Abstract
This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation.
Library of Congress Subject Headings
Guava--Sensory evaluation--Data processing; Computer vision; Neural networks (Computer science); Machine learning
Publication Date
3-2021
Document Type
Thesis
Student Type
Graduate
Degree Name
Information Sciences and Technologies (MS)
Department, Program, or Center
Information Sciences and Technologies (GCCIS)
Advisor
Michael McQuaid
Advisor/Committee Member
Edward Holden
Advisor/Committee Member
Erik Golen
Recommended Citation
Mehra, Vipul, "GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer" (2021). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/10703
Campus
RIT – Main Campus
Plan Codes
INFOST-MS
