Defects Detection in Fruits and Vegetables Using Image Processing and Soft Computing Techniques

Narendra, Varun; Pinto, Ancilla J.

doi:10.1007/978-981-15-8603-3_29

Cited by 9 publications

(1 citation statement)

References 21 publications

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“…For vegetables and fruits, an intelligent categorization technique is required, as well as a fault detection system. The goal of this study [47] was to use morphology, colour, and texture to detect exterior abnormalities in fruits and vegetables. External fruits and vegetables faults, as well as different picture approaches, are used in this proposed work, which is carried out using various algorithms proposed for quality inspection.…”

Section: Quality Defects Grading and Quantity/identifyingmentioning

confidence: 99%

Trend Review Related To Defects Detection from Fruits and Vegetables

Anjali¹,

Kannan²,

Andrès³

et al. 2021

Preprint

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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.

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Section: Quality Defects Grading and Quantity/identifyingmentioning

confidence: 99%