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Unmanned aerial vehicles (UAV) are used successfully in many applicationareas such as military, security, monitoring, emergency aid, tourism,agriculture, and forestry. This study aims to automatically count trees indesignated areas on the Siirt University campus from high-resolution imagesobtained by UAV. Images obtained at 30 meters height with 20% overlap werestitched offline at the ground station using Adobe Photoshop's photo mergetool. The resulting image was denoised and smoothed by applying the 3x3 medianand mean filter, respectively. After generating the orthophoto map of theaerial images captured by the UAV in certain regions, the bounding boxes ofdifferent objects on these maps were labeled in the modalities of HSV (HueSaturation Value), RGB (Red Green Blue) and Gray. Training, validation, andtest datasets were generated and then have been evaluated for classificationsuccess rates related to tree detection using various machine learningalgorithms. In the last step, a ground truth model was established by obtainingthe actual tree numbers, and then the prediction performance was calculated bycomparing the reference ground truth data with the proposed model. It isconsidered that significant success has been achieved for tree count with anaverage accuracy rate of 87% obtained using the MLP classifier in predeterminedregions.

Development of Automatic Tree Counting Software from UAV Based Aerial Images With Machine Learning