BASF Agro Trademarks GmbH

A method for plantation treatment of a plantation field, the method comprising: determining (S 10 ) an application rate decision logic ( 10 ) based on offline field data (Doff) relating to expected conditions on the plantation field ( 300 ); taking (S 20 ) an image ( 20 ) of a plantation of a plantation field ( 300 ); recognizing (S 30 ) objects ( 30 ) on the taken image ( 20 ); determining (S 40 ) an application rate based on the determined application rate decision logic ( 10 ) and the recognized objects ( 30 ); and determining (S 50 ) a control signal (S) for controlling a treatment arrangement ( 50 ) of a treatment device ( 200 ) based on the determined application rate.

A computer-implemented method for operating an agricultural device, the method comprising the steps of: providing agricultural operation data for an agricultural device, wherein the agricultural operation data comprise treatment operation data and/or monitoring operation data; wherein if the agricultural operation data comprise treatment operation data and monitoring operation data, control data for controlling a treatment unit of the agricultural device are provided based on monitoring data provided by a monitoring unit of the agricultural device; if the agricultural operation data comprise treatment operation data and no monitoring operation data, control data for controlling the treatment unit of the agricultural device are provided and monitoring data provided by the monitoring unit of the agricultural device are provided; if the agricultural operation data comprise monitoring operation data and no treatment operation data, monitoring data provided by the monitoring unit of the agricultural device are provided.

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 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.

The present invention relates to a system and a method for operating a treatment device ( 120 ) applying a treatment product to an agricultural area, the method comprising: obtaining (S 210 ) at least one dataset of an area of interest within the agricultural area ( 110 ) to a control system ( 12.10 ); determining (S 220 ), by the control system ( 12.10 ), from the at least one dataset a plant indicator, wherein a basic threshold for triggering application of the treatment product is dynamically adjustable in relation to the plant indicator; and providing a control signal (S 230 ), by the control system, to control the treatment device ( 120 ) based on the determined plant indicator and the threshold for triggering application of the treatment product.

Method for generating a control signal for a smart spraying device with at least one individual spray nozzle, and a method for controlling a smart spraying device, using PWM and field data relating to a vegetative indicator for providing an improved adaptive application of products onto an area to be treated.

A computer-implemented method for determining location-specific seeding rate and/or seeding depth for planting seeds in an agricultural field by means of an agricultural equipment, comprising the steps. (step 1) identifying and/or indicating a parameter set comprising at least two seeding parameters, wherein the seeding parameter is a parameter having an impact and/or a potential impact to the seeding rate and/or seeding depth, (step 2) receiving by the computing unit-from a database and/or from user input-field data, data indicative of the characteristics of the at least two seeding parameters of the parameter set, and data indicative of the agricultural equipment setup (step 3) based on the field data, determining at least one level 1 zone, at least two level 2 zones and at least four level 3 zones, (step 4) based on the data indicative of the characteristics of the at least two seeding parameters of the parameter set, and/or based on the data indicative of the equipment setup, determining for each seeding parameter whether it is a level 1 parameter, level 2 parameter, or level 3 parameter, (step 5) if at least one level 1 parameter is present, generating a seeding logic for level 1 parameter(s), (step 6) if at least one level 2 parameter is present, generating a seeding logic for level 2 parameter(s), based on the seeding logic for level 1 parameter(s), (step 7) if at least one level 3 parameter is present, generating a seeding logic for level 3 parameter(s), based on the seeding logic for level 1 parameter(s) and on the seeding logic for level 2 parameter(s), (step 8) outputting the seeding rate and/or the seeding depth based on the determined seeding logics.

A computer-implemented method for cross-account model deployment is provided. According to the method, at least one artifact identifier of at least one to-be-deployed model artifact is provided. Further, at least one account tuple, the account tuple comprising a source account identifier identifying a source account and a target account identifier identifying a target account, is provided. Finally, the at least one to-be-deployed model artifact is moved from the source account to the target account. Further, a computer program element and a computer readable medium are provided.

The present invention relates to fungal disease management. In order to improve fungal disease management, a computer-implemented method is provided for determining a disease progression usable for fungicide spray schedule on an agricultural field. The method comprising the step of receiving data including crop variety data, environmental data, crop management data, and location data of the agricultural field. The crop variety data relates to a crop grown or to be grown on an agricultural field. The environmental data is indicative of an environmental condition for the agricultural field. The crop management data is indicative of fungicide spray history for the agricultural field. The method further comprises the step of applying a machine-learning model to the received data to determine disease progression time-series data of a fungal disease, wherein the machine-learning model has been trained to learn the disease progression under a condition defined by crop variety data, environmental data, crop management data, and location data based on historic data collected from one or more agricultural fields. The method further comprises the step of determining, based on the determined disease progression time-series data, a disease onset date of the fungal disease.

A computer-implemented method for providing variable application rate data for at least two application means of an application system of an application device for applying a treatment product onto an agricultural field, comprising: providing application configuration parameter data for the at least two application means of the application system of the application device; providing position and/or movement data of the application device; providing product application data comprising spatial information about the target application amount for the treatment product to be applied onto the agricultural field; generating a grid of application polygons; and determining variable application rate data for each of the at least two application means of the application system of the application device based on the application configuration parameter data, the position and/or movement data, and the product application data, wherein the variable application rate data is determined for each application polygon in the generated grid.