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Quality Inspection, Maturity Detection, and Size based Grading of Various Types of Mangoes using Machine Learning Methods

Farhana Tazmim Pinki, S.M. Mohidul Islam

Abstract


Mango is the most significant and flavorsome fruit in most continents, especially in Asia. Mango grading is an important task in agro-industry. The grading process creates many problems during harvesting for mango growers. The manual grading process is performed by visual inspection and it is very time consuming and labor intensive. Due to different market prices and different market demand, automation in mango grading plays an important role to achieve better accuracy and consistency. In this study, an automatic mango grading system is developed using machine learning and image processing techniques. The system is divided into four phases. In the first phase, quality inspection of mango is performed using Convolution Neural Network (CNN) to detect healthy and diseased mango. In the second phase, different types of healthy mangoes such as Badami, Kesar, and Totapuri are classified using the ensemble method, Random forest. In the third phase, maturity detection is performed using another ensemble method, AdaBoost for the specific type of healthy mangoes to detect ripe, unripe, and partially ripe mangoes. Finally, in the fourth phase, size based grading is performed on the specific type and maturity to determine large, medium, and small mangoes using K-nearest neighbor. Thus the different grades of mangoes based on quality, type, maturity, and size are obtained which have different market price and demand. From experiments, the system shows 94.52% average accuracy.

Keywords: Quality inspection, classification, maturity detection, size based grading, feature vector, machine learning methods

Cite this Article Farhana Tazmim Pinki, S.M. Mohidul Islam. Quality Inspection, Maturity Detection, and Size based Grading of Various Types of Mangoes using Machine Learning Methods. Journal of Image Processing & Pattern Recognition Progress. 2020; 7(1): 18–31p.


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DOI: http://dx.doi.org/10.37591%2Fjoipprp.v7i1.2376

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