037, F. oxysporum KACC 40052, F. oxysporum f. sp. radicis-lycopersici KACC 40537, F. oxysporum KACC 40902, M. cannonballus KACC 40940, P. cambivora KACC 40160, R. solani AG-1 KACC 40101, R. solani AG-4 KACC 40142, and S. scleotiorum KACC by agar diffusion method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.457-460
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• 2023

본 연구는 시설작물의 병충해 진단을 위해 딥러닝 모델을 응용한 인공지능 서비스 앱, Dr. Vegetable을 제안하고자 한다. 농업 현장에서 숙련된 농부는 한눈에 농작물의 병충해를 판단할 수 있지만 미숙련된 농부는 병충해 피해를 발견하더라도 그 종류와 해결 방법을 찾아내기가 매우 어렵다. 또한 아무리 숙련된 농부라고 할지라도 육안검사만으로 병충해를 조기에 발견하는 것은 쉽지 않다. 한편 시설작물의 경우 병충해에 의한 연쇄피해가 발생할 우려가 있으므로 병충해의 조기 발견 및 방제가 매우 중요하다. 즉, 농부의 경험에 따른 농작물 병해충 진단은 정확성을 장담할 수 없으며 비용과 시간적인 측면에서 위험성이 높다고 할 수 있다. 본 논문에서는 YOLOv5를 활용하여 상추, 고추, 토마토 등 농작물의 병충해를 진단하는 인공지능 서비스를 제안한다. 특히 한국지능정보사회진흥원이 운영하고 있는 AI 통합 플랫폼인 AI 허브에서 제공하는 노지 작물 질병 및 해충 진단 이미지를 사용하여 딥러닝 모델을 학습하였다. 본 연구를 통해 개발된 모바일 어플리케이션을 이용하여 실제 시설농장에서 병충해 진단 서비스를 적용한 결과 약 86%의 정확도, F1 Score 0.84, 그리고 0.98의 mAP 값을 얻을 수 있었다. 본 연구에서 개발한 병충해 진단 딥러닝 모델을 다양한 조도에서 강인하게 동작하도록 개선한다면 농업 현장에서 널리 활용될 수 있을 것으로 기대한다.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Proceedings of the KSCN Conference
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• 2003.05a
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• pp.149-149
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• 2003

토마토는 가지과에 속하는 일년생 작물로써 주로 온대지방에서 재배되며 세계각국에서 해마다 생산량이 증가하고 있고 우리나라에서도 기후풍토가 적합하여 전국적으로 재배되고 있다. 토마토는 특히 vitamin A와 ascorbic acid 가 풍부한 과일로 최근에는 각종 암, 비만, 심장질환 및 만성퇴행성 질환에 미치는 효과 등에 대한 연구가 활발히 진행되고 있다. 최근 우리나라의 모든 연령층에 걸쳐 칼슘 섭취 상태가 양호하지 못한 편으로 2001년도 국민영양조사(보건복지부 2002)에 의하면 우리나라 국민의 하루 평균 칼슘 섭취량이 권장량의 71.0%로 나타났다. (중략)

• THE HEALTH and ORGANIC FARMING
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• no.237
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• pp.1-12
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• 2007

3만2천여 회우동지들의 염원이던 '유기농회관' 준공후 입주준비완료/과대포장된 장밋빛 F T A의 허구성/<암을 이기는 음식>(2) 녹황색 채소/미국, 유기농식품시장 매년 두자릿수 성장/애완동물 사료도 유기농 바람, 리콜 이후 크게 늘어/"친환경유기농업 육성정책"의 성공조건/도시 어린이들에게 친환경농업교육 실시/사과 겹무늬썩음병의 발병생태/미생물을 이용한 질병의 방제/작물건전생육에 토양개량이 무엇보다 중요/염류장해 뿌리혹선충 극복/농업선진화운동본부, 친환경농산물 유통개혁세미나/벼 멀칭재배의 이론과 기술/미국에서도,친환경 웰빙쌀이 '바람몰이'/친환경농업 연수교육/우리의 산야초-범부채/탄질률과 영양주기 이론의 이해/유기질비료의 이해

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.417-419
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• 2022

농축업에 ICT 기술을 접목한 스마트 팜은 생육환경을 자동으로 조절하여 노동력 등을 줄이고도 생산성과 품질을 향상시키는 것이 큰 장점이다. 하지만, 수익으로 이어지는 출하량과 품질 유지를 위해서 병충해에 주의를 기울여야 함은 여전하다. 따라서 토마토 잎 병충해 발생 시, 적절한 대응을 통해 더 큰 피해를 막을 수 있으므로, 초기 증상을 포착하는 기법을 개발한다. 오픈 데이터 셋인 Ai hub 의 시설작물 질병 데이터셋과 추가로 확보한 샘플을 포함해 2 개의 충해, 4 개의 병해에 1,231 장으로 데이터셋을 직접 구성해서 학습했다. 객체 탐지와 세그먼테이션이 동시에 가능하며 작은 병변도 잘 탐지하는 모델을 사용해서 총 6 가지 병충해에 대한 뚜렷한 증상 탐지를 보여주었다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.1
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• pp.30-39
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• 1989

Forage productivity of cropping systems of rye - silage corn, silage corn - oats, silage corn - rape was studied in the south-eastern part of Korea where rice black-streaked dwarf virus(RBSDV) infection of corn are severe. Rye(cv. Paldanghomil) was planted on Oct. 20 of 1986 and harvested 10 times from April 5 to May 5 at the 5-day intervals in 1987, corn (cv. Suweon 19 and Jinjuok) was planted 5 times from April 5 to May 15 at the 10-day intervals in 1987, and oats(cv. Megwiri) and rape (cv. Velox) were planted 4 times from Sept: 4 to 25 at the 7-day intervals and harvested 4 times from Nov. 10 to Dec. 10 at the 10-day intervals in 1987. Considering yield, nutrition value, and in vitro dry matter digestibility (IVDMD), forage productivity of the cropping systems was compared. As harvesting time of rye delayed, plant height, dry matter(DM) yield, percent DM, crude fiber, and digestible DM yield increased, but crude protein, crude fat, and IVDMD decreased. However, nitrogen free extract was not different among the harvesting dates. As planting date of corn delayed, RBSDV infection rate increased. but DM yield of silage decreased. However, silage yield of Jinjuok was higher, but RBSDV infection rate was lower compared with Suweon 19 at all planting dates. DM yield of oats and rape decreased as planting date delayed. However, at Sept. 4 and 11 plantings yield of oats on Nov. 10 was much lower than that of rape, but the differences in yield between two crops decreased with delayed harvesting, and yield was similar on Dec. 10. A cropping system harvesting rye around April 20 and followed by planting corn in late April was best among the rye-corn systems considering yield and nutrition value of both crops. However, among the corn-oats or corn-rape cropping systems early April planting of corn and followed by early Sept. planting of oats or rape showed best results with similar yield potential of the best rye-corn cropping system.