Supply and Demand Forecasting Model of Multi-Agricultural Products Based on Deep Learning

doi:10.12133/j.smartag.SA202203013

Abstract

Abstract:

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.

Key words: deep learning, supply and demand forecasting model, LSTM, RNN, agricultural production, agricultural outlook

CLC Number:

F323.7

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[J]. Smart Agriculture, 2022, 4(2): 174-182.

Figures/Tables 4

Fig. 1

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Table 1

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	年份	产量	进口量	消费量	食用消费	加工消费	种用及损耗	出口量	结余变化
	2011	2830	0.007	2815	2072	496	248	10	5
	2012	2885	0.003	2867	2119	498	250	10	8
	2013	2906	0.002	2918	2167	500	251	9	-22
历史数据	2014	2930	0.002	2973	2219	502	253	9	-52
	2015	3046	0.002	3032	2326	458	248	10	4
	2016	3161	0.000	3145	2427	467	252	10	5
	2017	3096	0.006	3094	2369	476	249	11	-9
	2018	3128	0.000	3120	2386	483	251	10	-2
	2019	3309	0.002	3296	2542	491	263	10	3
	2020	3468	0.013	3449	2662	516	271	10	9
	2021	3406	0.003	3393	2598	526	269	11	2
	2022	3449	0.003	3438	2623	541	274	11	0
	2023	3488	0.003	3475	2644	552	279	11	2
	2024	3514	0.003	3504	2662	561	282	11	-1
预测数据	2025	3551	0.003	3535	2679	569	287	11	5
	2026	3577	0.003	3554	2689	576	288	11	12
	2027	3592	0.003	3573	2701	583	290	12	7
	2028	3608	0.002	3592	2710	590	292	12	4
	2029	3631	0.002	3612	2719	598	294	12	7
	2030	3650	0.002	3629	2726	608	295	12	9

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