• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.269-276
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• 2012

The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.16-18
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• 2011

Evaluation of climate change on the rice productivity was conducted using crop growth simulation model, where Odae, Hwaseong, Ilpum were used as a representative cultivar of early, medium, and medium-late rice maturity type, respectively, and climate change scenario 'A1B' was applied to weather data for future climate change at 57sites. When cropping season was fixed, rice yield decreased by 4~35% as climate change which was caused by poor filled grain ratio with high temperature and low irradiation during grain-filling. When cropping season was changed, rice yield decreased by only 0~5% as climate change which was caused poor filled grain ratio with low irradiation during grain-filling period. However, this irradiation decline was less than when cropping season was fixed. Therefore, we need to develop rice cultivars resistant to low irradiation which can maintain high filled grain ratio under poor irradiation condition, and late maturity rice cultivars whose growing period is longer than the present medium-late maturity type.

• Journal of Climate Change Research
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• v.6 no.1
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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.

• Water Engineering Research
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• v.4 no.3
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• pp.111-126
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• 2003

Meteorological data are often needed to evaluate the long-term effects of proposed hydrologic changes. The evaluation is frequently undertaken using deterministic mathematical models that require daily weather data as input including precipitation amount, maximum and minimum temperature, relative humidity, solar radiation and wind speed. Stochastic generation of the required weather data offers alternative to the use of observed weather records. The precipitation is modeled by a Markov Chain-exponential model. The other variables are generated by multivariate model with means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Ultimately, the objective of this paper is to compare Richardson's model and the improved weather generation model in their ability to provide daily weather data for the crop model to study potential impacts of climate change on the irrigation needs and crop yield. However this paper does not refer to the improved weather generation model and the crop model. The new weather generation model improved will be introduced in the Journal of KWRA.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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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.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.1
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• pp.57-71
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• 2009

Crop-livestock mixed farming systems depend on the efficiency with which nutrients are conserved and recycled. Home-grown forage is used as animal feed and animal excretions are applied to cultivated crop lands as manure. The objective of this study was to develop a mixed farming system model for dairy cattle in Japan. The model consisted of four sub-models: the nutrient requirement model, based on the Japanese Feeding Standards to determine requirements for energy, crude protein, dry matter intake, calcium, phosphorus and vitamin A; the optimum diet formulation model for determining the optimum diets that satisfy nutrient requirements at lowest cost, using linear programming; the herd dynamic model to calculate the numbers of cows in each reproductive cycle; and the whole farm optimization model to evaluate whole farm management from economic and environmental viewpoints and to optimize strategies for the target farm or system. To examine the model' validity, its predictions were compared against best practices for dairy farm management. Sensitivity analyses indicated that higher yielding cows lead to better economic results but higher emvironmental load in dairy cattle systems integrated with forage crop production.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.434-444
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• 2013

In order to evaluate drought risk at upland according to climate change scenario (RCP8.5), we have carried out the simulation using agricultural water balance estimation model, called AFKAE0.5, at 66 weather station sites in 2020, 2046, 2050, 2084, and 2090. Total Drought Risk Index between the first month (f) and last month (l) (TDRI(f/l)) and maximum continuous drought risk index (MCDRI(f/l)) were defined as the index for analyzing pattern and strength of drought simulated by the model. Based on distribution maps of MCDRI (1/12), drought strength was predicted to be most severe in 2084 for all regions. Some regions showed severe risk of drought meaning over 20 days of MCDRI (1/12) in the other years, while MCDRI (1/12) in other regions did not reach 5 days. Even though maximum value of TDRI (1/12) in 2090 was greater than in 2050, more severe drought risk in 2050 than in 2090 was predicted based on MCDRI (4/6). It implies that drought risk should be assessed for each crop with its own growing season.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.1-7
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• 2008

This experiment was carried out to evaluate the agronomic stability of North Korean rice varieties using the statistical model developed by Grafius, Finlay, and Ever hart. The lowest yearly variation based on coefficients of variation was found in Hannam 29 for number of panicles per hill, in Sijoong 9 for number of grains per panicle, in Pyeongyang 3 for ripened grain ratio, in Sijoong 16 for 1,000 grain weight, and in Yeomju 1 for grain yield. By Grafius's model, Pyeongbook 3, Weonsan 66 in early maturing groups and Seohaechalbyeo in medium maturing groups show stable for 3 years. Weonsan 66 in early maturing groups and Seohaechalbyeo in medium maturing groups were found to be highly stable as analyzed by both Finlay and Wilkinson's model and Everhart & Russell's model. With reference to three models, Weonsan 66 was highly stable for 3 years with showing more yield than Odaebyeo in early maturing groups while Seohaechalbyeo was highly stable for 3 years with showing high yield than Hwaseongbyeo in medium maturing groups above $5\;t\;ha^{-1}$ of milled rice respectively.