Fruit quality detection and classification: A survey

Abstract

Fruit quality is the most important factor in protecting humans from health problems. Automatic detection is particularly significant in the food industry and agriculture in that field. It saves time and protects you from health problems. Fruit quality detection and classification is done using various algorithms and image processing techniques. The image processing technique used in this study helps farmers, buyers, and shopkeepers identify fruit quality and classify fruits from a collection of diverse fruits. Several methods were employed by the researchers for the classification and detection of fruits quality. Support vector machine(SVM), k-nearest neighbor(KNN), Deep convolution neural network(DCNN), convolution neural network (CNN) are the algorithms examined for fruit classification and detection. For fruit detection, the CNN algorithm provided the highest accuracy. The aim of this work is to prevent health risks associated with consuming tainted fruit. The CNN algorithm is the most effective at detecting and classifying fruit flaws.