Vision-based machine-mediated mango sorting system using OpenCV and convolutional neural network with tensor flow

Date of Publication

2017

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Engineering

College

Gokongwei College of Engineering

Department/Unit

Electronics and Communications Engineering

Thesis Adviser

Melvin K. Cabatuan

Defense Panel Chair

Alexander C. Abad

Defense Panel Member

Cesar A. Llorente
Jose Martin Z. Maningo

Abstract/Summary

A large part of the Philippine economy depends on the export of fresh produce to other countries. Among these major products is the mango, a yellow-coloured sweet fruit that is prone to bruises and other skin defects and are thus carefully scrutinized and screened by farmers to ensure the quality of their output. The standard of what constitutes an export-quality mango however can vary significantly among farmers throughout the day. The group therefore seeks to develop a system that would aid farmers in examining their mango produce to increase their output and to ensure standardization in the quality of the mangoes they export. The system would be equipped with OpenCV to analyze the images taken from the mango and tensor flow neural network to countercheck the features of the skin of the subject with the store desired characteristics to determine the status of the mango that is being examined by the system. The improvement in the mango selection process would translate into higher quality outputs of farmers and in turn would increase the demand for local products outside of the country, creating a better economy for the Philippines and for Filipinos.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU21946

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

xii, 84, 35 leaves : illustrations (some color) ; 28 cm.

Keywords

Mango; Neural computers

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