shirin asadian; ahmad banakar; Bahareh Jamshidi
Abstract
Nowadays, due to the evaluation and high costs of maintenance and repair of sugarcane harvesting machines, it is necessary to monitor sugarcane harvester hydraulic oil using a faster and non-destructive method to determine contamination and TAN index. In this research, the ability of the visible spectroscopy ...
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Nowadays, due to the evaluation and high costs of maintenance and repair of sugarcane harvesting machines, it is necessary to monitor sugarcane harvester hydraulic oil using a faster and non-destructive method to determine contamination and TAN index. In this research, the ability of the visible spectroscopy method to non-destructively measure and predict the water content and TAN index in harvester Austoft 7000 hydraulic oil samples at different operating hours was investigated. For this purpose, spectra were taken from the samples in the spectral region of 400-780 nm. Multivariate Partial Least Squares (PLS) regression models were developed based on reference measurements and pre-processed spectra information by combining different pre-processing (Moving Average, Savitzky-Golay, Standard normal variate and First Derivative) methods to measure and to predict the water content and TAN index of hydraulic oil. The results showed that the visible spectroscopy method could be used for quick and non-destructive measurement of water content and TAN index at different operating hours of harvester Austoft 7000 hydraulic oil. The best prediction results of water content in hydraulic oil were obtained with PLS model based on moving average (MA) preprocessing method (rcv=0.96, RMSECV=1.86, rp=0.89 and RMSEP=3.18), which had excellent accuracy (SDR=3.12). On the other hand, the PLS model based on the combination of moving average preprocessing and standard normal distribution (MA+SNV) was able to predict the TAN index with excellent accuracy (SDR=3.1) (rcv=0.94, RMSECV=0.007, rp=0.89 and RMSEP= 0.010). Therefore, the application of visible spectroscopy technology in agriculture and industries can be recommended for rapid monitoring of hydraulic oil quality and with the aim of controlling pollution.
roya farhadi; Amir Hossein Afkari Sayyah; Bahareh Jamshidi; ahmad mousapour gorji
Abstract
Potato storage is essential to ensure the continued supplying of potatoes to consumers and the potato processing industry. During storage, physiological changes and water loss lead to changes in color, shape, size, and texture of potatoes. Therefore, there is a need for a quick and accurate method to ...
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Potato storage is essential to ensure the continued supplying of potatoes to consumers and the potato processing industry. During storage, physiological changes and water loss lead to changes in color, shape, size, and texture of potatoes. Therefore, there is a need for a quick and accurate method to measure the quality of the product. In this study, machine vision and neural network methods were used in classification and modeling of two stored potato samples (Agria and Clone 8-397009) under constant and variable conditions. Among 29 measured parameters relating to color, texture and morphological features of potato, some features were selected as the main parameters to monitor the chnges in product during storage period: Major Axis Length, Compactness, and area (morphological features), L* and b* (color features) and Average contrast (Ac) and Average gray level (Agl) (texture features). Among the training algorithms, Levenberg–Marquardt (LM) training algorithm with the lowest root mean square error (RMSE=0.012) and the highest coefficient of determination (R2=95.01) were considered as an optimal model for classification of two samples stored in non-technical and technical storage. The accuracy of identification of the Agria genotype was 89.2% and 87.6%, and the accuracy of the genotype Clone 8-397009 was 92.4% and 90.3%, in non-technical and technical storage respectively.
