Abstract:
Food insecurity is a huge problem that is affecting countries under development particularly in
sub-Saharan Africa. The ability to collect data with high resolution and field wide in precision
agriculture has come to attention of key players in agronomic crop production as well as in
agronomical research due to its high accuracy and efficiency compared to the traditional methods
used to be popular over the past years. The most important objective of this research work is to
establish a model that would enhance food security measures through the integrated use of
Unmanned Aerial Vehicle (UAV) as an IoT system and transfer learning based on Convolution
Neural Network (CNN) model to classify and monitor the crop conditions for earlier decisions
making when necessary. The proposed system will put in place methods to monitor crop conditions
while predicting the presence of Fall Army Worm (FAW) in crops for farmers and government to
act accordingly.
In the following master thesis, an IoT based UAV system is integrated with machine learning
techniques in order to increase crop production and reduce hunger that has been found in some
area of the country. The use of UAV with elevated multispectral camera for agricultural practices
provides spatial, spectral, and ground data used for monitoring and analyzing crop’s conditions,
for the increased crop production. This work mainly proposed and analyzed data on FAW
classification and presence in maize crop by utilizing transfer learning approach based on fine
tunned Inception V3 pre-trained model. Range of numerical computations are performed to
evaluate the performance of the proposed model.
