GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer

2021/07/05 by Vipul Mehra, Mehra, Vipul
Agricultural and Biological Sciences · Chemistry · Engineering · #Advanced Chemical Sensor Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Smart Agriculture and AI #Spectroscopy and Chemometric Analyses #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2108.02563

openalex created_date 2021/05/24 · openalex publication_date 2021/07/05 · openalex updated_date 2026/07/28

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.

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• CSPNet: A New Backbone that can Enhance Learning Capability of CNN

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