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 ...
Read More
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.
Abstract
The current study developed and tested machine vision and automatic control systems to improve performance and reduce rice loss during paddy husking. This system was optimally adjusted for paddy type, moisture content of paddy, roller spacing and rotational speed of the motor. The percentage of breakage ...
Read More
The current study developed and tested machine vision and automatic control systems to improve performance and reduce rice loss during paddy husking. This system was optimally adjusted for paddy type, moisture content of paddy, roller spacing and rotational speed of the motor. The percentage of breakage of rice kernels was determined using a machine vision system and a singulation device. If rice breakage was greater than a set point, the husker device was adjusted as necessary. The variables of paddy moisture content, roller spacing, and motor rotational speed were used to determine the working conditions of the husker for two paddy varieties. The dependent variables were husking index and rice kernel breakage percentage. An image processing algorithm was coded and evaluated in MATLAB software to determine the percentage of rice kernel breakage. The results showed that selection of proper treatment for the medium-sized kernel paddy, the average husking index was 82.65% and the average rice breakage was 3.88%. For the long kernel paddy, the average husking index and rice breakage were 51.4% and 27.46%, respectively. Without use of the system and with improper selection of motor rotational speed and roller spacing in the medium-sized kernel paddy produced a husking index of 61.58% and rice breakage of 7.51%. For the long kernel paddy, the husking index was 19.14% and rice breakage was 35.03%. Results from the algorithm showed that its accuracy was 91.81%. Evaluation of the singulation device showed that a suction of -45 to -50 mmHg yielded an appropriate 81.3% separation efficiency. The best combination of the machine parameter levels were programmed into the system, which operated to make the proper adjustments automatically. This resulted in the most appropriate working conditions for husking in accordance with paddy variety, paddy moisture content, roller spacing, and motor rotational speed.
