The current study developed and tested machine vision and automatic control systems to improve performance and reduce rice loss from paddy husker. 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.

Grain motion on the blade was observed at the rated impeller speed of 2362 min−1using a high-speed camera. The grain exit velocity resulted in an impact force above the yield force of the husk but below the yield force of the grain. However, the maximum friction force experienced on the blade was far below the yield shear force of the husk for all three varieties of rice. Husking tests were performed at different impeller speeds using a hard urethane liner, a soft polystyrene liner and without a liner. Type of liner significantly affected the husking performance. Short-grain rice had high husking energy capacity and cracked grain ratio, but a low broken grain ratio compared with long-grain rice. Performance curves for the three varieties of rice were well expressed by the Weibull's distribution function.

Much of the paddy rice is run through more efficient continuous rubber rollers. The rubber does not scratch or "scarify" the seed coat the way the friction hullers do. As a result, the grain, with its harder, unscarred surface, takes considerably longer to cook than hand-picked rice; also it does not cook as uniformly and, most important, its yield is less. One cup of hand-harvested wild rice will produce nearly four cups of cooked grain, while a cup of paddy rice, after simmering twice as long as the hand-harvested grain, increases to only about three cups. Thus hand-harvested rice is considerably more economical than paddy rice.

The conventional way to husk rice is to pass it between two rubber rollers that are rotating with a surface speed differential. The resulting normal pressure and shear stress causes the husk to be peeled away from the kernel. The process is suited to high-rice flow rates, but is energy intensive and can result in considerable wear to the surfaces of the rollers. The operating parameters for machines of this design are usually determined and set empirically. In this article, some experiments and calculations had been carried out in order to explore the mechanisms involved in husking rice grains using this method. A simple sliding friction rig with load cell and high-speed camera was used to observe the mechanisms that occur during husking. The husking performance of different rubbers was compared for changes in the applied normal load. It was found that grains rotate between the rubber counterfaces on initial motion before being husked. In addition, harder rubbers were found to husk a higher proportion of entrained grains at lower applied normal load.

Results of the evaluations indicated that the developed ACS had 89.2% accuracy in determining the desired working conditions for the rice whitener. The total time of each monitoring round was, on the average, equal to 14.73 s, of which 6.4 s was devoted to kernel sampling and transporting the samples into the imaging chamber, 7.33 s for taking three images from the kernels, processing the captured images and executing the fuzzy inference process, and the remaining 1.5 s for making the adjustments in the level of pressure in the mechanism. Based on this information and in contrast to the corresponding time spent by the human operator to perform a similar process, it was revealed that the performance speed of the ACS was, on average, 31.3% higher than that of the human operator. Evaluation of the samples obtained from the discharge section of the rice whitening machine at different stages of the control process showed that the decisions made by the developed ACS during the control process resulted in a satisfactory improvement in the quality of the output product.

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