Prediction Model of Soil Electrical Conductivity
Based on ELM Optimized by Bald Eagle
Search Algorithm
Ying Huang, Hao Jiang, Weixing Wang and Daozong Sun
Soil electrical conductivity is one of the indispens-
able and important parameters in fine agriculture management,
and a suitable soil electrical conductivity can promote good plant
growth. Prediction model of soil electrical conductivity is con-
structed to effectively obtain the conductivity values of soil, which
can provide a reference basis for irrigation and fertilization man-
agement and prediction evaluation in fine agriculture. Prediction
model of soil electrical conductivity based on extreme learning
machine (ELM) optimized by bald eagle search (BES) algorithm is
proposed in this paper. In the prediction model, the input weights
and bias values of the ELM network were optimized using the BES
algorithm, and the performance of the model was evaluated with
parameters such as mean square error (MSE), coefficient of de-
termination (R 2 ). Also, the correlations of parameters such as soil
temperature, moisture content, pH, and water potential in the soil
conductivity prediction model were determined using the explor-
atory data analysis (EDA) and HeatMap heat map tools. Finally,
the proposed model was compared with back propagation neural
network (BP), radial basis function networks (RBF), support vec-
tor machine (SVM), gated recurrent neural network (GRNN), long
short-term memory (LSTM), particle swarm algorithm (PSO) op-
timization ELM, genetic algorithm (GA) optimized ELM predic-
tion model. The experimental results showed that MSE, R 2 of the
proposed model are 4.09 and 0.941, which are better than the other
models. Also the results verified the effectiveness of the proposed
method, which is a feasible prediction method to guide the irriga-
tion and fertilization management in fine agriculture, because of
its good prediction effect on soil conductivity.