基于改进WOA-LSSVM和高光谱的猕猴桃糖度无损检测
Non destructive detection of kiwifruit sugar content based on improved WOA-LSSVM and hyperspectral analysis
章恺 1朱丽芳 1李入林 2王子异3
作者信息
- 1. 南阳职业学院,河南 南阳 473000;南阳市药食同源资源开发工程技术研究中心,河南 南阳 473000
- 2. 南阳理工学院,河南南阳 473000
- 3. 河南农业大学,河南郑州 450046
- 折叠
摘要
目的:解决猕猴桃糖度无损检测方法存在的准确性差和效率低等问题.方法:提出一种将高光谱检测技术、最小二乘支持向量机和改进的鲸鱼算法相结合的猕猴桃糖度无损检测方法.通过高光谱检测系统采集猕猴桃的高光谱信息,对其进行预处理和特征波长筛选后,输入改进鲸鱼算法优化的最小二乘支持向量机模型,实现猕猴桃糖度的快速无损检测,并验证其性能.结果:所提方法可以实现猕猴桃糖度的快速无损检测,测试集决定系数为0.965 2,测试集均方根误差为0.880 5,平均检测时间为1.06 s.结论:将机器学习算法与高光谱检测技术相结合,可以实现猕猴桃糖度的快速无损检测.
Abstract
Objective:Addressing the issues of poor accuracy and low efficiency in non-destructive testing methods for kiwifruit sugar content.Methods:Proposing a non-destructive testing method for kiwifruit sugar content that combined hyperspectral detection technology,least squares support vector machine,and improved whale algorithm.By collecting hyperspectral information of kiwifruit through a hyperspectral detection system,after preprocessing and feature wavelength screening,and then input into an improved whale algorithm optimized least squares support vector machine model to achieve rapid and non-destructive detection of kiwifruit sugar content,and verify its performance.Results:The proposed method could achieve rapid and non-destructive detection of kiwifruit sugar content,with a determination coefficient of 0.965 2 for the test set,a root mean square error of 0.880 5 for the test set,and an average detection time of 1.06 seconds.Conclusion:Combining machine learning algorithms with hyperspectral detection technology can achieve rapid and non-destructive detection of kiwifruit sugar content.
关键词
猕猴桃/高光谱检测/糖度/机器学习算法/鲸鱼优化算法/最小二乘支持向量机Key words
kiwi fruit/hyperspectral detection/sugar content/machine learning algorithms/whale optimization algorithm/least squares support vector machine引用本文复制引用
基金项目
河南省科技攻关计划(22104370125)
出版年
2024
国家科技期刊平台
NSTL
万方数据