• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.80-80
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• 2022

Estimation of heading date of paddy rice is laborious and time consuming. Therefore, automatic estimation of heading date of paddy rice is highly essential. In this experiment, deep learning classification models were used to classify two difference categories of rice (vegetative and reproductive stage) based on the panicle initiation of paddy field. Specifically, the dataset includes 444 slanted view images belonging to two categories and was then expanded to include 1,497 images via IMGAUG data augmentation technique. We adopt two transfer learning strategies: (First, used transferring model weights already trained on ImageNet to six classification network models: VGGNet, ResNet, DenseNet, InceptionV3, Xception and MobileNet, Second, fine-tuned some layers of the network according to our dataset). After training the CNN model, we used several evaluation metrics commonly used for classification tasks, including Accuracy, Precision, Recall, and F1-score. In addition, GradCAM was used to generate visual explanations for each image patch. Experimental results showed that the InceptionV3 is the best performing model in terms of the accuracy, average recall, precision, and F1-score. The fine-tuned InceptionV3 model achieved an overall classification accuracy of 0.95 with a high F1-score of 0.95. Our CNN model also represented the change of rice heading date under different date of transplanting. This study demonstrated that image based deep learning model can reliably be used as an automatic monitoring system to detect the heading date of rice crops using CCTV camera.

• 농업과학연구
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• 제43권4호
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• pp.537-546
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• 2016

The development of models for agriculture systems, especially for crop production, has supported the prediction of crop yields under various environmental change scenarios and the selection of better crop species or cultivar. Crop models could be used as tools for supporting reasonable nutrient management approaches for agricultural land. This paper outlines the simplified structure of main crop models (crop growth model, crop-soil model, and crop-soil-environment model) frequently used in agricultural systems and shows diverse application of their simulated results. Crop growth models such as LINTUL, SUCROS, could provide simulated data for daily growth, potential production, and photosynthesis assimilate partitioning to various organs with different physiological stages, and for evaluating crop nutrient demand. Crop-Soil models (DSSAT, APSIM, WOFOST, QUEFTS) simulate growth, development, and yields of crops; soil processes describing nutrient uptake from root zone; and soil nutrient supply capability, e.g., mineralization/decomposition of soil organic matter. The crop model built for the DSSAT family software has limitations in spatial variability due to its simulation mechanism based on a single homogeneous field unit. To introduce well-performing crop models, the potential applications for crop-soil-environment models such as DSSAT, APSIM, or even a newly designed model, should first be compared. The parameterization of various crops under different cultivation conditions like those of intensive farming systems common in Korea, shortened crop growth period, should be considered as well as various resource inputs.

• 한국유기농업학회지
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• 제23권1호
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• pp.31-42
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• 2015

This thesis has analyzed the determination factor for the crop insurance of rice focused on paddy rice. The analysis on each farmer has been used with integrated probit model & random effects probit model. It has shown in the analysis result of determination factor for buying the crop insurance of paddy rice farmer through integrated probit model & random effects probit model that the higher age, degree of education, cultivated area, and amount of received insurance money and the lower in a number of family member have revealed the higher possibility to buy the crop insurance in the integrated probit model. While the random effects probit model has shown a higher possibility to buy the crop insurance as the higher age, cultivated area, and amount of received insurance money.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2019년도 추계학술대회
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• pp.19-19
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• 2019

It is necessary to maintain stable crop productions under the unsuitable environments, because the drought and flood may be frequently caused by the global warming. Therefore, it is agent to improve the crop growth model corresponded to soil moisture status. Chili pepper (Capsicum annuum) is one of the useful crop in Asia, and then it is affected by change of precipitation in consequence drought and flood occur however crop model to evaluate water stresses on chili pepper is not enough yet. In this study, development of crop model under different soil moisture status was attempted. The experiment was conducted on the slope fields in the greenhouse. The water level was kept at 20cm above the bottom of the container. Habanero (C. chinense) was used as material for crop model. Sap bleeding rate, SPAD value, chlorophyll content, stomatal conductance, leaf water potential, plant height, leaf area and shoot dry weight were measured at 10 days after treatment (DAT) and 13 DAT. Moreover, temperature and RH in the greenhouse, soil volume water contents (VWC) and soil water potential were measured. As a result, VWC showed 4.0% at the driest plot and 31.4% at the wettest plot at 13 DAT. The growth model was calculated using WVC and the growth analysis parameters. It was considered available, because its coefficient of determination showed 0.84 and there are significant relationship based on plants physiology among the parameters and the changes over time. Furthermore, we analyzed the important factors for higher accuracy prediction using multiple regression analysis.

• 한국기후변화학회지
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• 제6권1호
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• pp.21-31
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• 2015

Various crop models have been extensively used for estimation of the crop yields. Compared to the other models, the EPIC model uses a unified approach to simulate more than 100 types of crops. It has been successfully applied in simulating crop yields for various combinations of weather conditions, soil properties, crops, and management schemes in many countries. The objective of this study was to estimate the rice and maize yield in South Korea using the EPIC model. The input datasets for the 30 types in the 11 categories were created for the EPIC model. The EPIC model simulated rice and maize yields. The performance of the EPIC model was evaluated with the goodness-of-fit measures including Root Mean Square Error (RMSE), Relative Error (RE), Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE), and Pearson Correelation Coefficient (r). The rice yield showed to more high accuracy than maize yield on four type of method without NSEC. Theses results showed that the EPIC model better simulated rice yields than maize yields. The results suggest that the EPIC crop model can be useful to estimate crop yield in South Korea.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.274-276
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• 2003

The traditional crop productivity simulations based on crop models are normally site-specific. To simulate regional crop productivity, the spatial crop model is developed in this study by integrating Geographical Information System (GIS) with Erosion Productivity Impact Calculator (EPIC) model. The integration applied a loose coupling approach. Data are exchanged using ASCII or binary data format between GIS and EPIC model without a common user interface. The spatial EPIC model is conducted to simulate the average corn and wheat productivity of 1980s in North China. The results show that the simulation accuracy of the spatial EPIC model is acceptable. The simulation accuracy can be improved by using the detailed crop management information, such as irrigation, fertilizer and tillage schedule.

• Journal of Biosystems Engineering
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• 제16권2호
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• pp.159-166
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• 1991

A simulation model was developed for analyzing a crop transport system using SLAMSYSTEM. The developed model could predict total delivered amount and delivery rate of a crop under various conditions of transport and analyze the effects of work factors on the efficiency of the crop transport system. A simulation model was also developed to analyze a rice transport system based on the generalized model of the crop transport system.

• 한국작물학회지
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• 제52권4호
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• pp.393-402
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• 2007

Remote sensing data can be integrated into crop models, making simulation improved. A crop model that uses remote sensing data was evaluated for its capability, which was performed through comparing three different methods of canopy measurement for cotton(Gossypium hirsutum L.). The measurement methods used were leaf area index(LAI), hand-held remotely sensed perpendicular vegetation index(PVI), and satellite remotely sensed PVI. Simulated values of cotton growth and lint yield showed reasonable agreement with the corresponding measurements when canopy measurements of LAI and hand-held remotely sensed PVI were used for model calibration. Meanwhile, simulated lint yields involving the satellite remotely sensed PVI were in rough agreement with the measured lint yields. We believe this matter could be improved by using remote sensing data obtained from finer resolution sensors. The model not only has simple input requirements but also is easy to use. It promises to expand its applicability to other regions for crop production, and to be applicable to regional crop growth monitoring and yield mapping projects.

• 한국농림기상학회지
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• 제14권4호
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• pp.269-276
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• 2012

작물모형 평가에 사용되거나 사용할 수 있는 9가지 지표를 소개하였으며 이들의 특징은 다음과 같다. efficiency of model (EF)와 index of agreement (d)은 dimension이 없고 관측수(n)에 의존적이지 않았으며, dimension에 대해서만 자유로운 것은 relative root mean square error (RRMSE), bias factor (Bf)와 accuracy factor (Af)이다. Root mean sqruar, mean error, mean absolute error들은 관측수와 dimension에 영향을 받기 때문에 판단 시 주의가 필요하다. 따라서 이들의 특징을 파악하여 목적에 맞게 모형의 성능을 파악하여야 한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.126-126
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• 2022

Climate change has continued to impact meteorological factors like rainfall in many countries including Nigeria. Thus, altering the rainfall patterns which subsequently affect the crop yield. Maize is an important cereal grown in northern Nigeria, along with sorghum, rice, and millet. Due to the challenge of water scarcity during the dry season, it has become critical to design appropriate strategies for planning, developing, and management of the limited available water resources to increase the maize yield. This study, therefore, determines the quantity of water required to produce maize from planting to harvesting and the impact of drought on maize during different growth stages in the region. Rainfall data from six rain gauge stations for a period of 36 years (1979-2014) was considered for the analysis. The standardized precipitation and evapotranspiration index (SPEI) is used to evaluate the severity of drought. Using the CROPWAT model, the evapotranspiration was calculated using the Penman-Monteith method, while the crop water requirements (CWRs) and irrigation scheduling for the maize crop was also determined. Irrigation was considered for 100% of critical soil moisture loss. At different phases of maize crop growth, the model predicted daily and monthly crop water requirements. The crop water requirement was found to be 319.0 mm and the irrigation requirement was 15.5 mm. The CROPWAT 8.0 model adequately estimated the yield reduction caused by water stress and climatic impacts, which makes this model appropriate for determining the crop water requirements, irrigation planning, and management.