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.

A self-consistent system of nonlinear spinor and gravitational fields, modeled by static spherical symmetric metric, is considered and studied. Exact spherical symmetric solutions of nonlinear spinor field equations in the Gravitational Theory are obtained. The nonlinearity in the spinor lagrangian is given by an arbitrary function which depends on the invariant generated from the Fierz-Pauli bilinear spinor form IS = S2. It is shown that a soliton-like configuration has a localized energy density and a finite total energy. In addition, The total charge and total spin are also finite. Let us emphasize that the effect of gravitational field on the properties of regular localized solutions significantly depends on the symmetry of the system. The nonlin- ear terms, the gravitational field of elementary particles and the geometrical properties of the metric of the space-time play an important role in the obtaining of analytical solutions having the soliton-like configuration. Let us emphasize that the numerical solutions of the solutions obtained here are presented in graphical form.

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The different controls of water hyacinth, an invasive species of tropical and subtropical environ-ments, have demonstrated some limitations requiring additional monitoring tasks to maintain the ecological balance. Therefore, quantifying and valuing this aquatic biomass becomes a sustainable management alternative. However, the water hyacinth estimation remains a challenging task in developing countries with regard to the used 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.