Vineyard Classification Using Machine Learning Techniques Applied to RGB-UAV Imagery

In this study machine learning methods were applied to RGB data obtained by an unmanned aerial vehicle (UAV) to assess this effectiveness in vineyard classification. The very high-resolution UAV-based imagery was subjected to a photogrammetric processing allowing the generation of different outcomes: orthophoto mosaic, crop surface model and five vegetation indices. The orthophoto mosaic was used in an object-based image analysis approach to group pixels with similar values into objects. Three machine learning techniques-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were applied to classify the data into four classes: grapevine, shadow, soil and other vegetation. The data were divided with 22% (n=240, 60 per class) for training purposes and 78% (n = 850) for testing purposes. The mean value of the objects from each feature were used to create a dataset for prediction. The results demonstrated that both RF and ANN models showed a good performance, yet the RF classifier achieved better results.