Research on Flow Decision-Making Model of Plant Protection UAV Based on Feature Selection

Abstract

The field environment is complex and variable, and multiple factors constrain the effectiveness of UAV applications, and a single flow rate application can no longer meet the requirements. Therefore, it is crucial to study a decision-making model of spraying flow for plant protection UAVs under multi-factor interaction. In this paper, based on a large amount of experimental data combined with Pearson correlation analysis and random forest variable importance score ranking, the experimentally acquired features are screened and input into the GA-BP neural network. The model evaluation results showed that the GA-BP neural network model has a correlation coefficient of 0.99 between the true value, predicted value, and a coefficient of determination of 0.98, which is better than the general regression model. A validation test was conducted to test the effectiveness of the model for new data. The final result yields an error value within ±2 0% for the GA-BP model to predict the flow rate. At the same time, the BP neural network fluctuated more for some of the predicted values, which caused a 50% increased error in fitting results. It proves the feasibility of the BP neural network optimized based on a genetic algorithm in plant protection UAV flow rate decisionmaking, which can provide a reference basis and scientific guidance for precise variable spraying operation of plant protection UAVs.INDEX TERMS plant protection drone; BP neural network; genetic algorithm; variable spraying; decision model; spraying flow rate