Food insecurity is a huge problem affecting developing countries particularly in sub-Saharan Africa. The ability to collect data in resolution and field wide in precision agriculture has attracted the 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 main aim of this research work is to 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.