Saechua, Wanphut

Loading...
Thumbnail Image
Preferred name
Saechua, Wanphut
Alternative Name
Saechua, W.
Main Affiliation
Email
wanphut.sa@kmitl.ac.th

Settings

Now showing 1 - 1 of 1
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A low-cost system for moisture content detection of bagasse upon a conveyor belt with multispectral image and various machine learning methods
    (2021-05-01)
    Nakawajana, Natrapee
    ;
    Lerdwattanakitti, Patchara
    ;
    Saechua, Wanphut
    ;
    Posom, Jetsada
    ;
    Saengprachatanarug, Khwantri
    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 (r<sup>2</sup> ) 0.72, root mean square error of prediction (RMSEP) 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.