Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)

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Optimized Digital Webcam with Hungry Roach Infestation Optimization to Monitor the Drying Process of Cassava Chips

Authors
Yusuf Hendrawan1, *, La Choviya Hawa1, Retno Damayanti1, Dimas Firmanda Al Riza1, Mochamad Bagus Hermanto1, Sandra Malin Sutan1
1Department of Biosystem Engineering, Universitas Brawijaya, Jl. Veteran Malang, ZIP, Malang, 65145, Indonesia
*Corresponding author. Email: yusuf_h@ub.ac.id
Corresponding Author
Yusuf Hendrawan
Available Online 27 October 2023.
DOI
10.2991/978-94-6463-274-3_22How to use a DOI?
Keywords
Cassava chips; computer vision; drying process
Abstract

This study utilizes Logitec HD webcam C270 as a computer vison-based precision monitoring system to optimize the performance of cassava chips drying machines. Cassava chips processed from optimal drying is later utilized as raw material for quality modified cassava flour (MOCAF). The purpose of this study is to optimize the selection of textural features (TFs) in computer vision to predict the water content of cassava chips during the drying process by applying a combination of optimization methods, commonly referred as hungry roach infestation optimization (HRIO) algorithm and modeling methods, which is artificial neural network (ANN). Multi-objective optimization (MOO) was performed with two objectives, by maximizing the accuracy of the predicted water content of cassava chips and by minimizing the number of feature subset of a total of 260 TFs. The test results indicate that the best feature subset depict the 6 TFs such as grey energy, hue energy, red entropy, saturation(HSV) contrast, green homogeneity, and grey correlation. The best feature subset has been tested as ANN input to predict the water content of cassava chips during the drying process (presenting the expected results), marked with the achievement of R2 values between real data and predictive data of 0.98. The results of the measurement of mean square error (MSE) on the training data are 0.000056 and the MSE value in the validation data of 0.000098.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)
Series
Advances in Biological Sciences Research
Publication Date
27 October 2023
ISBN
978-94-6463-274-3
ISSN
2468-5747
DOI
10.2991/978-94-6463-274-3_22How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yusuf Hendrawan
AU  - La Choviya Hawa
AU  - Retno Damayanti
AU  - Dimas Firmanda Al Riza
AU  - Mochamad Bagus Hermanto
AU  - Sandra Malin Sutan
PY  - 2023
DA  - 2023/10/27
TI  - Optimized Digital Webcam with Hungry Roach Infestation Optimization to Monitor the Drying Process of Cassava Chips
BT  - Proceedings of the 7th International Conference on Food, Agriculture, and Natural Resources (IC-FANRES 2022)
PB  - Atlantis Press
SP  - 251
EP  - 271
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-274-3_22
DO  - 10.2991/978-94-6463-274-3_22
ID  - Hendrawan2023
ER  -