Research On Quality Inspection Of Crown Pear Based On Nondestructive Testing Technology

In the process of exporting crown pears to overseas markets,they will produce internal corruption and deterioration without obvious changes in appearance.As a result,all the products will be rejected,resulting in huge economic losses.In addition,the sensory evaluation method of destructive detection technology has been used in its quality detection process.On the one hand,the method has less samples and less coverage;on the other hand,its results have poor reliability and repeatability.Therefore,this article proposes a new type of non-destructive testing technology,which establishes a crown pear classification model based on electronic nose olfactory and visual image features,and applies fusion technology based on electronic nose technology and machine vision technology to crown pear quality detection.Improve the accuracy of classification models.This article first introduces the research background and significance of the subject,briefly describes the current status of quality inspection technology of pear fruits,introduces the current sensory evaluation methods of Shijiazhuang Crown pear,and elaborated the research status of machine vision detection technology and electronic nose detection technology at home and abroad.Secondly,it introduces the collection and processing scheme of experimental materials,and sets up an image acquisition system suitable for the crown pear sample,develops the workflow,and then performs feature extraction and analysis based on the original image data,including color feature extraction and epidermal point feature extraction.Finally,the obtained image feature data is analyzed,and experiments are performed in accordance with the steps of image visual data analysis,and dimensionality reduction visualization analysis and model training and verification are performed to obtain the accuracy rate of the test set and verification set of each classification model.Thirdly,it introduces the working process,sensor characteristics and parameter settings of electronic nose equipment,formulates specific detection conditions and experimental schemes based on experimental materials and equipment,and introduces data analysis methods and principles.The response curve analysis and radar chart analysis of the electronic nose sniff data are performed,and the feature extraction and analysis are performed based on the electronic nose data.The traditional single feature extraction method is improved.A multi-feature extraction method is proposed,compare model accuracy of single feature data and model accuracy of multiple feature data,finally analyze the obtained electronic nose characteristic data,perform experiments according to the electronic nose olfactory data analysis steps,perform dimensionality reduction visual analysis,and get the test set and validation set accuracy of each classification model.Finally,the fusion data after the fusion of electronic nose data and image data is analyzed.the principle and method of multi-information fusion are introduced,and then different fusion methods are compared and selected.The fusion feature data is analyzed,and the fusion information data analysis steps are followed.Perform dimensionality reduction visual analysis,and model training and verification to obtain the accuracy rate of the test set and verification set of each classification model,and compare and analyze the verification results of three different technologies to determine the best quality based on crown pear quality inspection.Optimal classification adjustable model.