农业机械学报2024，Vol.55Issue(4)：337-345,9.DOI:10.6041/j.issn.1000-1298.2024.04.033
基于多模型融合策略的温室番茄光合速率预测方法
Prediction of Photosynthetic Rate of Greenhouse Tomatoes Based on Multi-model Fusion Strategy
摘要
Abstract
Accurately predicting the photosynthetic rate of greenhouse tomatoes is crucial for evaluating their growth and yield.However,due to the complexity and variability of the greenhouse environments,traditional photosynthetic rate prediction models often fail to meet the demand of precise prediction.To address this issue and enhance the accuracy and stability of prediction model,a multi-model fusion strategy for predicting the photosynthetic rate of greenhouse tomatoes was proposed.Initially,the photosynthetic rate of tomato was collected under various combinations of temperature,humidity,light intensity,and carbon dioxide concentration,and a sample set was constructed.The data was preprocessed by using five-fold cross-validation method.Based on preprocessed data,prediction models for tomato photosynthetic rate were established by using particle swarm optimization-support vector regression(PSO-SVR),cuckoo search optimization-extreme learning machine(CS-ELM),and northern goshawk optimization-Gaussian process regression(NGO-GPR)algorithms,and preliminary predictions were made.Next,the Stacking algorithm was used to combine the predictions of the basic models through training an ensemble tree meta-model(XGBoost),thereby achieving multi-model fusion.The results of simulation analysis demonstrated that compared with a single prediction model,the photosynthetic rate prediction model based on multi-model fusion effectively utilized the advantages of the basic models,enhancing the accuracy and stability of predicting photosynthetic rate.The MAE of the validation set for the model was 0.569 7 µmol/(m2·s),and the RMSE was 0.721 4 pmol/(m2·s).Therefore,the method proposed had significant advantages in predicting the photosynthetic rate of greenhouse crops,and can provide theoretical basis and technical support for the management and control of the light environment of greenhouse tomatoes and other crops.关键词
温室/番茄/光合速率预测/极限学习机/高斯过程回归/多模型融合Key words
greenhouse/tomato/photosynthetic rate prediction/extreme learning machine(ELM)/Gaussian process regression(GPR)/multi-model fusion分类
农业科技引用本文复制引用
刘潭,朱洪锐,袁青云,王永刚,张大鹏,丁小明..基于多模型融合策略的温室番茄光合速率预测方法[J].农业机械学报,2024,55(4):337-345,9.基金项目
辽宁省教育厅面上项目(LJKMZ20221035、LJKZ0683)、辽宁省科技厅面上项目(2023-MS-212)、国家自然科学基金项目(32001415、61673281)和辽宁省自然基金指导计划项目(2019-ZD-0720) （LJKMZ20221035、LJKZ0683）
