Olive tree crown detection, delineation and counting by using image processing techniques

UAVs are rapidly improving and increasing to enhance satellite based remote discovery. UAV (Unmanned Aerial Vehicle) remote sensing and low altitude remote sensing (LARS) applications play a significant role in the environment. This thesis shows the utilization of LARS in agriculture especially in the farming for detection and counting the "olive trees" by the proposed algorithms in this study. The image acquired by RGB camera and the processing of detection and counting was by utilizing Digital Image Processing techniques and Machine Learning algorithms. Several methods preformed to get better results, the first method utilized Uneven Illumination Correction, Gaussian filter, Standard Deviation filter and Circular Hough Transform (CHT) to detect olive trees. The second method utilized Histogram Equalization (HE), Mean, Median, and Wiener filters with 3 unsupervised machine learning algorithms (Clustering technique) are K-means, Fuzzy C–means (FCM), and Expectation Maximization (EM) algorithms and Morphological Operations then Circular Hough Transform. The third method implemented supervised machine learning algorithms (Classification) are K-Nearest Neighbor, Linear Discriminant Analysis, and Support Vector Machine.