Abstract: 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.

Thanh-Nghi Doan and Duc-Ngoc Le-Thi. “A Novel Mango Grading System Based on Image Processing and Machine Learning Methods”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.5 (2023). http://dx.doi.org/10.14569/IJACSA.2023.01405115

@article{Doan2023,
title = {A Novel Mango Grading System Based on Image Processing and Machine Learning Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01405115},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01405115},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Thanh-Nghi Doan and Duc-Ngoc Le-Thi}
}