基于植物电信号的环境因子预测模型

Environment Factor Prediction Models Based on Plant Electrical Signals

摘要:

以采集的植物电信号为生理指标，综合分析其时域、频域和时频域中的典型特征值，利用学习速度快、泛化性能好的极限学习机算法，以电信号的多个特征及环境参数作为输入量，建立适合植物生长的环境因子（温度、湿度、光照度）预测模型。结果表明：通过对采集的碧玉叶面电信号进行不同域的分析，得出植物电信号属于低频微弱信号；利用极限学习机(ELM)分别对适合碧玉生长的温度、湿度及光照度3个环境因子建立预测模型，通过与传统的BP神经网络对比，ELM算法下的均方根误差小于0.97，而决定系数大于0.92，训练所需的时间低于0.03s，验证了此方法的可行性，为科学指导温室环境因子调控提供科学依据。

Abstract:

The typical characteristic values of electrical signals in plant from time domain, frequency domain and time-frequency domain were analyzed and the electrical signals in plant were to be as physiological indicators. To establish environment prediction models, typical features of electrical signals and some environmental parameters were chosen to be as input of neural network with the extreme learning machine algorithm characterized by fast learning speed and good generalization. The results showed that the plants electrical signals were the low-frequency weak signals by analysis of the electrical signals in Peperomia tetraphylla leaf on different domains, and by extreme learning machine three prediction models such as temperature, humidity and illumination were established to make plants grow well. Compared with the traditional BP neural network, the root mean square error with ELM algorithm is less than 0.97, while the coefficient of determination is more than 0.92 and each training time is less than 0.03s. This method provided the scientific basis for greenhouse environmental regulating and was verified to be feasible.