Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Abstract

Sorting and Classification of mango, there are different colors, weights, sizes, shapes and densities. Currently, classification based on the above features is being carried out mainly by manuals due to farmers' awareness of low accuracy, high costs, health effects and high costs, costly economically inferior. This study was conducted on three main commercial mango species of Vietnam to find out the method of classification of mango with the best quality and accuracy. World studies of mango classification according to color, size, volume and almost done in the laboratory but not yet applied in practice. The quality assessment of mango fruit has not been resolved. Application of image processing technology, computer vision combined with artificial intelligence in the problem of mango classification or poor quality. The goal of the study is to create a system that can classify mangoes in terms of color, volume, size, shape and fruit density. The classification system using image processing incorporates artificial intelligence including the use of CCD cameras, C language programming, computer vision and artificial neural networks. The system uses the captured mango image, processing the split layer to determine the mass, volume and defect on the mango fruit surface. Especially, determine the density of mangoes related to its maturity and sweetness and determine the percentage of mango defects to determine the quality of mangoes for export and domestic or recycled mangoes. This article is about the development of an automatic mango classification system to control and evaluate mango quality before packaging and exporting to the market. It is in the research, design and fabrication of mango classification model and the completion of an automatic mango classification system using machine vision combining artificial intelligence. Index Terms-The classification of mango, sorting of mangoes, image processing technology, artificial intelligence; computer vision, artificial neural networks.

Damage Detection and Classification of Mangoes Based on Thermal Imaging

“…To do so is crucial to mango industries and producers. This lack of quality is not possible to identify by visual inspection, which leads to losing the belief of the end-users in exporters [4], In recent years, fruit and vegetable processing industries have relied heavily on nondestructive grading and quality evaluation methods, including spectroscopy techniques, MRI (magnetic resonance imaging), hyperspectral, thermal, and Raman imaging [5], Mango fruit quality evaluation and bruise detection were also detected using earlier techniques [6][7][8][9][10][11]. However, these approaches are difficult and expensive to use in the automated online detection of mango damage, and the speed of precision and visibility of these methods are low.…”

“…Numerous algorithms have already been employed in fruit bruise classification. such as K nearest neighbor, artificial neural networks, backpropagation neural networks (BPNN), Partial Least Squares Regression (PLSR), principal component analysis (PCA), Convolution neural network (CNN), support vector machine (SVM) [4], [6], [21][22][23][24][25][26][27][28], Such algorithms attained various advantages with specific cases of classification. However, in most situations, the most successful classification algoritcenteredeep learning is centered on artificial neural networks.…”

Makine Öğrenmesi Algoritmaları Kullanılarak Kayısı İç Çekirdeklerinin Sınıflandırılması

“…Thong ve arkadaşları [12], mango meyvesinin otomatik olarak sınıflandırıldığı ve yapay sinir ağları ile çalışan bir görüntü işleme sistemi geliştirmişlerdir. Mangoların renk, boyut, hasar görmüş gibi kriterlerini dikkate alarak sınıflandırma işlemini gerçekleştirmişlerdir.…”