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Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

Computers, Materials & Continua 2024年第1期78卷 781-794页

作者： S.Sofana Reka ankita bagelikar Prakash Venugopal V.Ravi Harimurugan Devarajan Centre for Smart Grid Technologies School of Electronics EngineeringVellore Institute of TechnologyChennai600127India School of Electronics Engineering Vellore Institute of TechnologyChennai600127India Department of Electrical Engineering Dr.B R Ambedkar National Institute of TechnologyJalandharPunjab144008India

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional *** primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to *** impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the *** overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf *** agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf ***,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten *** proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and ***,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf *** results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best *** comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf *** developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated ***,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.

关键词： Rotten fruit detection shelf life deep learning convolutional neural network machine learning gaussian naïve bayes random forest visual geometry group16

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