The temperature and humidity inside poultry houses are highly coupled through nonlinear psychrometric processes, and the limitation of actuators makes this type of system difficult to control. To understand the dynamics of such systems and further to design a suitable controller, in this study, the mathematical model for a closed poultry house was derived from the governing equations of the various components related to the poultry house, including the energy and mass balance and the psychrometric correlations of the moist air. The model was simulated and the simulation result was compared to the data collected experimentally for model verification and control gains estimation. Under the assumptions of 70 percent Active Mixing Volume (AMV) with the constant maximum ventilation rate in the case study, the temperature and the relative humidity simulated results were in the good agreement with the real physical plant data. At the front, the middle and the rear part of the poultry house, the rootmean-square error (RMSE) obtained for internal temperatures are 1.
This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.
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