Forecast and Control of China's Grain Yield Based on Big Data and BP Neural Network in the Context of Sustainable Agriculture

DOI

10.2991/978-94-6463-056-5_10How to use a DOI?

Keywords

Agricultural Economics; Machine Learning; Neural Network; Grain Yield; Sustainable Agriculture; Grain Yield Forecasting

Abstract

In actual agricultural production, many factors affect grain yield. Among them, non-linear factors such as climate, capital, land directly affect farmers' enthusiasm for production and significantly impact production forecasts. Based on the BP neural network, this paper considers the influence of various non-linear factors on grain yield. It then establishes models through machine learning to realize grain yield forecasting and management by using different economic factors. It provides a helpful reference for formulating macroeconomic policies, sustainable agricultural policies, and regulating grain yield.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haoran Wang
AU  - Zhengxian Chen
PY  - 2022
DA  - 2022/12/29
TI  - Forecast and Control of China's Grain Yield Based on Big Data and BP Neural Network in the Context of Sustainable Agriculture
BT  - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
PB  - Atlantis Press
SP  - 58
EP  - 65
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-056-5_10
DO  - 10.2991/978-94-6463-056-5_10
ID  - Wang2022
ER  -