A review of external quality inspection for fruit grading using CNN models

Abstract

This article reviews the state of the art of recent CNN models used for external quality inspection of fruits, considering parameters such as color, shape, size, and defects, used to categorize fruits according to international marketing levels of agricultural products. The literature review considers the number of fruit images in different datasets, the type of images used by the CNN models, the performance results obtained by each CNNs, the optimizers that help increase the accuracy of these, and the use of pre-trained CNN models used for transfer learning. CNN models have used various types of images in the visible, infrared, hyperspectral, and multispectral bands. Furthermore, the fruit image datasets used are either real or synthetic. Finally, several tables summarize the articles reviewed, which are prioritized according to inspection parameters, facilitating a critical comparison of each work. • Automated fruit grading: the key to ensuring quality and market competitiveness. • State of the art: critical review of CNN models in fruit quality inspection • Dataset challenges: use of public data, synthetic images, and data volume • Four key categories: ripeness, deformities, defects, and grading applications

Topics & Concepts