Classification of Cumin, Fennel and Carom Using Transfer Learning

Document Type

Conference Proceeding

Publication Title

2022 5th International Conference on Signal Processing and Information Security, ICSPIS 2022

Abstract

Machine learning has become an important part of our daily lives. And, with the assistance of image processing, we can accomplish much more than before. Different types of seeds can be detected and classified. However, differentiating seeds is a difficult task. And, as the use of homemade medicine grows, it is critical to choose the right ingredient. Some may experience severe side effects. Cumin, fennel, and carom seed are all members of the same family and have similar appearances, making it easy for people to confuse one for the other. This study proposes an ML model based on image processing and transfer learning to distinguish between cumin, fennel, and carom. For training and testing purposes, a dataset of 360 images from each class was created.

First Page

46

Last Page

49

DOI

10.1109/ICSPIS57063.2022.10002538

Publication Date

1-2-2023

Keywords

Training, Image processing, Transfer learning, Information security, Signal processing, Task analysis, Testing, Transfer Learning, Machine Learning, ML Models, Neural Network, Deep Learning, Antifungal, Complex Network, Convolutional Neural Network, Test Dataset, Validation Dataset, Image Recognition, Antitumor Properties, Food Categories, Percent Accuracy, Black Pepper, Yellow Stripe, Digestive Properties, Millions Of Images, Mobile Devices

Comments

IR conditions: non-described

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