# Coastal Sargassum Level Estimation from Smartphone Pictures
## 1 Introduction  
Since 2011, significant and atypical arrival of two species of surface dwelling algae, _Sargassum natans_ and _Sargassum Fuitans_, have been detected in the Mexican Caribbean Sea [1]. According to [2], the most affected area in Mexico is the coastline between Tulum and Playa del Carmen, including the east coast of Cozummel Island. This massive accumulation of algae has had a great environmental and economic impact, especially for the tourism sector, which is of great importance for the inhabitants of the Mexican Caribbean. In addition to the economic impact on tourism, it can also have a strong impact on the ecosystem: beach erosion due to removal efforts; temperatures that can be lethal for turtle embryos; nearshore fauna, such as coral colonies and seagrasses, can be affected by the change in light condition, oxygen level, and temperature [2; 3; 4].  
Most of the works related to algae detection in the ocean have been carried out using remote sensing. Several research efforts relate to monitoring and detecting Sargassum have been developed in recent years using data from satellites, such as GOCI-II, Landsat-8, or Sentinel-2 [5; 6; 7; 8; 9; 10; 11; 12]. Although remote sensing is a powerful tool, it still has some disadvantages for certain problems, being the main disadvantages the spatial and temporal resolution. For instance, Sentinel-2 has a spatial resolution of 10, 20, and 60 m. [13]. This resolution allows to cover extensive areas in one image, but for problems like Sargassum detection in coastal areas a higher resolution, despite covering smaller areas, could be moreappropriate. Temporal resolution is an even more serious problem, as an example, Sentinel-2 has temporal resolutions of 10 and 5 days, which means that it will take 10 or 5 days before the satellite revisit a particular point on the Earth's surface. Sargassum observation in beach areas requires a more frequent observation of the phenomenon. Another important factor is the meteorological condition in the area of interest, under cloudy conditions during the raining/hurricane season, the information provided by the satellite images would be limited. Another highly relevant factor is the cost of these satellite image databases.  
Given the previously mentioned disadvantages of satellite images, ground-based camera systems can be more adequate to address the problem of Sargassum detection. This approach has been little explored [14; 15], but the few studies carried out have shown promising results. Another aspect that can be exploited is the rapid development of mobile technologies, which has led to the high availability of high-resolution images taken from cell phones [16].  
In this work, we propose an approach for Sargassum amount estimation. We model the problem as a classification task where a set of five labels define the amount of Sargassum in each image. The classification is solved with a supervised learning approach where a convolutional neural network model is trained with several already labeled examples to predict the class on unseen examples. For this purpose, we have built a dataset with more than 1000 images collected from public social network forums such as Facebook and Instagram, labeled into one of 5 categories of Sargassum level (none, low, mild, plenty, and excessive). Additionally, in our experiments, we have compared the performance of four state-of-the-art convolutional neural networks. As a result, our system can provide accurate predictions that can help with the Sargassum monitoring.  
The rest of the paper is organized as follows. Section 2 provides a review of methods that have been proposed to deal with the monitoring of Sargassum. Section 3 describes the neural networks and the dataset used in this study. In Section 4, we detail the proposed method and the experiments that were carry out for validating our proposal. Finally, in Section 5, we provide the conclusions of our study.