Forecast and Control of China's Grain Yield Based on Big Data and BP Neural Network in the Context of Sustainable Agriculture
- 10.2991/978-94-6463-056-5_10How to use a DOI?
- Agricultural Economics; Machine Learning; Neural Network; Grain Yield; Sustainable Agriculture; Grain Yield Forecasting
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.
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Cite this article
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 -