Feasibility of Hybrid PSO-ANN Model for Identifying Soybean Diseases

Peng Liu,Miaomiao Ji,Qiufeng Wu

doi:10.4018/ijcini.290328

Abstract

Soybean disease has become one of vital factors restricting the sustainable development of 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 diseases 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 diseases control for modern agriculture.

Highlights

Soybean, as one of the important grain and oil crop in the world, plays an important role in the world’s agricultural production and trade (Wu, Zhang, & Meng, 2019)
particle swarm optimization (PSO) algorithm is used to optimize the parameters in artificial neural network (ANN), including the activation function, the number of hidden layers, the number of neurons in each hidden layer, and the optimizer
This paper presents an application of PSO-based multi-layer perceptron (MLP) neural network in soybean diseases identification

Summary

Introduction

As one of the important grain and oil crop in the world, plays an important role in the world’s agricultural production and trade (Wu, Zhang, & Meng, 2019). Various soybean diseases have constrained sustainable development of high-yield and high-quality soybean industry for a long time. There will likely be a large increase in the demand for soybean with the growth of population and economy. For another thing, soybean diseases have characteristics of large variety, great impact and local outbreaks, which have been responsible for productivity and quantitative losses in crop yield. Classification algorithms play a substantial role in crop diseases identification. With the development of sophisticated instruments and fast computational techniques, the application of machine learning technologies to diagnose crop diseases has become one of the important research

Methods

Results

Conclusion