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Machine Learning Based Water Analysis Using an Underwater Robot
Water Contamination is an important issue in many cases, for example aquaculture and aquaponics and dealing with it requires to recognize and identify it. While some approaches for water analysis exist, they either are non-spatial or only use 2D data. To solve that issue, this thesis deals with developing an approach that can transform spatial data into features and then use them with standard classifiers to classify different kinds of contamination, as well as introduce requirements for adoi:10.5445/ir/1000141250 fatcat:tekosyrlofaqbgkweewlripvce