GuavaNet：用于自动感官评价以预测消费者对番石榴的接受程度的深度神经网络架构
GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer
摘要：
本论文分为两个部分：第一部分：基于计算机视觉和深度学习的图像处理的水果、蔬菜、奶酪和鱼分析：综述。它包括一个全面的审查图像处理，计算机视觉和深度学习技术应用于进行水果，蔬菜，奶酪和鱼的分析。这一部分也作为第二部分的文献综述。第二部分：GuavaNet：一个深神经网络架构的自动感官评价，以预测消费者对番石榴的接受程度。这一部分介绍了一个端到端的深度神经网络架构，可以预测的程度，由消费者的番石榴感官评价的基础上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
