利用长短期记忆网络模拟水培植物生长对营养液温度的响应

G. Aji, K. Hatou, T. Morimoto
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引用次数: 1

摘要

本研究探讨了一种在动态系统中模拟植物生长对营养液温度的响应的方法的发展。营养液温度是水培栽培中控制植物生长的重要因素之一。确定栽培过程中营养液温度的最优控制策略,可实现植株生长的最大化。通过动态系统识别过程,可以确定最优控制策略。然而,由于营养液温度与植物生长之间的生理行为非常复杂和不确定,建立植物生长对营养液温度的动态模型并不容易。我们提出了长短期记忆(LSTM)网络来识别和建立一个受营养液温度影响的植物生长动态特征模型。以辣椒植株为研究对象,获取植株生长的时间序列数据,采用5种不同类型的营养液温度动态模式进行系统识别。结果表明,所建立的LSTM模型能较好地预测水培条件下植物生长对营养液温度的响应。
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Performance of Long Short-Term Memory Networks for Modeling the Response of Plant Growth to Nutrient Solution Temperature in Hydroponic
This study examines the development of an approach for modeling the response of plant growth to nutrient solution temperature in hydroponic cultivation in a dynamic system. Nutrient solution temperature is one of the essential manipulating factors for plant growth in hydroponic cultivation. Determining the optimal control strategy of nutrient solution temperature during cultivation could lead to maximize the growth of the plant. By identifying the process using a dynamic system, the optimal control strategy can be determined. However, developing a dynamic model of plant growth to nutrient solution temperature is not easy due to physiological behavior between them are quite complex and uncertain. We propose the long short-term memory (LSTM) networks to identify and develop a model of dynamic characteristics of plant growth as affected by the nutrient solution temperature. Chili pepper plants were used to obtain time-series data of plant growth, with five different types of dynamic nutrient solution temperature patterns for system identification. The results showed that the proposed LSTM model provides promising performance in predicting the response of plant growth to nutrient solution temperature in hydroponic cultivation.
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