Paper Title :Determining the Freshness of Fruits and Vegetables using Deep Learning Approaches

Author :Mukhriddin Mukhiddinov, Safarov Furkat Eshpulatovich, Avazov Kuldoshbay Yuldoshboevich, Taeg Keun Whangbo,Abdusalomov Akmalbek Bobomirzaevich

Article Citation :Mukhriddin Mukhiddinov ,Safarov Furkat Eshpulatovich ,Avazov Kuldoshbay Yuldoshboevich ,Taeg Keun Whangbo ,Abdusalomov Akmalbek Bobomirzaevich , (2022 ) " Determining the Freshness of Fruits and Vegetables using Deep Learning Approaches " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 21-25, Volume-10,Issue-11

Abstract : Abstract - Fruit and vegetable freshness classification plays an essential function in the food industry. Freshness is the fundamental measure of fruit and vegetable quality and directly affects consumers' physical health and purchasing motivation.This paper presents a deep learning system for multi-class fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves developing an improved YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone part is enhanced by using the Mish activation function for more precise and rapid detection. The proposed system has outstanding prospects for constructing an autonomous and real-time fruit and vegetable classification for the food industry, marketplaces, and visually impaired people to choose fresh food and avoid food poisoning. Keywords - Fruit and Vegetable Classification, Fruit Categorization, Fresh and Rotten Fruit, Deep Learning, YOLOv4, Food Industry;

Type : Research paper

Published : Volume-10,Issue-11

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