  Since 2011, significant and atypical arrival of two species of surface
dwelling algae, Sargassum natans and Sargassum Fluitans, have been detected in
the Mexican Caribbean. This massive accumulation of algae has had a great
environmental and economic impact. Therefore, for the government, ecologists,
and local businesses, it is important to keep track of the amount of sargassum
that arrives on the Caribbean coast. High-resolution satellite imagery is
expensive or may be time delayed. Therefore, we propose to estimate the amount
of sargassum based on ground-level smartphone photographs. From the computer
vision perspective, the problem is quite difficult since no information about
the 3D world is provided, in consequence, we have to model it as a
classification problem, where a set of five labels define the amount. For this
purpose, we have built a dataset with more than one thousand examples from
public forums such as Facebook or Instagram and we have tested several
state-of-the-art convolutional networks. As a result, the VGG network trained
under fine-tuning showed the best performance. Even though the reached accuracy
could be improved with more examples, the current prediction distribution is
narrow, so the predictions are adequate for keeping a record and taking quick
ecological actions.
