• 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.

• 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.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 1992.12a
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• pp.15-16
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• 1992

1. 연구의 필요성 및 목적 필요성 - 기상변화에 따른 수도생육의 예측을 통한 적절한 Crop management - 수도수확량 예측을 통한 계획생산의 가능 - 최적 물관리를 위한 기초자료제공 목적 수도의 생육 및 수확량을 예측 할 수 있는 생리학적(physiological ) 모형인 SIMRIW을 우리의 기후조건과 수도품종에 적용하여 모형의 매개변수를 보정하고, 모형의 적용성을 검사하는데 있다. (중략)

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.592-598
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• 2012

Forest stand yield and its changes along with 10 thinning scenarios were estimated using a forest stand yield model for six major tree species in Korea, such as Pinus densiflora in Gangwon province, Pinus densiflora in other regions, Pinus koraiensis, Larix leptolepis, Quercus acutissima Carruth, Quercus mongolica. The 10 thinning scenarios were generated based on a number of constraints and assumptions. For instance, it was assumed that thinning is implemented between 15 years and 40 years with 5 year period and its duration should be at least 10 years. Also, the overall removal rate from the thinning treatments was assumed to be not greater than 60%. Under the 10 scenarios, the overall stand yield volumes from thinning and final harvesting were estimated for each species and site index. The results showed that highest yield volumes were obtained for Pinus densiflora in Gangwon province, Pinus koraiensis and Quercus mongolica when 30% of basal areas were thinned at 20 and 40 years, while highest yield volumes were obtained for Pinus densiflora in other regions and Larix leptolepis when 20% of basal areas were thinned at 20, 30 and 40 years. Those two scenarios gave the same amount of highest yield volume for Quercus acutissima Carruth. Also the results indicated that thinning treatment is effective to increase overall stand yield volume and its effects are larger with a higher site index. The largest thinning effects were found in Pinus densiflora in Gangwon province (28%) and Larix leptolepis (25%), while limited in Pinus koraiensis (12%). The forest stand yield model, used in this research, could be an effective tool for estimating the stand dynamics with various thinning treatments, but it could be improved in a further research that validates its applicability in the field.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• 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.

• 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.

• Journal of Bio-Environment Control
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• v.8 no.3
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• pp.164-171
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• 1999

Tomato yield and harvest date were analyzed to examine the productivity of Single Truss Tomato Production System(STTPS) for four regions in Korea. It was found that the solar radiation was not sufficient to get the maximum tomato yield during the low light seasons. The difference of total annual yield between Suwon and Jinju regions was about 12kg.m$^{-2}$ . These results indicate that supplemental lights are needed to increase the yield. The availability of natural light should be considered in deciding the locations of tomato greenhouses. The harvest date could be adjusted by using supplemental lighting. The development and implementation of the lighting control strategies are required for reducing electricity expense.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2015.08a
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• pp.94-97
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• 2015

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• v.10 no.2
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• pp.362-365
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• 2005