Version 1
: Received: 30 October 2021 / Approved: 2 November 2021 / Online: 2 November 2021 (10:31:56 CET)
How to cite:
N, A.; Kannan, R.; Andres, F.; Ghinea, G. Trend Review Related To Defects Detection from Fruits and Vegetables. Preprints2021, 2021110035. https://doi.org/10.20944/preprints202111.0035.v1
N, A.; Kannan, R.; Andres, F.; Ghinea, G. Trend Review Related To Defects Detection from Fruits and Vegetables. Preprints 2021, 2021110035. https://doi.org/10.20944/preprints202111.0035.v1
N, A.; Kannan, R.; Andres, F.; Ghinea, G. Trend Review Related To Defects Detection from Fruits and Vegetables. Preprints2021, 2021110035. https://doi.org/10.20944/preprints202111.0035.v1
APA Style
N, A., Kannan, R., Andres, F., & Ghinea, G. (2021). Trend Review Related To Defects Detection from Fruits and Vegetables. Preprints. https://doi.org/10.20944/preprints202111.0035.v1
Chicago/Turabian Style
N, A., Frederic Andres and Gheorghita Ghinea. 2021 "Trend Review Related To Defects Detection from Fruits and Vegetables" Preprints. https://doi.org/10.20944/preprints202111.0035.v1
Abstract
Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
