• 전자공학회논문지 IE
• /
• v.43 no.4
• /
• pp.13-20
• /
• 2006

In the advanced country, It is forecasting farm prices using intelligence style of farming technique. In our country, It is offering basis research to prevent the prices rising and falling, But, It is impossible that no one can predict exactly for farming price. In this paper to improve forecasting farming price using neural network as a preprocessing. Also, we developed a fuzzy algorithm for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results preyed reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.37-38
• /
• 2022

농산물의 산지 가격이나 도매가격이 등락하면, 즉시 또는 일정한 시차 이후에 소비자가격도 등락한다. 본 논문에서는 선형회귀모델을 통해 쌀 가격을 예측하고 쌀 가격에 영향을 미치는 날씨의 시기를 찾아보고자 한다. 이에 따라 KAMIS, 기상자료개방포털, KOSIS에서 수집한 날씨, 생산량, 그리고 소비자물가 등락률 데이터를 이용하여 쌀 가격 예측을 수행하고, 날씨 데이터와 쌀 가격 데이터의 날짜 간격을 두어 날씨가 쌀 가격에 영향을 미치는 시기를 알아보았다. 모델 평가 결과, 2개월 간격을 두고 예측한 RMSE가 164.135로 가장 큰 영향을 미쳤다. 본 연구를 기반으로 향후 다른 농산물의 가격 예측도 가능할 것이며 농산물에 영향을 미치는 변수의 시기도 예측할 수 있을 것으로 기대한다.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.83-91
• /
• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• The Journal of the Korea Contents Association
• /
• v.18 no.11
• /
• pp.416-429
• /
• 2018

Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model's accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.

• Journal of Advanced Technology Convergence
• /
• v.3 no.2
• /
• pp.17-23
• /
• 2024

In this paper, we analyzed the impact of temperature and precipitation on agricultural product prices and predicted the prices of major agricultural products using TensorFlow. As a result of the analysis, the rise in temperature and precipitation had a significant effect on the rise in prices of cabbage, radish, green onion, lettuce, and onion. In particular, prices rose sharply when temperature and precipitation increased simultaneously. The prediction model was useful in predicting agricultural product price changes due to climate change. Through this, agricultural producers and consumers can prepare for climate change and prepare response strategies to price fluctuations. The paper can contribute to understanding the impact of climate change on agricultural product prices and exploring ways to increase the stability and sustainability of agricultural product markets. In addition, it provides important data to increase agricultural sustainability and ensure economic stability in the era of climate change. The research results will also provide useful insights to policy makers and can contribute to establishing effective agricultural policies in response to climate change.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.710-712
• /
• 2022

Agricultural products take a large part of the wholesale and retail market as a necessity for daily consumption, and the consumption and price of agricultural products affect the supply and demand of agricultural products, consumer spending, and agricultural household income. Therefore, in this study, It was conducted on unit price prediction using LSTM to trade agricultural products, weather observation, import and export performance and fresh food index data. In order to study the supply and demand management of agricultural products and appropriate prices in the wholesale and retail market, unit prices are predicted for garlic, cabbage, and onions with high consumer price index weights among items subject to vegetable price stabilizers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.412-414
• /
• 2017

It is important to predict price of agricultural products accurately to government, local government, bodies in charge of agriculture. Production and shipping of agricultural products are affected by weather condition significantly. In this research, prediction model of a Chinese cabbage which is highly sensitive to weather condition is proposed using deep learning technique. After performance of proposed model and a model of previous research is compared, superiority of proposed model is proved.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.2
• /
• pp.201-207
• /
• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Journal of the Korea Computer Industry Society
• /
• v.6 no.2
• /
• pp.241-248
• /
• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Journal of Convergence for Information Technology
• /
• v.12 no.3
• /
• pp.1-10
• /
• 2022

Vegetables such as cabbage are greatly affected by natural disasters, so price fluctuations increase due to disasters such as heavy rain and disease, which affects the farm economy. Various efforts have been made to predict the price of agricultural products to solve this problem, but it is difficult to predict extreme price prediction fluctuations. In this study, cabbage prices were analyzed using the ensemble Voting technique, a method of determining the final prediction results through various classifiers by combining a single classifier. In addition, the results were compared with LSTM, a time series analysis method, and XGBoost and RandomForest, a boosting technique. Daily data was used for price data, and weather information and price index that affect cabbage prices were used. As a result of the study, the RMSE value showing the difference between the actual value and the predicted value is about 236. It is expected that this study can be used to select other time series analysis research models such as predicting agricultural product prices