Use of Convolutional Neural Networks (CNN) to recognize the quality of oranges in Peru by 2023

Abstract

Introduction: the agricultural sector in Peru has witnessed a notable increase in the production of oranges, which has promoted the essential use of convolutional neural networks (CNN). The ability to interpret images by visual artificial intelligence has been fundamental for the analysis and processing of these images, especially in the detection and classification of fruits, standing out in the specific case of oranges. Objective: conduct a systematic literature review (RSL) to evaluate the neural networks used in the classification of oranges in Peru. Method: an RSL was carried out using the PICO strategy to search the Scopus database. The selection criteria included studies that used convolutional neural networks to classify the quality status of oranges in the Peruvian context. Results: all the studies reviewed were based on the use of convolutional neural networks (CNN) for fruit classification, using various architectures and techniques. Some studies focused on a single specific fruit, while others addressed the classification of multiple types of fruits, highlighting the importance of the number and variety of images for training the networks. Conclusions: convolutional neural networks show effectiveness in orange classification, but the quality of the images and the variety of data are essential to improve accuracy.