Document Type
Thesis - Open Access
Award Date
2018
Degree Name
Master of Science (MS)
Department / School
Electrical Engineering and Computer Science
First Advisor
Sung Shin
Abstract
The Precision Agriculture plays a crucial part in the agricultural industry about improving the decision-making process. It aims to optimally allocate the resources to maintain the sustainable productivity of farmland and reduce the use of chemical compounds. [17] However, the on-site inspection of vegetations often falls to researchers’ trained eye and experience, when it deals with the identification of the non-crop vegetations. Deep Convolution Neural Network (CNN) can be deployed to mitigate the cost of manual classification. Although CNN outperforms the other traditional classifiers, such as Support Vector Machine, it is still in question whether CNN can be deployable in an industrial environment. In this paper, I conducted a study on the feasibility of CNN for Vegetation Mapping on lawn inspection for weeds. I want to study the possibility of expanding the concept to the on-site, near real-time, crop site inspections, by evaluating the generated results.
Library of Congress Subject Headings
Vegetation mapping.
Neural networks (Computer science)
Vegetation classification.
Description
Includes bibliographical references
Format
application/pdf
Number of Pages
41
Publisher
South Dakota State University
Recommended Citation
SUH, Sae-han, "Development of Vegetation Mapping with Deep Convolutional Neural Network" (2018). Electronic Theses and Dissertations. 2963.
https://openprairie.sdstate.edu/etd/2963
Included in
Agriculture Commons, Bioresource and Agricultural Engineering Commons, Electrical and Computer Engineering Commons
Rights Statement
