Supervised and unsupervised learning for plant and crop row detection in precision agriculture

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Show simple item record Varshney, Varun 2017-04-21T13:17:26Z 2017-04-21T13:17:26Z 2017-05-01 en_US
dc.description.abstract The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop rows. Aerial images, taken by an Unmanned Aerial Vehicle (UAV), of a corn field at various stages of growth were acquired in RGB format through the Agronomy Department at the Kansas State University. Several segmentation and clustering approaches were applied to these images, namely K-Means clustering, Excessive Green (ExG) Index algorithm, Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and a deep learning approach based on Fully Convolutional Networks (FCN), to detect the plants present in the images. A Hough Transform (HT) approach was used to detect the orientation of the crop rows and rotate the images so that the rows became parallel to the x-axis. The result of applying different segmentation methods to the images was then used in estimating the location of crop rows in the images by using a template creation method based on Green Pixel Accumulation (GPA) that calculates the intensity profile of green pixels present in the images. Connected component analysis was then applied to find the centroids of the detected plants. Each centroid was associated with a crop row, and centroids lying outside the row templates were discarded as being weeds. A comparison between the various segmentation algorithms based on the Dice similarity index and average run-times is presented at the end of the work. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject precision agriculture en_US
dc.subject deep learning
dc.subject machine learning
dc.subject supervised
dc.subject unsupervised
dc.title Supervised and unsupervised learning for plant and crop row detection in precision agriculture en_US
dc.type Thesis en_US Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor William H. Hsu en_US 2017 en_US May en_US

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