| dc.description.abstract | The agricultural industry has been, for a long time, dependent upon
human expertise to detect plant disease. However, human experts may take
years of training and can be inconsistent, as well as prone to fatigue.
Presented in this thesis is the work conducted on utilising electronic nose
incorporating artificial intelligence to detect plant malaise, specifically, basal
stem rot (BSR) disease that is caused by Ganoderma boninense, a type of fungi
affecting oil palm plantations in South East Asia. A commercial electronic nose,
Cyranose 320, was used as the front-end sensors with artificial neural networks
trained using Levenberg-Marquardt algorithm employed for decision making.
For the first stage, a study on Cyranose 320 embedded pattern recognitions
and artificial neural networks (ANNs) was conducted using a few types of
essences. This stage confirmed that the ANNs is better than the embedded
pattern recognitions in terms of accuracy and hence should be used for the next
experiments. The second stage involved the Ganoderma boninense fruiting
bodies detection in laboratory and oil palm plantation. This stage proved that
the fungi odour can be detected after being tested using a few types of odour
parameter. The next stage is to discriminate the healthy and non-healthy oil
palm trunk in the plantation. The conducted work indicates that the combination
of the electronic nose and ANNs has the ability to discriminate the infected
trunk. The findings of the work were also used to develop an in-house low cost
electronic nose to support further fundamental study and implementations. As a
conclusion, this work confirms that it is feasible to utilise the electronic nose and
ANNs to detect and discriminate the BSR disease both in the laboratory and in
the plantation. | en_US |
