Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 174-182.doi: 10.12133/j.smartag.SA202203013
• Intelligent Management and Control • Previous Articles
ZHUANG Jiayu1,2(), XU Shiwei1,2(
), LI Yang3, XIONG Lu1,2, LIU Kebao3, ZHONG Zhiping1,2
Received:
2022-03-23
Online:
2022-06-30
Published:
2022-08-05
corresponding author:
XU Shiwei
E-mail:zhuangjiayu@caas.cn;xushiwei@caas.cn
CLC Number:
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.
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URL: http://www.smartag.net.cn/EN/10.12133/j.smartag.SA202203013
Table 1
History and forecast balance sheet of egg
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 | 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. |
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