摘要
温室番茄光合速率的准确预测对于番茄的生长和产量评估具有重要意义。然而,由于温室环境的复杂性和多变性,传统的光合速率预测模型往往难以满足精准预测的需求。因此,为了进一步提高预测模型的准确性和稳定性,本研究提出了一种基于多模型融合策略的温室番茄光合速率预测方法。首先,采集温湿度、光照强度、CO_(2)浓度不同组合下的番茄光合速率,构建样本集,并采用五折交叉验证法(Cross-Validation)对数据进行预处理。以预处理的数据为基础,分别基于粒子群优化支持向量机(PSO-SVR)、布谷鸟优化极限学习机(CS-ELM)和北方苍鹰优化高斯过程回归(NGO-GPR)算法建立番茄光合速率预测模型对光合速率进行初步预测,然后采用Stacking算法通过基于决策树的集成学习模型(XGBoost)组合各基础模型的预测结果,进而实现多模型融合。仿真分析结果表明,与单一预测模型相比,基于多模型融合的光合速率预测模型充分发挥了各基础模型的优势,可以进一步提高光合速率预测的准确性和稳定性,该模型验证集MAE为0.569 7μmol/(m^(2)·s),RMSE为0.721 4μmol/(m^(2)·s)。因此,本文提出的方法在温室作物光合速率预测方面具有一定的优势,可为温室番茄等作物光环境优化调控提供一定的理论基础和技术支撑。
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/(m^(2)·s),and the RMSE was 0.721 4 μmol/(m^(2)·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.
作者
刘潭
朱洪锐
袁青云
王永刚
张大鹏
丁小明
LIU Tan;ZHU Hongrui;YUAN Qingyun;WANG Yonggang;ZHANG Dapeng;DING Xiaoming(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China;Liaoning Engineering Research Center for Information Technology in Agriculture,Shenyang 110866,China;Academy of Agricultural Planning and Engineering,Ministry of Agriculture and Rural Affairs,Beijing 100125,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2024年第4期337-345,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
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)
辽宁省自然基金指导计划项目(2019-ZD-0720)。
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
greenhouse
tomato
photosynthetic rate prediction
extreme learning machine(ELM)
Gaussian process regression(GPR)
multi-model fusion
