Hyperspectral and Deep Learning-based Regression Model to Estimate Moisture Content in Sea Cucumbers

Hendra Angga Yuwono, Adhi Harmoko Saputro, Sabar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The hyperspectral image technology contains information in spectral and spatial forms that produce a huge amount of data. This data becomes an additional load while data is processed. Deep learning is the latest method capable of processing large-scale data with a deep structure of artificial neural network (ANN) and improving the model performance of data analysis. Therefore, this study aims to get a deep learning model into hyperspectral image processing for quantitative measurements of moisture content in dried sea cucumbers study case. The sea cucumber used in this study is the dried sea cucumber (Holothuria scabra), commonly known as Beche-de-mer. This study used the 400-1000 nm wavelength range to measure the moisture content quickly and nondestructively. The proposed model is deep learning which is used to build a predictive model system for moisture content in dried sea cucumbers. The coefficient of determination and the root means square error evaluate the measurement system. The measurement results of moisture content, the coefficient of determination, and the root mean square error values for training data are 0.99 and 0.11%, while testing data are 0.92 and 0.29%.

Original languageEnglish
Title of host publicationProceedings - 8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021
EditorsAuzani Jiddin, M Amjad, Imam MI Subroto, Mochammad Facta
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-287
Number of pages5
ISBN (Electronic)9786236264195
DOIs
Publication statusPublished - 2021
Event8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021 - Virtual, Semarang, Indonesia
Duration: 20 Oct 202121 Oct 2021

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2021-October
ISSN (Print)2407-439X

Conference

Conference8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021
Country/TerritoryIndonesia
CityVirtual, Semarang
Period20/10/2121/10/21

Keywords

  • Deep learning
  • Dried sea cucumber
  • Hyperspectral
  • Moisture content
  • Proposed CNN

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