Performance of machine learning algorithms for detecting pine wilt disease infection using visible color imagery by UAV remote sensing

Abstract

Automatic detection of pine wilt disease will be useful in forest management, as it can prevent the spread of epidemics and resulting huge economic losses. Previous studies have reported the precise pine wilt disease detection by combining machine learning algorithms and hyperspectral sensors with platform of unmanned aerial vehicles (UAVs). However, these approaches require expensive hardware and advanced processing techniques, rendering them unsuitable for practical use in on-site forest management. This study deals with the simplification of the automatic detection method, which enables practical countermeasures to be taken by forest management entities. An optimal method for detecting PWD using images obtained by a simple unmanned aerial vehicle (UAV)-mounted visible color sensor is presented in this study. The method combines six major machine learning algorithms: logistic regression, linear support vector machine, support vector machine, k-nearest neighbors, random forest, and artificial neural network, with both RGB and HSV color space datasets. The performance of the six algorithms was validated using the metrics of accuracy, precision, recall, F1-scores, and area under the receiver operating characteristic curve with their 95% confidence intervals. The experimental results show that the combination of the HSV color space dataset with the artificial neural network algorithm is the best performer algorithm (accuracy: 0.995). Moreover, logistic regression, linear support vector machine, support vector machine, and artificial neural network have equally high detection accuracy in the RGB color space (accuracy: 0.990, 0.990, 0.990, 0.985, respectively), with a low possibility of overfitting. These results indicate that the generally used visible color sensors mounted on UAV, which are available at a low cost, can be used for highly accurate detection.