基于SMA-SVM模型的茎流速率预测温室西瓜蒸腾量

doi:10.6048/j.issn.1001-4330.2024.10.011

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (10): 2434-2443.DOI: 10.6048/j.issn.1001-4330.2024.10.011

基于SMA-SVM模型的茎流速率预测温室西瓜蒸腾量

张静¹(), 郭俊先¹(), 刘湘江², 柴扬帆²

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

收稿日期:2024-04-11 出版日期:2024-10-20 发布日期:2024-11-07
通信作者: 郭俊先(1975-),男,新疆巴里坤人,教授,博士,硕士生/博士生导师,研究方向为农业信息智能感知技术与装备,(E-mail)junxianguo@163.com
作者简介:张静(1996-),女,山东菏泽人,硕士研究生,研究方向为作物茎流,(E-mail)2232282799@qq.com
基金资助:
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)

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

ZHANG Jing¹(), GUO Junxian¹(), LIU Xiangjiang², CHAI Yangfan²

1. College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310000, China

Received:2024-04-11 Published:2024-10-20 Online:2024-11-07
Correspondence author: GUO Junxian (1975-), male, from Balikun, Xinjiang, professor, doctoral supervisor, research direction: agricultural information intelligent perception technology and equipment,(E-mail)junxianguo@163.com
Supported by:
Key R&D Program Project of Xinjiang Uygur Autonomous Region "R & D of Agricultural Sensors and Intelligent Perception Technology and Products"(2022B02049-1)

摘要/Abstract

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。

关键词: 支持向量机; 茎流速率; 蒸腾量; 预测

Abstract:

【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.

Key words: support vector machine; stem flow rate; transpiration rate; prediction

中图分类号:

S627

张静, 郭俊先, 刘湘江, 柴扬帆. 基于SMA-SVM模型的茎流速率预测温室西瓜蒸腾量[J]. 新疆农业科学, 2024, 61(10): 2434-2443.

ZHANG Jing, GUO Junxian, LIU Xiangjiang, CHAI Yangfan. Prediction of watermelon transpiration in a greenhouse considering the stem flow rate based on the SMA-SVM model[J]. Xinjiang Agricultural Sciences, 2024, 61(10): 2434-2443.

图/表 9

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时期 Period	影响因子 Impact factor	回归方程 Regression equation	R²
幼苗期 Seedling stage	S	Y=0.06S+0.097	0.64
	T、H、V	Y=0.059T-0.032H-0.005V+1.208	0.58
	S、T、H、V	Y=0.058S+0.147T-0.053H-1.509V+1.630	0.82
伸蔓期 Stretching stage	S	Y=0.055S+0.037	0.65
	T、H、V	Y=0.054T+0.111H+0.016V-3.603	0.59
	S、T、H、V	Y=0.051S-0.222T+0.087H+2.385V-0.839	0.80
膨果期 Swelling stage	S	Y=0.62x-0.344	0.70
	T、H、V	Y=0.057T-0.024H-0.001V+0.323	0.61
	S、T、H、V	Y=0.064S-0.053T+0.006H+0.229V+0.273	0.83

时期 Period	影响因子 Impact factor	回归方程 Regression equation	R²
幼苗期 Seedling stage	S	Y=0.06S+0.097	0.64
	T、H、V	Y=0.059T-0.032H-0.005V+1.208	0.58
	S、T、H、V	Y=0.058S+0.147T-0.053H-1.509V+1.630	0.82
伸蔓期 Stretching stage	S	Y=0.055S+0.037	0.65
	T、H、V	Y=0.054T+0.111H+0.016V-3.603	0.59
	S、T、H、V	Y=0.051S-0.222T+0.087H+2.385V-0.839	0.80
膨果期 Swelling stage	S	Y=0.62x-0.344	0.70
	T、H、V	Y=0.057T-0.024H-0.001V+0.323	0.61
	S、T、H、V	Y=0.064S-0.053T+0.006H+0.229V+0.273	0.83

模型 Model	时期 Period	决定系数Coefficient of determination(R²)				均方根误差Root mean square error(RMSE)
模型 Model	时期 Period	训练集 Training set (h、t、v)	训练集 Training set (s、h、t、v)	测试集 Test set (h、t、v)	测试集 Test set (s、h、t、v)	训练集 Training set (h、t、v)	训练集 Training set (s、h、t、v)	测试集 Test set (h、t、v)	测试集 Test set (s、h、t、v)
SVM	幼苗	0.52	0.77	0.53	0.74	0.57	0.46	0.61	0.54
	伸蔓	0.61	0.82	0.58	0.76	0.63	0.43	0.56	0.48
	膨果	0.66	0.86	0.64	0.81	0.49	0.29	0.47	0.32
SMA-SVM	幼苗	0.59	0.88	0.62	0.83	0.54	0.34	0.52	0.38
	伸蔓	0.67	0.91	0.74	0.87	0.49	0.26	0.46	0.31
	膨果	0.72	0.94	0.76	0.92	0.44	0.13	0.34	0.15
GWO-SVM	幼苗	0.58	0.79	0.59	0.77	0.55	0.41	0.54	0.47
	伸蔓	0.75	0.86	0.64	0.83	0.46	0.36	0.55	0.41
	膨果	0.78	0.92	0.73	0.87	0.37	0.21	0.43	0.24
PSO-SVM	幼苗	0.61	0.80	0.67	0.77	0.52	0.37	0.51	0.42
	伸蔓	0.63	0.89	0.78	0.85	0.56	0.29	0.56	0.34
	膨果	0.69	0.87	0.66	0.83	0.48	0.26	0.44	0.33

模型 Model	时期 Period	决定系数Coefficient of determination(R²)				均方根误差Root mean square error(RMSE)
模型 Model	时期 Period	训练集 Training set (h、t、v)	训练集 Training set (s、h、t、v)	测试集 Test set (h、t、v)	测试集 Test set (s、h、t、v)	训练集 Training set (h、t、v)	训练集 Training set (s、h、t、v)	测试集 Test set (h、t、v)	测试集 Test set (s、h、t、v)
SVM	幼苗	0.52	0.77	0.53	0.74	0.57	0.46	0.61	0.54
	伸蔓	0.61	0.82	0.58	0.76	0.63	0.43	0.56	0.48
	膨果	0.66	0.86	0.64	0.81	0.49	0.29	0.47	0.32
SMA-SVM	幼苗	0.59	0.88	0.62	0.83	0.54	0.34	0.52	0.38
	伸蔓	0.67	0.91	0.74	0.87	0.49	0.26	0.46	0.31
	膨果	0.72	0.94	0.76	0.92	0.44	0.13	0.34	0.15
GWO-SVM	幼苗	0.58	0.79	0.59	0.77	0.55	0.41	0.54	0.47
	伸蔓	0.75	0.86	0.64	0.83	0.46	0.36	0.55	0.41
	膨果	0.78	0.92	0.73	0.87	0.37	0.21	0.43	0.24
PSO-SVM	幼苗	0.61	0.80	0.67	0.77	0.52	0.37	0.51	0.42
	伸蔓	0.63	0.89	0.78	0.85	0.56	0.29	0.56	0.34
	膨果	0.69	0.87	0.66	0.83	0.48	0.26	0.44	0.33

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

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