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Indica type rice (a common staple in the world) is usually treated using the parboiling process. Loss in grain quantity and quality during this treatment is currently observed in many communities of the world. However, due to its non-availability in Korea, Japonica variety (a widely grown rice in Korea) was investigated in this study to establish optimal parboiling condition(s) for milling. The Taguchi L9 (34) orthogonal array optimization method was adopted. To achieve quantity, optimal parboiling conditions peaked in moisture content of 27% after soaking; steaming temperature of 90°C for 40 minutes; and drying at a temperature of 35°C. There was a significant increase in the milling rate from 70.48% of the control to 74.97% of the best experimental result. Minitab analysis showed drying temperature to be the most significant factor for milling yield. In qualitative analyses, there were varied optimal conditions, but the conditions for quantitative analyses can be adopted since nutrients optimized at the same conditions as stated above. Nutrient analysis showed a significant increase of 2% and 1.4% in protein and amylose contents respectively when compared with the control. Findings from this study show that only a right combination of all factors can guarantee a good outcome from the parboiling process; it is better to skip the process than to perform it improperly. The degree of combination of factors is dependent on the variety. Therefore, studies should always be carried out on new developed varieties before parboiling commences. [ABSTRACT FROM AUTHOR]

Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson's correlation coefficient ( r ) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ajani, Aboyeji, Mallipeddi, Dooyum Uyeh, Ha and Park.)