Abstract: Disclosed herein is a method for monitoring an industrial plant including plant objects, where the following steps are carried out: receiving (S1) a video stream (VS) of the plant generated with the aid of a video camera device; assigning (S2) two-dimensional image components (PT) of the video stream (VS) to plant objects (A?, B?, C?, D?) with the aid of a three-dimensional (3D) model (CAD) of the plant; determining (S3) a recording property of the image data depending on the image components (PT) assigned to one or more plant objects (C?) and the respective object property (XYZ); and outputting (S4) the video stream (VS?) together with the determined object properties (XYZ) and/or recording properties. Further disclosed herein are a computer program product and an apparatus.

Abstract: A computer-implemented method (100) for estimating a consumption of an agricultural product for an area of a geographical region cultivated with a specific crop, the method comprising the steps: providing (110) crop growth index data for the geographical region; determining (120) an area of the geographical region cultivated with a specific crop at least based on a comparison of the provided crop growth index data with plant-specific reference data; providing (130) a product consumption model for the agricultural product configured to estimate a consumption of the agricultural product at least based on the area of the geographical region cultivated with the specific crop; providing (140) an estimation of the consumption of the agricultural product for the determined area of the geographical region cultivated with the specific crop for the geographical region at least based on the determined area cultivated with the specific crop using the product consumption model.

Abstract: The present teachings relate to a method comprising a plurality of sensors, and one or more functionally connected processing units, the method comprising: providing, at any of the one or more processing units, time-series residual data of a sensor object; the sensor object being a group of at least some of the sensors from the plurality of sensors, and wherein the residual data comprises, for each of the sensors of the sensor object, a residue signal which is a difference between the sensor's measured output and the sensor's expected output, monitoring, via any of the one or more processing units, a level signal; wherein the level signal is indicative of a collective time-based variation of the time-series residual data, monitoring, via any of the one or more processing units, an association signal; wherein the association signal is indicative of the variation and/or association structure of the time-series residual data, generating, via any of the one or more processing units, an anomaly event signal when a

Abstract: The present teachings relate to a method comprising a plurality of sensors, and one or more functionally connected processing units, the method comprising: providing, at any of the one or more processing units, time-series residual data of a sensor object; the sensor object being a group of at least some of the sensors from the plurality of sensors, and wherein the residual data comprises, for each of the sensors of the sensor object, a residue signal which is a difference between the sensor's measured output and the sensor's expected output, monitoring, via any of the one or more processing units, a level signal; wherein the level signal is indicative of a collective time-based variation of the time-series residual data, monitoring, via any of the one or more processing units, an association signal; wherein the association signal is indicative of the variation and/or association structure of the time-series residual data, generating, via any of the one or more processing units, an anomaly event signal when a