Abstract: In order to achieve a more effective application of a seed product and/or crop nutrition product, a computer-implemented method is provided for applying a seed product of at least one crop and/or applying crop nutrition product to at least one crop in a field.

Abstract: A method performed by at least one apparatus is inter alia disclosed, said method comprising: obtaining weather model data indicative of location-specific weather information for a first set of locations (26) on a first grid (28); obtaining an area of interest (30) associated to at least one user (32); obtaining and/or determining a second set of locations (34) based on a second grid (36) within said area of interest (30); obtaining measurement data on location-specific weather information of a measurement device associated to said at least one user located at a measurement location (38) within and/or proximate to said area of interest (30); and determining, based on at least said obtained weather model data and said obtained measurement data, location-specific weather information for said second set of locations (34) based on said second grid (36).

Abstract: The present invention relates to a method for treatment of an agricultural field, the method comprising the steps: 1) receiving (S10) a parametrization (10) for controlling a treatment device (200) by the treatment device (200) from a field manager system (100); 2) receiving (S20) from at least one soil sensor (400) real-time soil information on the real-world situation of the geographical location G1 in the agricultural field; 3) processing (S30) the real-time soil information to generate processed information (30), 4) determining (S40) a control signal (50) for controlling a treatment arrangement (270) of the treatment device (200) based on the received parametrization (10) and the processed information (30), 5) executing (S50) a treatment on the geographical location G2 in the agricultural field, wherein the treatment is executed based on the control signal (50) real-time after receiving the real-time soil information in such a way that the distance between location G1 and location G2 does not exceed 100 m

Abstract: A method for estimating precipitation distribution for a geographical region comprising the steps of: providing precipitation data (S10) for the geographical region with a first spatial resolution for a predetermined period of time (t1, t2); providing first soil moisture data (S20) for the geographical region for a first point in time (t3) with a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the first point in time (t3) is within the predetermined period of time (t1, t2); providing second soil moisture data (S30) for the geographical region for a second point in time (t4) with a third spatial resolution, wherein the third spatial resolution is higher than the first spatial resolution, and wherein the second point in time (t4) is within the predetermined period of time (t1, t2); calculating soil moisture difference data (S40) between the first soil moisture data and the second soil moisture data; calculating precipitation distribution

Abstract: In order to achieve a more effective application of a crop efficiency product, a computer-implemented method is provided for applying a crop efficiency product to at least one crop in a field.

Abstract: A method for correcting remote sensor data of an agricultural field, the method comprising the following steps: receiving remote sensor data (DR) for the agricultural field from a remote sensor, wherein the remote sensor data (DR) comprises at least one remote measurement value corresponding to at least one location that is measured by the remote sensor at at least one point in time of obtaining the remote measurement value; receiving local sensor data (DL) for the agricultural field from at least one local sensor, wherein the at least one local sensor data (DL) comprises at least one local measurement value corresponding to at least one location of the at least one local sensor and corresponding to at least one point in time of obtaining the local measurement value correlating to the location and point of time of obtaining the remote measurement value; determining a correction model based on the previously received local sensor data (DL) and the previously received remote sensor data (DR); and determining co

Abstract: A method performed by at least one apparatus is inter alia disclosed, said method comprising: obtaining weather model data indicative of location-specific weather information for a first set of locations (26) on a first grid (28); obtaining an area of interest (30) associated to at least one user (32); obtaining and/or determining a second set of locations (34) based on a second grid (36) within said area of interest (30); obtaining measurement data on location-specific weather information of a measurement device associated to said at least one user located at a measurement location (38) within and/or proximate to said area of interest (30); and determining, based on at least said obtained weather model data and said obtained measurement data, location-specific weather information for said second set of locations (34) based on said second grid (36).

Abstract: The invention relates to training a machine learning algorithm and predicting a value for a weather data variable, preferably at a field or sub-field level.