Electronic Theses and Dissertations

Identifier

6627

Date

2020

Document Type

Thesis

Degree Name

Master of Science

Major

Electrical and Computer Engr

Committee Chair

Eddie Jacobs

Committee Member

Lan Wang

Committee Member

Alfredo Ramirez

Abstract

The discrimination between cotton and the invasive Palmer amaranth is economically important, as these weeds take resources away from cotton, resulting in diminished crop yield. There has been research into the discrimination between species of plants, including cotton and Palmer amaranth, that focused on the use of aerial imagery and the derived Red, Green, and near-infrared (RGN) spectral data fed into a machine-learning algorithm to classify these plants based on the measurable differences in their spectral characteristics. We believe that this research can be expanded upon by using geometric data derived from aerial imagery to classify cotton and non-cotton plants based on their physical characteristics. This would also allow for accurate geolocation of the classified weeds for later removal. An autonomous drone with a GPS and a RGN camera attached will take a predetermined path to scan a crop field, and the resulting videos will be divided into individual frames. From these frames, both the RGN spectral data and a 3D point cloud can be derived. The RGN data and the geometric data will be fed into a machine learning algorithm for classification between the cotton and non-cotton plants, and then additional processing will be done to geolocate the weeds. With this additional information for classification, it is hoped that the discrimination between cotton and weeds can be more accurate, and the location of the weeds can be more exact.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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