A Novel Mango Grading System Based on Image Processing and Machine Learning Methods

Mangoes are a great commercial fruit and are widely cultivated in tropical areas. In smart agriculture, the automatic quality inspection and grading application is essential to post-harvest processing, due to the laborious nature and inconsistencies of traditional manual visual grading. This paper p...

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Bibliographic Details
Published inInternational journal of advanced computer science & applications Vol. 14; no. 5
Main Authors Doan, Thanh-Nghi, Le-Thi, Duc-Ngoc
Format Journal Article
LanguageEnglish
Published West Yorkshire Science and Information (SAI) Organization Limited 2023
Subjects
Accuracy
Agriculture
Algorithms
Apples
Automation
Classification
Computer science
Digital imaging
Fruits
Harvest
Image processing
Information technology
Inspection
Labor
Machine learning
Maintenance
Mangoes
Neural networks
Skin
Statistical analysis
Support vector machines
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Summary:Mangoes are a great commercial fruit and are widely cultivated in tropical areas. In smart agriculture, the automatic quality inspection and grading application is essential to post-harvest processing, due to the laborious nature and inconsistencies of traditional manual visual grading. This paper presents a low-cost, efficient, and effective mango grading system based on image processing and machine learning methods to generate higher quality fruit sorting, quality maintenance, production, and cut back labor concentration. A novel database of classified mangoes was collected and built in An Giang province. Methodologies and algorithms that utilize digital image processing, content-predicated analysis, and statistical analysis are implemented to determine the grade of local mango production. On our collected dataset, the proposed system achieved overall with an overall accuracy of 88% for all mango grades. The system shows compromised results for higher-quality fruit sorting, quality maintenance, and production while reducing labor concentration.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.01405115