Bird's-Eye View Images Taken Plant Material and Counting

Abstract

In this study, the recognition of agri-food plants out of the images obtained by the UAV and are intended to implement the counting process. Images obtained with the UAV from plants separation from the background; K-Means (K-Means) with the help of visual elements in the classifier was classified as soil and plants. A better image segmentation and noise in the resulting plants were made to eliminate the morphological filtering. The plants on the noise-free image separation nested data for individual numbers of watershed algorithm was applied. To represent the resulting plant was subjected to the binary image acquisition and counting process. Plant identification methods applied and the counting process accuracy and 87.7% sensitivity, 86.6% were found to implement.