An End-To-End Deep Learning System for Hop Classification

Project Code :TCMAPY916

Objective

The objective of developing an end-to-end deep learning system for hop classification is to accurately and automatically classify hop varieties used in brewing beer. The system should take raw hop images or data as input and output the corresponding hop variety, streamlining the hop classification process for brewers and researchers.

Abstract

Hop classification is a crucial task in the field of agriculture and brewing, as it determines the quality and flavor of beer production. Traditionally, hop classification has been a labor-intensive and subjective process, relying on human expertise. In recent years, deep learning techniques, specifically Convolutional Neural Networks (CNNs), have shown promise in automating this task. This paper presents an end-to-end deep learning system for hop classification, leveraging the power of CNNs.

Our system begins with the collection of high-resolution images of hop cones from different varieties. These images are preprocessed to enhance their quality and consistency. We then design a deep CNN architecture tailored for hop classification, which learns to automatically extract relevant features from the hop cone images. The CNN model is trained on a large dataset comprising various hop varieties, ensuring robustness and accuracy in classification.

Keywords: Hop Classification, Deep Learning, Convolutional Neural Networks (CNN), Inception-ResnetV2, Image Processing, Beer Production

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.