Abstract
Controlling soil moisture is crucial in optimizing watering and crop performance. Traditional monitoring systems rely on a single sensor or on a column of sensors that do not allow farmers to properly capture soil moisture dynamics in the soil volume occupied by roots. In this paper we propose PLUTO, an original approach that builds fine-grained 2D and 3D soil moisture profiles by relying on a grid of sensors. Profiles are computed using both interpolation-based and machine learning approaches. Besides the technical description of the approach, the paper reports a set of original visualizations and a large set of tests computed, over two years, on real Kiwi orchards. PLUTO proved to largely overcome the accuracy of profiles obtained with traditional sensor layouts. Considering that the cost of sensors is progressively decreasing, PLUTO provides a cost-effective, operative, and precise solution to moisture monitoring.
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