Meta-learning Prediction of Physical and Chemical Properties of Magnetized Water and Fertilizer Based on LSTM

Abstract BackgroundDue to the high cost of data collection and labeling for magnetization detection of medium, the sample size is limited, it is not suitable to use deep learning method to predict its change trend. The prediction of physical and chemical properties of magnetized water and fertilizer(PCPMWF) by meta-learning can help to explore the effects of magnetized water and fertilizer irrigation on crops. MethodIn this article, we propose a meta-learning optimization model based on the meta-learner LSTM in the field of regression prediction of PCPMWF. In meta-learning, LSTM is used to replace MAML's gradient descent optimizer for regression tasks, enables the meta-learner to learn the update rules of the LSTM, and apply it to update the parameters of the model. The proposed method is compared with the experimental results of MAML and LSTM to verify the feasibility and correctness.ResultsThe average absolute percentage error of the meta-learning optimization model of meta-learner LSTM is reduced by 0.37% compared with the MAML model, and by 4.16% compared with the LSTM model.ConclusionsIn the case of few sample, the proposed model is superior to the traditional LSTM model and the basic MAML model.