methods empirical relationships between yield and production indices calculated experimentally, structural parameters measured or calculated through specific experiments (not dynamic), etc. These methods lose precision depending on the type of plant, cultural methods and practices and the seasons. Then, it becomes urgent to develop a dynamic estimation method with a proven track record of reliability despite the inconsistency of the factors mentioned above. This article contributes to the improvement of aquatic biomass estimation by proposing a Computer Vision based solution for estimating fresh mass of water hyacinth. To achieve this goal, the morphology of the species is assessed and an XML classifier is developed. This model is then implemented in a mobile app facilitating its end use. The proposed algorithm demonstrated a mean average precision of 96.89%. Considering the recorded level of accurateness, the developed method can be used to estimate different types of biomass.