Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)

Animal and Plant-Based Milk Identification System Using Hyperspectral Imaging and Convolutional Neural Network

Authors
Nugi Asmara1, *, Adhi Harmoko Saputro1, **
1Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, West Java, 16424, Indonesia
Corresponding author. Email: * nugi.asmara@sci.ui.ac.id, **adhi@sci.ui.ac.id
Corresponding Authors
Nugi Asmara, Adhi Harmoko Saputro
Available Online 10 March 2022.
DOI
10.2991/absr.k.220305.049How to use a DOI?
Keywords
hyperspectral; milk; animal-based; plant-based; UHT; CNN
Abstract

Milk is a beverage that completes human nutrition. It is produced by cows and goats and can be obtained by plants such as soy and coconut. The nutrition composition contained in kinds of milk is different from one another. The differences in nutrition composition have their identification potential, such as the processing, nutrition differences, purity, quality, etc. Hence, it is necessary to build a system that can identify milk types with a non-destructive method utilizing hyperspectral images and a Deep Learning algorithm. This research used a hyperspectral camera at a Visible and Near-Infrared (VNIR) range of light (400 - 1000 nm). We used Convolutional Neural Network (CNN) as its image classification algorithm. Milk sample was collected from cow, goat, soy, and coconut and obtained exactly 1920 data. After the data was collected, we created datasets based on the type of classification tested. The category includes milk types with classes of animal-based and plant-based milk, the organisms that produce the milk with classes of coconut, cow, goat, and soy, and the processing method with classes of fresh and Ultra High Temperature (UHT). The tested algorithms of CNN architecture are GoogleNet, AlexNet, and Proposed CNN. The highest accuracy for 480 data was 100% reached by processing method classification of soy milk, and the computation took only 20 seconds. Meanwhile, the highest accuracy for 1920 data was 99.9% achieved by Proposed CNN architecture, and the calculation took only 78 seconds. These results showed that hyperspectral imaging and CNN algorithm are suitable for classifying types of milk.

Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Download article (PDF)

Volume Title
Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)
Series
Advances in Biological Sciences Research
Publication Date
10 March 2022
ISBN
10.2991/absr.k.220305.049
ISSN
2468-5747
DOI
10.2991/absr.k.220305.049How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Nugi Asmara
AU  - Adhi Harmoko Saputro
PY  - 2022
DA  - 2022/03/10
TI  - Animal and Plant-Based Milk Identification System Using Hyperspectral Imaging and Convolutional Neural Network
BT  - Proceedings of the 2nd International Conference on Smart and Innovative Agriculture (ICoSIA 2021)
PB  - Atlantis Press
SP  - 321
EP  - 326
SN  - 2468-5747
UR  - https://doi.org/10.2991/absr.k.220305.049
DO  - 10.2991/absr.k.220305.049
ID  - Asmara2022
ER  -