Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery

The disease of banana Fusarium wilt currently threatens banana production areas all over the world. Rapid and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting adjustments. The objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms to identify locations that were infested or not infested with Fusarium wilt. An unmanned aerial vehicle (UAV) equipped with a five-band multi-spectral sensor (blue, green, red, red-edge and near-infrared bands) was used to capture the multi-spectral imagery. A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium wilt. The results showed that the SVM, RF, and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral imagery. The overall accuracies of the SVM, RF, and ANN were 91.4%, 90.0%, and 91.1%, respectively for the pixel-based approach. The RF algorithm required significantly less training time than the SVM and ANN algorithms. The maps generated by the SVM, RF, and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm(2), accounting for 36.3%-40.1% of the total planting area of bananas in the study area. The results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%. A simulation of the resolutions of satellite-based imagery (i.e., 0.5 m, 1 m, 2 m, and 5 m resolutions) showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium wilt. The results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing imagery. The results provide guidance for disease treatment and crop planting adjustments.