Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning
To further improve the simulation and estimation accuracy of the supply and demand process of agricultural products, a large number of agricultural data at the national and provincial levels since 1980 were used as the basic research sample, including production, planted area, food consumption, indu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Smart Agriculture
2022-06-01
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| Series: | 智慧农业 |
| Subjects: | |
| Online Access: | http://www.smartag.net.cn/article/2022/2096-8094/2096-8094-2022-4-2-174.shtml |
| _version_ | 1811313711774695424 |
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| author | ZHUANG Jiayu XU Shiwei LI Yang XIONG Lu LIU Kebao ZHONG Zhiping |
| author_facet | ZHUANG Jiayu XU Shiwei LI Yang XIONG Lu LIU Kebao ZHONG Zhiping |
| author_sort | ZHUANG Jiayu |
| collection | DOAJ |
| description | To further improve the simulation and estimation accuracy of the supply and demand process of agricultural products, a large number of agricultural data at the national and provincial levels since 1980 were used as the basic research sample, including production, planted area, food consumption, industrial consumption, feed consumption, seed consumption, import, export, price, GDP, population, urban population, rural population, weather and so on, by fully considering the impact factors of agricultural products such as varieties, time, income and economic development, a multi-agricultural products supply and demand forecasting model based on long short-term memory neural network (LSTM) was constructed in this study. The general thought of supply and demand forecasting model is packaging deep neural network training model as an I/O-opening modular model, reserving control interface for input of outside data, and realizing the indicators forecasting of supply and demand and matrixing of balance sheet. The input of model included forecasting balance sheet data of agricultural products, annual price data, general economic data, and international currency data since 2000. The output of model was balance sheet data of next decade since forecasting time. Under the premise of fully considering the mechanical constraints, the model used the advantages of deep learning algorithms in nonlinear model analysis and prediction to analyze and predict supply and demand of 9 main types of agricultural products, including rice, wheat, corn, soybean, pork, poultry, beef, mutton, and aquatic products. The production forecast results of 2019－2021 based on this model were compared and verified with the data published by the National Bureau of Statistics, and the mean absolute percentage error was 3.02%, which meant the average forecast accuracy rate of 2019－2021 was 96.98%. The average forecast accuracy rate was 96.10% in 2019, 98.26% in 2020, and 96.58% in 2021, which shows that with the increase of sample size, the prediction effect of intelligent learning model would gradually get better. The forecasting results indicate that the multi-agricultural supply and demand prediction model based on LSTM constructed in this study can effectively reflect the impact of changes in hidden indicators on the prediction results, avoiding the uncontrollable error introduced by manual experience intervention. The model can provide data production and technical support such as market warning, policy evaluation, resource management and public opinion analysis for agricultural production and management and macroeconomic regulation, and can provide intelligent technical support for multi-regional and inter-temporal agricultural outlook work by monitoring agricultural operation data in a timely manner. |
| first_indexed | 2024-04-13T10:59:39Z |
| format | Article |
| id | doaj.art-2e2afdc47e0241feb45e2d240b3f39d3 |
| institution | Directory Open Access Journal |
| issn | 2096-8094 |
| language | English |
| last_indexed | 2024-04-13T10:59:39Z |
| publishDate | 2022-06-01 |
| publisher | Editorial Office of Smart Agriculture |
| record_format | Article |
| series | 智慧农业 |
| spelling | doaj.art-2e2afdc47e0241feb45e2d240b3f39d32022-12-22T02:49:27ZengEditorial Office of Smart Agriculture智慧农业2096-80942022-06-014217418210.12133/j.smartag.SA202203013SA202203013Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep LearningZHUANG Jiayu0XU Shiwei1LI Yang2XIONG Lu3LIU Kebao4ZHONG Zhiping5Agricultural Information Institute of CAAS, Beijing 100081, ChinaAgricultural Information Institute of CAAS, Beijing 100081, ChinaInstitute of Agricultural Remote Sensing and Information of HAAS, Harbin 150086, ChinaAgricultural Information Institute of CAAS, Beijing 100081, ChinaInstitute of Agricultural Remote Sensing and Information of HAAS, Harbin 150086, ChinaAgricultural Information Institute of CAAS, Beijing 100081, ChinaTo further improve the simulation and estimation accuracy of the supply and demand process of agricultural products, a large number of agricultural data at the national and provincial levels since 1980 were used as the basic research sample, including production, planted area, food consumption, industrial consumption, feed consumption, seed consumption, import, export, price, GDP, population, urban population, rural population, weather and so on, by fully considering the impact factors of agricultural products such as varieties, time, income and economic development, a multi-agricultural products supply and demand forecasting model based on long short-term memory neural network (LSTM) was constructed in this study. The general thought of supply and demand forecasting model is packaging deep neural network training model as an I/O-opening modular model, reserving control interface for input of outside data, and realizing the indicators forecasting of supply and demand and matrixing of balance sheet. The input of model included forecasting balance sheet data of agricultural products, annual price data, general economic data, and international currency data since 2000. The output of model was balance sheet data of next decade since forecasting time. Under the premise of fully considering the mechanical constraints, the model used the advantages of deep learning algorithms in nonlinear model analysis and prediction to analyze and predict supply and demand of 9 main types of agricultural products, including rice, wheat, corn, soybean, pork, poultry, beef, mutton, and aquatic products. The production forecast results of 2019－2021 based on this model were compared and verified with the data published by the National Bureau of Statistics, and the mean absolute percentage error was 3.02%, which meant the average forecast accuracy rate of 2019－2021 was 96.98%. The average forecast accuracy rate was 96.10% in 2019, 98.26% in 2020, and 96.58% in 2021, which shows that with the increase of sample size, the prediction effect of intelligent learning model would gradually get better. The forecasting results indicate that the multi-agricultural supply and demand prediction model based on LSTM constructed in this study can effectively reflect the impact of changes in hidden indicators on the prediction results, avoiding the uncontrollable error introduced by manual experience intervention. The model can provide data production and technical support such as market warning, policy evaluation, resource management and public opinion analysis for agricultural production and management and macroeconomic regulation, and can provide intelligent technical support for multi-regional and inter-temporal agricultural outlook work by monitoring agricultural operation data in a timely manner.http://www.smartag.net.cn/article/2022/2096-8094/2096-8094-2022-4-2-174.shtmldeep learningsupply and demand forecasting modellstmrnnagricultural productionagricultural outlook |
| spellingShingle | ZHUANG Jiayu XU Shiwei LI Yang XIONG Lu LIU Kebao ZHONG Zhiping Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning 智慧农业 deep learning supply and demand forecasting model lstm rnn agricultural production agricultural outlook |
| title | Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning |
| title_full | Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning |
| title_fullStr | Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning |
| title_full_unstemmed | Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning |
| title_short | Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning |
| title_sort | supply and demand forecasting model of multi agricultural products based on deep learning |
| topic | deep learning supply and demand forecasting model lstm rnn agricultural production agricultural outlook |
| url | http://www.smartag.net.cn/article/2022/2096-8094/2096-8094-2022-4-2-174.shtml |
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