Non destructive detection of kiwifruit sugar content based on improved WOA-LSSVM and hyperspectral analysis
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.
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