Using Machine Learning to Grade the Mango’s Quality Based on External Features Captured by Vision System

Nowadays, mangoes and other fruits are classified according to human perception of low productivity, which is a poor quality of classification. Therefore, in this study, we suggest a novel evaluation of internal quality focused on external features of mango as well as its weight. The results show th...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2020-09, Vol.10 (17), p.5775
Main Authors: Truong Minh Long, Nguyen, Truong Thinh, Nguyen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Cameras
captured images
Classification
Datasets
Deep learning
Defects
Discriminant analysis
fruit grade
Fruits
image processing
Kalman filters
Learning algorithms
Machine learning
mango classification
Mangoes
Measurement techniques
Methods
Model accuracy
Nondestructive testing
Outliers (statistics)
Quality
sorting algorithm
Studies
Support vector machines
Unstructured data
Vision systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nowadays, mangoes and other fruits are classified according to human perception of low productivity, which is a poor quality of classification. Therefore, in this study, we suggest a novel evaluation of internal quality focused on external features of mango as well as its weight. The results show that evaluation is more effective than using only one of the external features or weight combining an expensive nondestructive (NDT) measurement. Grading of fruits is implemented by four models of machine learning as Random Forest (RF), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Models have inputs such as length, width, defect, weight, and outputs being mango classifications such as grade G1, G2, and G3. The unstructured data of 4983 of captured images combining with load-cell signals are transferred to structured data to generate a completed dataset including density. The data normalization and elimination of outliers (DNEO) are used to create a better dataset which prepared for machine learning algorithms. Moreover, an unbiased performance estimate for the training process carried out by the nested cross-validation (NCV) method. In the experiment, the methods of machine learning have high accurate over 87.9%, especially the model of RF gets 98.1% accuracy.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10175775