LAPSE:2023.5851
Published Article
LAPSE:2023.5851
A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods
February 23, 2023
Abstract
This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r2) of 0.72, root mean square error of prediction (RMSEP) of 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse.
This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r2) of 0.72, root mean square error of prediction (RMSEP) of 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse.
Record ID
Keywords
moisture content, multispectral reflectance imagery, real-time estimation, sugarcane bagasse
Subject
Suggested Citation
Nakawajana N, Lerdwattanakitti P, Saechua W, Posom J, Saengprachatanarug K, Wongpichet S. A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods. (2023). LAPSE:2023.5851
Author Affiliations
Nakawajana N: Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Lerdwattanakitti P: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand; Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Kho
Saechua W: Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand [ORCID]
Posom J: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand; Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Kho [ORCID]
Saengprachatanarug K: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand; Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Kho
Wongpichet S: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Lerdwattanakitti P: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand; Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Kho
Saechua W: Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand [ORCID]
Posom J: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand; Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Kho [ORCID]
Saengprachatanarug K: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand; Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Kho
Wongpichet S: Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Journal Name
Processes
Volume
9
Issue
5
First Page
777
Year
2021
Publication Date
2021-04-28
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9050777, Publication Type: Journal Article
Record Map
Published Article
LAPSE:2023.5851
This Record
External Link
https://doi.org/10.3390/pr9050777
Publisher Version
Download
Meta
Record Statistics
Record Views
227
Version History
[v1] (Original Submission)
Feb 23, 2023
Verified by curator on
Feb 23, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.5851
Record Owner
Auto Uploader for LAPSE
Links to Related Works