基于机器学习的小麦产量预估模型设计与实现
Design and Implementation of Wheat Yield Estimation Model Based on Machine Learning
樊弋鸣 1王黎光1
作者信息
- 1. 西京学院计算机学院,陕西 西安 710123
- 折叠
摘要
2021 年政府工作报告中提到"十四五"时期,继续保持粮食综合生产能力在13 万亿斤以上,严守18 亿亩耕地红线,实施高标准农田建设工程,确保种源安全.基于物联网大数据的小麦产量预估模型设计与实现,对小麦产量预测背景及意义进行了研究,并对小麦产量预估模型进行技术、经济及操作方面的可行性分析,运用数据可视化技术,对近五年小麦在不同环境下的产量进行直观展现,并据此分析出影响小麦产量的主要因素.同时采用LightGBM(简称:LGB)模型和XG-Boost(简称:XGB)模型作为关键技术,构建小麦产量预估模型,将预估的小麦产量与实际产量进行比对,误差控制在5kg之内,具有良好的预测效果.
Abstract
Based on the Internet of things big data of wheat yield forecast model design and implementation,the wheat yield prediction background and significance of wheat yield prediction are studied in the article;and the technical,economic and operational feasibility of the wheat yield estimation model is analyzed.Using data visual-ization technology,the yield of wheat in different environments in recent five years is directly displayed,and the main factors affecting wheat yield are analyzed accordingly.At the same time,lightgbm(LGB)model and xgboost(XGB)model are used as key technologies to build a wheat yield prediction model.The predicted wheat yield is compared with the actual yield,and the error is controlled within 5kg,which has a good prediction effect.
关键词
产量预测/LGB模型/XGB模型/数据可视化Key words
yield forecast/Light GBM/XGBoost/data visualization引用本文复制引用
出版年
2024
NETL
NSTL
万方数据