Classification and Predicting Abundance of Anopheles Mosquitoes in Zimbabwe Using Machine Learning

This study seeks to utilize Machine Learning techniques on entomology data available in Zimbabwe. Much of the data is just lying around so it end up being of no use. By applying machine learning models to analyze the data in a time series manner and data mining techniques in Entomological research is a key approach to utilizing large volumes of available mosquito-related data for extracting knowledge. The objectives of the research are to (1) design a model to classify mosquito species from images; (2) use data mining to identify presence/absence of malaria vectors in an area; and (3) find out which months when malaria vectors are in abundance. The study clearly pointed out that ML can be used come up with solution in solving complex problems found in fighting Malaria. I particular coming up with models that can classify mosquito species from images which is the primary objective. Even though the images used were not from Zimbabwe, but the mere fact that it worked well means that it can used by images of species found in Zimbabwe.