• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.3
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• pp.65-73
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• 2011

기후변화는 에너지 수지와 물 수지의 변화를 초래하여 육상 생물권에 영향을 미칠 것이다. 기온과 강수량의 변화와 대기중의 탄산가스 농도 변화는 작물의 생육환경을 크게 변화시킬 것이다. 본 연구에서는 FAO AquaCrop 모형을 이용하여 기온과 강수량의 변화와 대기중 탄산가스 농도의 변화가 짐바브웨의 옥수수 수확량에 미치는 영향을 분석하였다. 미래 기후 값은 HadCM3 모형 예측 값을 change factor 기법으로 상세화 하였다. 배출 시나리오는 A2와 B2를 선정하였으며 시간대는 2020s, 2050s 및 2080s의 30년 기간을 선정하였다. 기준작물 증발산량은 Penman-Monteith 식으로 산정하였다. 관개용수 공급이 충분한 것으로 가정하고 전통적인 보충관개를 실시하였을 때 기준년도 (1970s)에 비해 옥수수 증발산량은 최대 26 %, 옥수수 잠재 수확량은 최대 93 %까지 증가할 것으로 예측되었으며 물의 생산성은 최대 53 %까지 증가할 것으로 예측되었다.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• Proceedings of the Korean Society of Crop Science Conference
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• 2021.10a
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• pp.58-58
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• 2021

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.415-416
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• 2023

지구온난화와 농업 활동 감소로 인해 농작물 생산량이 줄어드는 추세이다. 4차 산업혁명 시대를 맞아 농업 분야에서도 인공지능 기술을 활용하여 효율적인 작물 재배가 가능해지고 있다. 작물의 수확량을 최고로 끌어올릴 수 있는 시간대별 최적 환경을 알아낼 수 있다면 식물 재배와 관련한 제반 사업에 도움이 될 것이다. 본 연구에서는 LSTM 알고리즘을 이용하여 상추의 일별 중량을 예측하는 인공지능 모델을 생성하였다. 제안하는 AI 예측 모델을 통해, 보다 효율적인 작물 재배가 가능해질 수 있을 것으로 보인다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.99-106
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• 2023

Recently, many studies on crop yield prediction using deep learning technology have been conducted. These algorithms have difficulty constructing a linear map between input data sets and crop prediction results. Furthermore, implementation of these algorithms positively depends on the rate of acquired attributes. Deep reinforcement learning can overcome these limitations. This paper analyzes the performance of DQN, Double DQN and Dueling DQN to improve crop yield prediction. The DQN algorithm retains the overestimation problem. Whereas, Double DQN declines the over-estimations and leads to getting better results. The proposed models achieves these by reducing the falsehood and increasing the prediction exactness.

• 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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.07a
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• pp.249-254
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• 2002

온실에서 환기는 외기와의 공기교환을 통한 온도 및 습도의 조절뿐만 아니라 이산화탄소 등의 가스농도를 조절함으로써 온실내 공기의 쾌적성 확보와 실내기류의 형성으로 인한 작물의 생육촉진에도 중요한 역할을 담당한다. 그러므로, 작물생육환경의 최적화를 통한 품질향상 및 수확량 증대를 목적으로 하는 온실재배에 있어서 환기특성 분석 및 공기유동 예측은 가장 기본적인 설계요소라 할 수 있다. (중략)

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.284-291
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• 2010

A simulation modeling for predicting the harvesting date with high potato solids consists of development of mathematical models. The mathematical model on potato growth and its development should be obtained by using agricultural elements which analyze relations of solar radiation quantity, temperature, photon quantity, carbon dioxide exchange rate, water stress and loss, relative humidity, light intensity, and wind etc. But more reliable way to predict harvesting date against climatic change employs in vivo energy consumption for growth and induction shape in a slight environmental adaptation. Therefore, to calculate in vivo energy loss, we take a concept of estimate of the amount of basal metabolism in each tuber on the basis of $Wm={\int}^m_tf(x)dt$ and $Tp=\frac{Tm{\cdot}Wm^{Tp}}{Wm^{Tm}}$. In the validation experiments, results of measuring solid accumulation of potato harvested at simulated date agreed fairly well with the actual measured values in each regional field during the growth period of 2005-2009. The calculation method could be used to predict an appropriate harvesting date for a production of high potato solids according to weather conditions.