This study aimed to develop a novel end-to-end plant diagnosis model for the
analysis of plant health conditions in near real-time to optimize the rate of
production on farmlands for an intensive, yet environmentally safe farming
production to preserve the natural environment.
First, field research was conducted to determine the extent of the problems
faced by farmers in agricultural production. This allowed us to refine the
research statement and the level of technology involved in the production
processes. The advantages of unmanned aerial systems were exploited in the
continuous monitoring of farm plantations to develop automated and accurate
measures of farm conditions.
To this end, this thesis applies the Precision Agricultural technology as a data based management system that takes into account spatial variations by using
the Global Positioning System, Geographical Information System, remote
sensing, yield monitors, mapping, and guidance system for variable rate
applications.
An unmanned aerial vehicle embedded with an optic and radiometric sensor
was used to obtain high spectral resolution images of plantation status during
normal production/growth cycle. Then, an ensemble of classifiers with Convolution Neural Networks (CNN) was used as off the shelf feature extractor
to train images to develop an end-to-end feature detection and multiclass
classification system for plant overall health’s conditions. Whereby previous
works have concentrated on using CNN as off the shelf feature extractor and
model training to detect only plant diseases from plants.
To date, no research has yet been carried out to develop an end-to-end model
for the overall plant diagnosis system. Previous studies focused on the
detection of diseases at any given time, making it difficult to implement
comprehensive real-time PA systems.
Applying the pretrained model to the new images showed that the model can
accurately predict any plant condition with an average of 97% accuracy.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/19206 |
| Date | January 2020 |
| Creators | Abdullahi, Halimatu S. |
| Contributors | Abd-Alhameed, Raed, Sheriff, Ray E., Mahieddine, Fatima |
| Publisher | University of Bradford, Faculty of Engineering and Informatics, School of Electrical Engineering and Computer Science |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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