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.

中文翻译：

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具有多光谱图像和各种机器学习方法的低成本检测传送带上蔗渣中水分的系统

这项研究旨在提出一个基于多光谱图像的在线系统，用于实时估计甘蔗渣的水分含量。该系统由一条传送带，四个卤素灯泡和一个多光谱相机组成。MC模型是使用机器学习算法开发的，即多元线性回归（MLR），主成分回归（PCR），人工神经网络（ANN），PCA-ANN，高斯过程回归（GPR），PCA-GPR，随机森林回归（RFR）和PCA-GPR。使用150个样本（校准集）开发模型，同时将其余50个样本用作验证集。所有开发模型的比较表明，PCA-RFR模型实现了更好的检测，并且MC预测的准确性更高。²）0.72，预测的均方根误差（RMSEP）为11.82 wt％，预测的标准误差与标准偏差（RPD）的比率为1.85。结果表明，该技术对甘蔗渣的MC快速筛选非常有用。