Multigene Genetic Programming Model for Temperature Optimization to Improve Lettuce Quality

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI:10.1109/HNICEM54116.2021.9731974

Jo-Ann V. Magsumbol, Maria Gemel B. Palconit, Lovelyn C. Garcia, Marife A. Rosales, A. Bandala, E. Dadios

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引用次数: 1

Abstract

This paper presents a Multigene Genetic Programming (MGGP) approach in optimizing the temperature of romaine lettuce inside an artificially controlled environment (ACE). In this research, MGGP is used to find the prediction model that will lead to the optimum temperature for growing lettuce crop. The system used a 1000 population using tournament selection with 40 generations. A mutation probability of 0.14 was applied to validate if it is at global optima. When the iterations reached the termination criteria, the system stopped, resulting in the best temperature model for growing lettuce crop. Training and testing of predictions were done. The model developed in this study can be used for the control system of the temperature setting inside the ACE which can provide optimal condition.

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莴苣品质温度优化的多基因遗传规划模型

本文提出了一种多基因遗传规划(MGGP)方法来优化人工控制环境(ACE)下长叶莴苣的温度。本研究采用MGGP方法寻找莴苣作物生长最适温度的预测模型。该系统使用了1000个人口，采用40代锦标赛选择。应用0.14的突变概率来验证它是否处于全局最优状态。当迭代达到终止准则时，系统停止运行，得到生菜作物生长的最佳温度模型。完成了预测的训练和测试。所建立的模型可用于ACE内部温度设定的控制系统，能够提供最优的温度设定条件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)

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