Abstract
Long-term grain storage solutions use spacious facilities holding thousands of tons of different commodities. Molds can contaminate the stored grain developing local zones of high temperature, i.e. hot-spots. Continuous temperature monitoring is therefore important to prevent substantial damage to the stored commodities. However, it is hard to do so due to the immense scale and uneven surface of the area of storage. We propose a method that uses surface temperature monitoring by an infrared camera from an autonomous flying drone, cable car, or autonomous grain vehicle. A temperature map with identified anomalous areas will enable timely measures to avert damage to the stored agricultural products. This paper consists in creating a heat-transfer model of the stored grain. For this purpose we constructed and performed a small-scale experiment in the first stage of the presented project. From the collected data, a heat transfer model was identified.
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