The Detection of Vegetation Species in Remote Sensing Imaging
type: CreativeWork
description: Remote sensing classification is a complicated process and requires the perception of many factors. The main image classification factors that can be taken into consideration to improve classification accuracy may include placement of a sufficient classification system, chosen of training samples, feature extraction, and selection of appropriate classification techniques. The focus of the research in this thesis is on the monitoring of cereal crops, in particular infestation of wheat crops by black grass, a pernicious weed. This is regarded as the major pest for cereal crops, at least in East Anglia, one that causes significant loss of income for farmers. Although annual rather than perennial, a single back grass plant is able to produce many thousands of seeds, which are small enough to be distributed by the wind, though it is believed that the principal means by which it spreads from field to field is by sticking inside farm machinery such as combine harvesters. Anecdotal evidence from farmers is that black grass plants tend to grow directly adjacent to wheat plants, with their roots enveloping those of the wheat and thus effectively strangling the wheat. Black grass is resistant to most weed-killers, and spraying with those weed-killers that are allowed requires a licence — this makes eradicating it difficult. When young black grass plants are potentially detectable because they do not grow in the regular pattern of the wheat plants; but after germination, they tend to be smaller than adjacent wheat plants until they reach maturity, at which time they are slightly taller and with their characteristic black seed-heads. Thus the purpose of this thesis is to explore the detection of black grass infestations of wheat crops. Imagery of fields infested with black grass plants is simply not available at the kinds of spatial resolution needed for detection, so an important part of the research was the collection of imagery from a drone. This was done over more than one y…
abstract: Remote sensing classification is a complicated process and requires the perception of many factors. The main image classification factors that can be taken into consideration to improve classification accuracy may include placement of a sufficient classification system, chosen of training samples, feature extraction, and selection of appropriate classification techniques. The focus of the research in this thesis is on the monitoring of cereal crops, in particular infestation of wheat crops by black grass, a pernicious weed. This is regarded as the major pest for cereal crops, at least in East Anglia, one that causes significant loss of income for farmers. Although annual rather than perennial, a single back grass plant is able to produce many thousands of seeds, which are small enough to be distributed by the wind, though it is believed that the principal means by which it spreads from field to field is by sticking inside farm machinery such as combine harvesters. Anecdotal evidence from farmers is that black grass plants tend to grow directly adjacent to wheat plants, with their roots enveloping those of the wheat and thus effectively strangling the wheat. Black grass is resistant to most weed-killers, and spraying with those weed-killers that are allowed requires a licence — this makes eradicating it difficult. When young black grass plants are potentially detectable because they do not grow in the regular pattern of the wheat plants; but after germination, they tend to be smaller than adjacent wheat plants until they reach maturity, at which time they are slightly taller and with their characteristic black seed-heads. Thus the purpose of this thesis is to explore the detection of black grass infestations of wheat crops. Imagery of fields infested with black grass plants is simply not available at the kinds of spatial resolution needed for detection, so an important part of the research was the collection of imagery from a drone. This was done over more than one y…
author: Azeez, Nassr
language: en
date published: 2021-03
date modified: 2026-07-24T02:18:40Z
provider: University of Essex
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Essex