Title :
Plant counting by using k-NN classification on UAVs images
Author :
Tavus, Mustafa Resit ; Eker, Muhammed Emin ; Senyer, Nurettin ; Karabulut, Bunyamin
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Ondokuz Mayis Univ., Samsun, Turkey
Abstract :
In this study, Plant Counting was implemented by appling k-NN classification to images obtained from Unnamed Air Vehicle (UAV). Firstly, The images were subjected to erosion process by transforming different colour levels. The objects in the images were classified as plant and soil by means of k-NN classification. It was observed that plants can be counted with 87,7% of accuracy and 86,6% of precision by being performed last processing of the morphology of the binary image.
Keywords :
autonomous aerial vehicles; image classification; pattern recognition; surveying; UAV images; binary image; k-NN classification; plant counting; unnamed air vehicle; Accuracy; Image color analysis; Remote sensing; Soil; Vegetation; Vehicles; image processing; k-NN algorithm; plant counting;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7130015
