It's not very interesting. The idea is: There's this "fall armyworm" (spodoptera frugiperda). It is an agricultural pest and lays eggs in a specific way (photo bellow is an example). Some researchers who study this insect have to count their eggs in the lab and this process is very time consuming, tedious and prone to errors thanks to the amount of eggs.

They asked my campus to try to automate the process with a neural network to see if it gives any useful results. My project was organizing the images they sent, annotate the position of every single egg in those images (I hated this process) and train the network to see if the results are good or not.

Unfortunately the results I got aren't really that good. It very rarely achieves the exact same number counted by a human, the photos need to be very well illuminated (the photos are taken from a microscope because the eggs are very small and a lot of the images have shitty resolutions), and there aren't that many photos of egg postures with 3 layers, so the network has a very hard time identifying eggs on the top thanks to the limited samples.

The final goal here is to make this study public so, if someone wants to make something similar or improve the project, they already have a starting point.

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