基于集成学习算法和WOFOST模型的小麦生长模拟分析与产量预测
Simulation analysis and yield prediction of wheat growth based on ensemble learning algorithm and WOFOST model
李博 1张婧婧 1雷嘉诚 1杜云1
作者信息
- 1. 新疆农业大学计算机与信息工程学院/智能农业教育部工程研究中心/新疆农业信息化工程技术研究中心,乌鲁木齐 830052
- 折叠
摘要
针对传统单一作物生长模型和机器学习模型在预测上的限制,将WOFOST模型与灌溉模型结合,利用集成学习算法建立多模型耦合系统(WOFOST耦合模型),选用美国航空航天局(NASA)1990-2020年数据进行模拟试验,选取2006年、2018年展示试验成果.结果表明,WOFOST耦合模型的小麦叶面积指数、总生物量均高于WOFOST模型,WOFOST耦合模型更贴近实际生产活动.耦合算法的MAE、MSE均低于Bagging、Boosting、Stacking算法,分别为2.836、7.581,R2均高于Bagging、Boosting、Stacking算法,高达0.942.WOFOST耦合模型更全面和准确地模拟作物生长状态,提高产量预测的准确性与可信度.
Abstract
In response to the limitations of traditional single crop growth models and machine learning models in prediction,the WO-FOST model was combined with irrigation models,and an ensemble learning algorithm was used to establish a multi model coupling system(WOFOST coupling model),simulated experiments were conducted using data from NASA from 1990 to 2020,and experimen-tal results were presented in 2006 and 2018.The results showed that the leaf area index and total biomass of wheat in the WOFOST coupled model were higher than those in the WOFOST model,and the WOFOST coupled model was closer to actual production activi-ties.The MAE and MSE of the coupled algorithm were lower than those of the Bagging,Boosting,and Stacking algorithms,with values of 2.836 and 7.581,respectively.The R2 was higher than that of the Bagging,Boosting,and Stacking algorithms,with a value as high as 0.942.The WOFOST coupled model provided a more comprehensive and accurate simulation of crop growth status,improving the accuracy and credibility of yield prediction.
关键词
集成学习算法/WOFOST模型/小麦生长/模拟/产量预测/耦合Key words
ensemble learning algorithm/WOFOST model/wheat growth/simulation/yield prediction/coupling引用本文复制引用
基金项目
新疆维吾尔自治区重大科技专项(2022A02011-2)
科技创新2030——"新一代人工智能"重大项目(2022ZD0115805)
出版年
2024
NETL
NSTL
万方数据