基于SMA-SVM模型的茎流速率预测温室西瓜蒸腾量  

Prediction of watermelon transpiration in a greenhouse considering the stem flow rate based on the SMA-SVM model

作　　者：张静 郭俊先 刘湘江 柴扬帆 ZHANG Jing;GUO Junxian;LIU Xiangjiang;CHAI Yangfan(College of Mechanical and Electrical Engineering,Xinjiang Agricultural University,Urumqi 830052,China;College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310000,China)

机构地区：[1]新疆农业大学机电工程学院,乌鲁木齐830052 [2]浙江大学生物系统工程与食品科学学院,杭州310000

出　　处：《新疆农业科学》2024年第10期2434-2443,共10页Xinjiang Agricultural Sciences

基　　金：新疆维吾尔自治区重点研发计划项目“农业传感器与智能感知技术及产品研究开发”(2022B02049-1)。

摘　　要：【目的】基于SMA-SVM模型预测温室西瓜需水量。【方法】以西瓜茎流速率与气象因子结合的作为特征变量作为模型输入,建立黏菌算法(Slime mold algorithm,SMA)优化的支持向量机(Support vector machine,SVM)的温室西瓜蒸腾量预测模型。【结果】气象因子与茎流速率共同作为输入要比气象因子单独作为模型输入的蒸腾量预测精度更高,且通过SMA优化后的SVM预测模型预测效果最好。【结论】茎流速率的SMA-SVM蒸腾预测模型在西瓜三个时期的R2和RMSE分别为0.83、0.87、0.92和0.38、0.31和0.15;模型预测值与实际值接近,预测结果可靠。【Objective】To accurately predict the water demand of greenhouse watermelons.【Methods】A greenhouse watermelon transpiration prediction model was proposed by using a combination of watermelon stem flow rate and meteorological factors as feature variables as model inputs,and a Support Vector Machine(Support Vector Machine,SVM)was established and optimized by slime mold algorithm(slime mold algorithm,SMA).【Results】The experimental results showed that the combined use of meteorological factors and stem flow rate as inputs resulted in higher accuracy in predicting transpiration than using meteorological factors alone as model inputs,and the SVM prediction model optimized by SMA had the best prediction performance.【Conclusion】The R2 and RMSE of the SMA-SVM transpiration prediction model considering stem flow rate in watermelon at three stages are 0.83,0.87,0.92,and 0.38,0.31,and 0.15,respectively and the predicted values of the model are close to the actual values,and the predicted results are reliable.

关 键 词：支持向量机 茎流速率 蒸腾量 预测 

分 类 号：S627[农业科学—园艺学]