The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that...The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.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 life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In 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 life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.展开更多
According to study for years,fruits disease of Jinsixiaozao was divided into three types,which were thick rotten,dark furuncle and brown rind.Different symptom had different pathogenic fungus.The main pathogenic fungu...According to study for years,fruits disease of Jinsixiaozao was divided into three types,which were thick rotten,dark furuncle and brown rind.Different symptom had different pathogenic fungus.The main pathogenic fungus of thick rotten type of jujube was Physalospora obtuse(Schw.)Cooke,and the isolated rate in the thick rotten disease fruits was 94.6.The main pathogenic fungus of dark furuncle type was Alternaria alternata(Fr.)Keissler,and the isolated rate in the dark furuncle fruits was 75.7.The main pathogenic fungus of the brown rind type was Phoma destructiva Plowr and A. alternata(Fr.)Keissler.The isolated rate in the brown rind type was 33.2 and 25.5 seperately.The mixed isolated rate was 19.1.According to this,three types of the fruits disease of Jinsixiaozao were named as thick rotten disease,dark furuncle and brown rind disease.展开更多
文摘The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.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 life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In 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 life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.
文摘According to study for years,fruits disease of Jinsixiaozao was divided into three types,which were thick rotten,dark furuncle and brown rind.Different symptom had different pathogenic fungus.The main pathogenic fungus of thick rotten type of jujube was Physalospora obtuse(Schw.)Cooke,and the isolated rate in the thick rotten disease fruits was 94.6.The main pathogenic fungus of dark furuncle type was Alternaria alternata(Fr.)Keissler,and the isolated rate in the dark furuncle fruits was 75.7.The main pathogenic fungus of the brown rind type was Phoma destructiva Plowr and A. alternata(Fr.)Keissler.The isolated rate in the brown rind type was 33.2 and 25.5 seperately.The mixed isolated rate was 19.1.According to this,three types of the fruits disease of Jinsixiaozao were named as thick rotten disease,dark furuncle and brown rind disease.
