A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods

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) 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.

Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

“…By combining several randomized decision trees and aggregates their predictions by averaging, the approach of RF has shown excellent performance in the dataset with large amount of variables and observations. It is also flexible enough to be implemented to large-scale task, is conveniently adapted to various ad hoc learning problems, and returns measures of variable importance [50][51][52][53][54][55][56][57][58][59][60]. The "randomForest" R package of version 4.6-14 [79] was used to construct the RF model.…”

“…There is relatively fewer research applying machine learning methods for NBA game outcomes prediction and NBA game final score prediction [16][17][18][19][20][21][22][23][24]. Therefore, five machine learning methods, including classification and regression trees (CART) [42][43][44][45][46][47][48][49], random forest (RF) [50][51][52][53][54][55][56][57][58][59][60], stochastic gradient boosting (SGB) [24,45,52,[61][62][63][64][65][66][67], eXtreme gradient boosting (XGBoost) [24,[68][69][70][71][72][73], and extreme learning machine (ELM) [24,[73][74]…”

“…CART is a statistical procedure often used as a regression tool to analyze continuous data [42][43][44][45][46][47][48][49]. The approach of RF has shown excellent performance in the dataset with large amount of variables and observations by combining several randomized decision tress and aggregates their predictions by averaging [50][51][52][53][54][55][56][57][58][59][60]. SGB is a hybrid method that merging boosting and bagging techniques, and the input data are selected by random sampling at each stage of the steepest gradient algorithm-based boosting procedure [24,45,52,[61][62][63][64][65][66][67].…”

Non-destructive testing of mechanical properties of solid wood panel based on partial least squares structural equation modeling transfer method

“…Consequently, using this stack of wavelength images or spectral cube, the average intensity and local changes of the intensity pixels at each spectral image may be analyzed and used for pattern identification. However, expensive and specialized hardware is required to capture hyperspectral images (Nakawajana et al 2021;Tunny et al 2022). In general, hyperspectrometers with somewhat higher resolution cost over a million dollars.…”

Development of an Active Training Method for Belt Conveyor

“…In world literature [ 1 , 2 , 3 ], the topic of utility of conveyor belts has been widely discussed. Their numerous advantages were emphasized, such as high reliability [ 4 , 5 , 6 ] and the ease of adapting the conveyor belt’s route to a terrain with rich topography [ 7 , 8 , 9 ]. Another group of advantages includes, among others, continuity of transport, simple structure and relatively low energy consumption [ 10 , 11 , 12 ].…”