BASF Agro Trademarks GmbH

A computer-implemented method for generating a control file usable for controlling an agricultural equipment based on at least one treatment parameter, comprising the following steps: (step 1 ) ( 110 ) providing genetic measurement data ( 40 ) of at least one organism which existed or is existing or is expected to exist in the agricultural field, (step 2 ) ( 120 ) providing treatment parameter data ( 42 ) for at least two treatment parameters capable of targeting the at least one organism, (step 3 ) ( 130 ) based on the treatment parameter data ( 42 ), providing first level efficacy data ( 44 ) comprising efficacies (“first level efficacies”) of the at least two treatment parameters relating to the at least one organism on a first level of the taxonomic rank, (step 4 ) ( 140 ) based on the treatment parameter data ( 42 ) and the first level efficacy data ( 44 ), determining a first ranking ( 46 ) of the at least two treatment parameters, (step 5 ) ( 150 ) providing an efficacy adjustment model ( 50 ), (step 6 ) ( 160 ) by modifying the first level efficacy data ( 44 ) based on the genetic measurement data ( 40 ) and the treatment parameter data ( 42 ) via the efficacy adjustment model ( 50 ), obtaining second level efficacy data ( 52 ) comprising efficacies (“second level efficacies”) of the at least two treatment parameters relating to the at least one organism on a second level of the taxonomic rank being below the first level of the taxonomic rank, (step 7 ) ( 170 ) based on the treatment parameter data ( 42 ) and the second level efficacy data ( 52 ), determining a second ranking ( 54 ) of the at least two treatment parameters. (step 8 ) ( 180 ) outputting the highest ranked or user-selected treatment parameter as a control file usable for controlling an agricultural equipment

A method for generating a zone specific application map ( 8 ) for treating an agricultural field with products is provided. The method comprises providing a hypermodel ( 1 ) comprising a product recommendation model, PRM ( 2 ) and a biophysical parameter model, BPM ( 3 ). The method further comprises providing PRM input parameters ( 4 ) for the product recommendation model ( 2 ) and generating PRM output ( 5 ) by the product recommendation model ( 2 ). The method also comprises providing BPM input parameters ( 6 ) for the biophysical parameter model ( 3 ) and generating BPM output ( 7 ) by the biophysical parameter model ( 3 ). Finally, the method comprises generating the zone specific application map ( 8 ) by the hypermodel ( 1 ), using at least parts of the PRM output ( 5 ) and parts of the BPM output ( 7 ). Further, a system ( 19 ) for generating a zone specific application map ( 8 ), a computer program element, a use of a zone specific application map ( 8 ) and an agricultural equipment ( 23 ) are provided.

The present invention relates to the technical field of precision farming. The present invention provides a method, a computer system and a computer program product with which a digital cultivation map for a field is created, said cultivation map comprising multiple zones, with at least one feature of at least one zone matched to at least one value of a machine working parameter that has been obtained from an image of the field.

Computer-implemented method for providing corrected plant-related index data, comprising the steps of: providing initial plant-related index data for an agricultural field, preferably based on at least one satellite image; and correcting the initial plant-related index data for the agricultural field at least based on historical plant-related index data for the agricultural field and providing corrected plant-related index data.

A method for generating a crop phenology prediction ( 7 ) is provided. The method comprises the steps of providing crop phenology training data ( 1 ) for a plurality of crops and a plurality of locations ( 17 ); training a machine learning system ( 6 ) using the crop phenology training data ( 1 ); providing a selection of the plurality of crops and a specific location ( 18 ); and generating a crop phenology prediction ( 7 ) for a selection of the plurality of crops at a specific location ( 18 ) using the trained machine learning system ( 6 ). Further, a system ( 21 ) for generating a crop phenology prediction ( 7 ) is provided. The system ( 21 ) comprises at least one input interface ( 25 ) for providing a selection of crops and a specific location ( 18 ), at least one processing unit ( 22 ) configured to carry out the method for generating a crop phenology prediction ( 7 ) and at least one output interface ( 23 ) for outputting the crop phenology prediction ( 7 ), the agronomic recommendation ( 8 ) and/or the agronomic control data ( 26 ) for the selection of crops at the specific location ( 18 ). Further, a computer program element, a use of a crop phenology prediction ( 7 ) and a use of agronomic control data ( 26 ) are provided.

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. The method comprises the steps of collecting remotely-sensed data of the field before an application of the seed product and/or crop nutrition product in the field, determining, based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field, generating, for each of the plurality of locations, a predicted yield response to the application of the seed product and/or crop nutrition product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the seed product and/or crop nutrition product, deciding, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.

Computer-implemented method for generating control data configured to be used or usable in an agricultural equipment for treating a field, comprising the following steps: (S 10 ) providing crop data; (S 20 ) providing field data; (S 30 )—optionally—providing historic treatment data; (S 40 )—optionally—providing environmental data; (S 50 ) at least based on the crop data and on the field data, initiating and/or performing data processing in at least one database and/or database system, (S 60 ) determining the nutrient-specific risk based on the result of the data processing, (S 70 ) providing and/or determining the nutrient-specific threshold, (S 80 ) determining, based on the nutri-ent-specific risk and the nutrient-specific threshold and based on the data processing in at least one treatment-related database, at least two treatment schedules capable of targeting the at least one nutrient, (S 90 ) ranking the at least two treatment schedules, based on one or more of the specific statistics (Q1) to (Q28), (S 100 ) outputting the ranked list of the at least two treatment schedules, (S 110 ) generating control data configured to be used or usable in an agricultural equipment, wherein the control data is based on the highest ranked treatment schedule from the ranked list, or a treatment schedule selected by a user from the ranked list.

Computer-implemented method for generating control data configured to be used or usable in an agricultural equipment for treating a field, comprising the following steps: (S 10 ) providing crop data, wherein the crop data comprise information about an agricultural crop species providing crop data grown or sown or planned to be grown or sown in a field; (S 20 ) providing field data, wherein the field data comprise information about the field; (S 30 )—optionally—providing historic treatment data, (S 40 )—optionally—providing environmental data, (S 50 ) at least based on the crop data and on the field data, initiating and/or performing data processing in at least one database and/or database system, (S 60 ) determining the organism-specific risk based on the result of the data processing, (S 70 ) providing and/or determining the organism-specific threshold, (S 80 ) determining, based on the organism-specific risk and the organism-specific threshold and based on the data processing in at least one treatment-related database, at least two treatment schedules capable of targeting the at least one organism, (S 90 ) ranking the at least two treatment schedules, based on one or more of the specific statistics (Q 1 ) to (Q 28 ): (S 100 ) outputting the ranked list of the at least two treatment schedules, (S 110 ) generating control data configured to be used or usable in an agricultural equipment, wherein the control data is based on the highest ranked treatment schedule from the ranked list, or a treatment schedule selected by a user from the ranked list.

A method for generating a control file to operate a treatment device ( 10 ) on an agricultural area ( 11 ) to be treated, the method comprising the steps of: providing ( 40 ) treatment data signifying a type of treatment to be conducted by the treatment device and crop data relating to a crop present on the agricultural area ( 11 ) to be treated to a preparation system ( 13 ); determining ( 42 ), by the preparation system ( 13 ), from the treatment data and the crop data at least one operation parameter, wherein the determined operation parameter is related to a real-time and/or location-specific condition to be monitored during treatment; generating ( 44 ), by the preparation system ( 13 ), a control file comprising the at least one operation parameter, the generated control file usable to operate the treatment device ( 10 ) based on the real-time and/or location-specific condition to be monitored during treatment.

A computer-implemented method for determining at least one of the treatment parameters selected from the group consisting of: (a) at least one rate (seeding rate) for planting at least one crop in a field or a sub-field zone, (b) at least one depth (seeding depth) for planting at least one crop in a field or a sub-field zone, and (c) at least one time window (seeding time) for planting at least one crop in a field or a sub-field zone, comprising the following steps: (step 1) receiving by the computing unit—from a database and/or from user input and/or from real-time measurements—crop data relating to the at least one crop to be planted in the field or in the sub-field zone and static field data relating to the field or the sub-field zone, (step 2) receiving by the computing unit—from a database and/or from user input and/or from real-time measurements—at least one type of additional data selected from the group consisting of: (A) yield data relating to the field or the sub-field zone, (B) weather data relating to the field or the sub-field zone, and (C) soil data relating to the field or the sub-field zone, (step 3) at least based on the crop data, and the static field data, and the at least one type of additional data, initiating and/or performing data processing in at least one database and/or database system containing (i) data related to crop data and/or data related to static field data, (ii) data related to the at least one treatment parameter, and (iii) data related to at least one type of additional data selected from the group consisting of: yield data, weather data, and soil data, (step 4) outputting the at least one treatment parameter based on the result of the data processing.