Dynamic optimization of water temperature for maximizing leaf water content of tomato in hydroponics using an intelligent control technique
Dynamic optimization of water temperature for maximizing leaf water content of tomato in hydroponics using an intelligent control technique
复制标题
利用智能控制技术动态优化水温以最大化水培番茄叶片含水量
DOI：
10.17660/actahortic.2017.1154.8
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
T. Morimoto
中科院分区：
文献类型：
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作者：
D. Yumeina;G. Aji;T. Morimoto
本研究利用神经网络和遗传算法对水培番茄叶片含水量最大化的水温动态优化进行了研究。叶片含水量估计从叶片厚度在一个连续的和非破坏性的方式使用涡流型位移传感器。利用神经网络实现了模型机理的辨识，并建立了动态模型。一个三层的神经网络允许这样一个复杂的系统被成功地识别和生成模型。接下来，控制过程被分为六个步骤和最佳的六步设定点的水温，最大限度地提高叶片含水量进行了检查，通过模拟识别的神经网络模型，使用遗传算法。控制过程的长度为10小时，每个步骤需要90分钟。在水温为10 ~ 40°C的约束条件下，水温的最佳6步设定点为40 → 10 →40→10→40→38°C。从模拟，它被证实，这种操作是有效的，在最大限度地提高番茄叶片含水量。最后，将该运算应用于一个真实的系统。在该操作中得到的叶含水量比常规对照中的叶含水量大约1.15倍。这表明，这种控制技术是有用的，促进水分吸收的根和相关的较高的叶片含水量在一天内的短期内。
In this study, the dynamic optimization of water temperature, which maximizes the leaf water content of tomatoes in hydroponics, was conducted using neural networks and genetic algorithms. The leaf water content was estimated from leaf thickness in a continuous and non-destructive manner using an eddy current-type displacement sensor. Identification of model mechanisms was achieved and a dynamic model was built using neural networks. A three-layered neural network allowed such a complex system to be successfully identified and generated the model. Next, the control process was divided into six steps and the optimal six-step set points for water temperature that maximizes the leaf water content were examined by simulating the identified neural-network model, using genetic algorithms. The length of the control process was 10 hours, and each step took 90 minutes. The optimal 6-step set points for the water temperature were 40→10→40→10→40→38°C, under the constraint of a fixed water temperature from 10 to 40°C. From simulation, it was confirmed that this operation is effective in maximizing the leaf water content of the tomato. Finally, this operation was applied to a real system. The resulting leaf water content in this operation was about 1.15 times larger than that in a conventional control. It is suggested that this control technique is useful for promoting water uptake of the root and associated higher leaf water content during a short-term period within a day.
影响因子：
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作者：
M. Falah;T. Wajima;D. Yasutake;Y. Sago;M. Kitano
通讯作者：
M. Falah;T. Wajima;D. Yasutake;Y. Sago;M. Kitano
决策和控制系统模仿熟练种植者的思维过程，以动态优化存储环境。
DOI：
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发表时间：
2003
期刊：
Environment Control in Biology 41(3)
影响因子：
--
作者：
Morimoto;T.;Hashimoto;Y.
通讯作者：
Y.