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

Fig. 2

Table 1

Fig. 3

References 25

1	农业农村部市场预警专家委员会. 中国农业展望报告(2021—2030)[M]. 北京: 中国农业科学技术出版社, 2021.
2	许世卫, 邸佳颖, 李干琼, 等. 农产品监测预警模型集群构建理论方法与应用[J]. 中国农业科学, 2020, 53(14): 2859-2871.
	XU S, DI J, Li G, et al. The methodology and application of agricultural monitoring and early warning model cluster[J]. Scientia Agricultura Sinica, 2020, 53(14): 2859-2871.
3	ZHUANG J, XU S, LI G, et al. The influence of meteorological factors on wheat and rice yields in China[J]. Crop Science, 2018, 58(3): 837-852.
4	LAUDIEN R, SCHAUBERGER B, MAKOWSKI D, et al. Robustly forecasting maize yields in Tanzania based on climatic predictors[J]. Scientific Reports, 2020, 10: ID 19650.
5	JOHNSON M D, HSIEH W W, CANNON A J, et al. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods[J]. Agricultural and Forest Meteorology, 2016, 218-219: 74-84.
6	EUGENIO F C E, GROHS M, VENANCIO L P, et al. Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery[J]. Remote Sensing Applications, 2020: ID 100397.
7	PAUDEL D, BOOGAARD H, DE WIT A, et al. Machine learning for large-scale crop yield forecasting[J]. Agricultural Systems, 2020, 187: ID 103016.
8	DE WITA, BOOGAARD H, FUMAGALLI D, et al. 25 years of the WOFOST cropping systems model[J]. Agricultural Systems, 2019, 168: 154-167.
9	MASUDA, TADAYOSHI, GOLDSMITH, et al. World soybean production: Area harvested, yield, and long-term projections[J]. International Food & Agribusiness Management Review, 2009, 12(4): 143-161.
10	王桂芝, 胡慧, 陈纪波, 等. 基于BP 滤波的Fourier 模型在粮食产量预测中的应用[J]. 中国农业气象, 2015, 36(4): 472-478.
	WANG G, HU H, CHEN J, et al. Application of Fourier model based on BP filter in crops yield prediction[J]. Chinese Journal of Agrometeorology, 2015, 36(4): 472-478.
11	肖玉, 成升魁, 谢高地, 等. 我国主要粮食品种供给与消费平衡分析[J]. 自然资源学报, 2017, 32(6): 927-936.
	XIAO Y, CHENG S, XIE G, et al. The balance between supply and consumption of the main types of grain in China[J]. Journal of Natural Resources, 2017, 32(6): 927-936.
12	谢高地, 成升魁, 肖玉, 等. 新时期中国粮食供需平衡态势及粮食安全观的重构[J]. 自然资源学报, 2017, 32(6): 895-903.
	XIE G, CHENG S, XIAO Y, et al. The balance between grain supply and demand and the reconstruction of China's food security strategy in the new period[J]. Journal of Natural Resources, 2017, 32(6): 895-903.
13	赵萱, 邵一珊. 我国粮食供需的分析与预测[J]. 农业现代化研究, 2014, 35(3): 277-280.
	ZHAO X, SHAO Y. Analysis and forecast of China's grain supply and demand[J]. Research of Agricultural Modernization, 2014, 35(3): 277-280.
14	刘洋, 罗其友, 周振亚, 等. 我国主要农产品供需分析与预测[J]. 中国工程科学, 2018, 20(5): 120-127.
	LIU Y, LUO Q, ZHOU Z, et al. Analysis and prediction of the supply and demand of China's major agricultural products[J]. Strategic Study of CAE, 2018, 20(5): 120-127.
15	LU W, NING L C, WEN X Q. Modeling the effects of urbanization on grain production and consumption in China[J]. Journal of Integrative Agriculture, 2017, 16(6): 1393-1405.
16	黄季焜. 对近期与中长期中国粮食安全的再认识[J]. 农业经济问题, 2021(1): 19-26.
	HUANG J. Recognition of recent and mid-long term food security in China[J]. Issues in Agricultural Economy, 2021(1): 19-26.
17	陈锡康, 杨翠红. 农业复杂巨系统的特点与全国粮食产量预测研究[J]. 系统工程理论与实践, 2002(6): 108-112.
	CHEN X, YANG C. Characteristic of agricultural complex giant system and national grain output prediction[J]. Systems Engineering-Theory & Practice, 2002(6): 108-112.
18	许世卫. 农业信息分析学[M]. 北京: 高等教育出版社, 2013.
19	高亮之. 农业模型学[M]. 北京: 气象出版社, 2019.
20	王盈旭, 韩红桂, 郭民. 一种基于改进型深度学习的非线性建模方法[J]. 信息与控制, 2018, 47(6): 680-686.
	WANG Y, HAN H, GUO M. A nonlinear modeling method based on improved deep learning[J]. Information and Control, 2018, 47(6): 680-686.
21	DELÉGLISE H, INTERDONATO R, BÉGUÉ A, et al. Food security prediction from heterogeneous data combining machine and deep learning methods[J]. Expert Systems with Applications, 2022, 190: ID 116189.
22	EMERSON R A, DOS REIS J G M, VENDRAMETTO O, et al. Time series prediction with artificial neural networks: An analysis using Brazilian soybean production[J]. Agriculture (Basel), 2020, 10(10): ID 475.
23	SCHWALBERT R A, AMADO T, CORASSA G, et al. Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern brazil[J]. Agricultural and Forest Meteorology, 2020, 284: ID 107886.
24	SHAHHOSSEINI M, HU G, HUBER I, et al. Coupling machine learning and crop modeling improves crop yield prediction in the US corn belt[J]. Scientific Reports, Scientific Reports, 2021, 11(1): 1-15.
25	BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166.

	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

[1]	HE Ruimin, ZHENG Kefeng, WEI Qinyang, ZHANG Xiaobin, ZHANG Jun, ZHU Yihang, ZHAO Yiying, GU Qing. Identification and Counting of Silkworms in Factory Farm Using Improved Mask R-CNN Model [J]. Smart Agriculture, 2022, 4(2): 163-173.
[2]	SHAO Mingyue, ZHANG Jianhua, FENG Quan, CHAI Xiujuan, ZHANG Ning, ZHANG Wenrong. Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases [J]. Smart Agriculture, 2022, 4(1): 29-46.
[3]	YANG Guofeng, HE Yong, FENG Xuping, LI Xiyao, ZHANG Jinnuo, YU Zeyu. Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle [J]. Smart Agriculture, 2022, 4(1): 1-16.
[4]	LI Zhijun, YANG Shenghui, SHI Deshuai, LIU Xingxing, ZHENG Yongjun. Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5 [J]. Smart Agriculture, 2021, 3(2): 100-114.
[5]	FLORES Paulo, ZHANG Zhao. Wheat Lodging Ratio Detection Based on UAS Imagery Coupled with Different Machine Learning and Deep Learning Algorithms [J]. Smart Agriculture, 2021, 3(2): 23-34.
[6]	QIU Wenjie, YE Jin, HU Liangqing, YANG Juan, LI Qili, MO Jianyou, YI Wanmao. Distilled-MobileNet Model of Convolutional Neural Network Simplified Structure for Plant Disease Recognition [J]. Smart Agriculture, 2021, 3(1): 109-117.
[7]	LI Liangde, WANG Xiujuan, KANG Mengzhen, HUA Jing, FAN Menghan. Agricultural Named Entity Recognition Based on Semantic Aggregation and Model Distillation [J]. Smart Agriculture, 2021, 3(1): 118-128.
[8]	GUO Wei, WU Huarui, ZHU Huaji. Design and Application of Facility Greenhouse Image Collecting and Environmental Data Monitoring Robot System [J]. Smart Agriculture, 2020, 2(3): 48-60.
[9]	LI Hualong, LI Miao, ZHAN Kai, LIU Xianwang, YANG Xuanjiang, HU Zelin, GUO Panpan. Construction Method and Performance Test of Prediction Model for Laying Hen Breeding Environmental Quality Evaluation [J]. Smart Agriculture, 2020, 2(3): 37-47.
[10]	WEI Jing, WANG Yuting, YUAN Huizhu, ZHANG Menglei, WANG Zhenying. Identification and Morphological Analysis of Adult Spodoptera Frugiperda and Its Close Related Species Using Deep Learning [J]. Smart Agriculture, 2020, 2(3): 75-85.
[11]	FLORES Paulo, ZHANG Zhao, MATHEW Jithin, JAHAN Nusrat, STENGER John. Distinguishing Volunteer Corn from Soybean at Seedling Stage Using Images and Machine Learning [J]. Smart Agriculture, 2020, 2(3): 61-74.
[12]	Zhou Chengquan, Ye Hongbao, Yu Guohong, Hu Jun, Xu Zhifu. A fast extraction method of broccoli phenotype based on machine vision and deep learning [J]. Smart Agriculture, 2020, 2(1): 121-132.
[13]	Xia Xue, Sun Qixin, Shi Xiao, Chai Xiujuan. Apple detection model based on lightweight anchor-free deep convolutional neural network [J]. Smart Agriculture, 2020, 2(1): 99-110.
[14]	Wu Huarui. Method of tomato leaf diseases recognition method based on deep residual network [J]. Smart Agriculture, 2019, 1(4): 42-49.
[15]	Chen Guifen, Zhao Shan, Cao Liying, Fu Siwei, Zhou Jiaxin. Corn plant disease recognition based on migration learning and convolutional neural network [J]. Smart Agriculture, 2019, 1(2): 34-44.