Feasibility of Hybrid PSO-ANN Model for Identifying Soybean Diseases

Abstract

Soybean disease has become one of the vital factors restricting the sustainable development of a high-yield and high-quality soybean industry. A hybrid artificial neural network (ANN) model optimized via particle swarm optimization (PSO) algorithm, which is denoted as PSO-ANN, is proposed in this paper for soybean disease identification based on categorical feature inputs. Augmentation dataset is created via synthetic minority over-sampling technique (SMOTE) to deal with quantitative insufficiency and categorical unbalance of the dataset. PSO algorithm is used to optimize the parameters in ANN, including the activation function, the number of hidden layers, the number of neurons in each hidden layer, and the optimizer. In the end, ANN model with 2 hidden layers, 63 and 61 neurons in hidden layers respectively; Relu activation function; and Adam optimizer yields the best overall test accuracy of 92.00%, compared with traditional machine learning methods. PSO-ANN shows superiority on various evaluation metrics, which may have great potential in crop disease control for modern agriculture.