ASSISTANCE SYSTEM FOR DETERMINING A RESOURCE FORECAST OF AN AGRICULTURAL FIELD

Description

[0001] The invention relates to an assistance system for determining a resource forecast of an agricultural field according to the preamble of claim 1 and a method for determining a resource forecast of an agricultural field according to the preamble of claim 12.

[0002] In modern agricultural studies, fields are now viewed as complex biochemical systems. Achieving optimal management requires precise measurements and forecasts of field condition data, such as plant nutrient uptake, and environmental data, such as weather. Similar to controlling a machine, a user, generally a farmer, has numerous ways to intervene in the system. These possibilities are subject to complex interactions that depend on historical data and influence future data. Simultaneously, these possibilities are also subject to technical, chemical, and economic constraints.

[0003] The known assistance system (US 9,058,633 A), from which the invention is based, provides field data from an agricultural field using satellite data. Based on a plant growth model, the use of agricultural resources such as pesticides is monitored depending on the field data.

[0004] However, it is not taken into account that, for example, there are various options for a specific pesticide, which differ in their effectiveness and cost-effectiveness. There is a general need for optimizing resource allocation, which should be adapted to the prevailing conditions. Furthermore, US 2012 / 101861 discloses a method for monitoring and predicting agricultural production.

[0005] The invention is based on the problem of designing and further developing the known assistance system in such a way that the provision of resources for carrying out agricultural actions is simplified.

[0006] The above problem is solved in an assistance system according to the preamble of claim 1 by the features of the characterizing part of claim 1.

[0007] The fundamental consideration is to provide resource data that specifies the availability of resource units. On the one hand, this ensures that the necessary resources can be provided, as only available resource units are used. On the other hand, it is essential that the resource data allows for an optimized selection of resource units, so that the selection is practical and based on the user's specific needs and the field's properties.

[0008] Therefore, an assistance system for determining a resource forecast of an agricultural field is proposed, wherein the assistance system comprises a database and a forecasting unit, wherein the database includes historical field data, current field data and expected field data of the agricultural field, and wherein the field data includes field condition data and field environment data.

[0009] Based on a planning specification and depending on the field data, the forecasting unit determines a resource forecast using a predefined forecasting model, which reflects the expected need for agricultural resources, at least auxiliary equipment and operating resources, to carry out agricultural activities in the field in accordance with the planning specification.

[0010] Specifically, it is proposed that the database includes resource data that specifies the availability of resource units for providing the resources, and that the forecasting unit assigns resource units to the determined resource forecast based on a predefined optimization criterion in a resource list, which are identified as available in the resource data.

[0011] The aim of the proposed teaching approach is therefore to provide the user with a resource list that contains a recommendation about the specific resource units, which further takes into account the respective needs of the user and the properties of the field.

[0012] The resource forecast is determined based on the planning specification, wherein, in the preferred embodiment according to claim 2, the planning specification is specified by the user. Likewise, at least part of the planning specification can be determined by the assistance system.

[0013] In the particularly preferred embodiment according to claim 3, the resource forecast comprises a yield forecast of a field's inhabitant based on the field data. The optimization criterion here preferably relates to the yield forecast. The allocation of resource units is thus expediently carried out based on the expected field yield.

[0014] According to the further, also preferred embodiment according to claim 4, the resource data further comprise resource impact data assigned to the respective resource units, thus expanding the possibilities for optimizing the selection of the resource units.

[0015] The forecasting unit preferably determines a profit forecast for the field based on the resource forecast (claim 5). In this way, in addition to the aforementioned yield, the economically relevant figure of profit can also be represented using the resource forecast, with the optimization criterion preferably relating to the profit forecast.

[0016] Another aspect that is important for many applications is the environmental compatibility of the resources, which is the subject of claim 6. The forecasting unit determines an environmental compatibility forecast for the field based on the resource forecast.

[0017] In the particularly advantageous embodiment according to claim 7, a reservation routine is provided to ensure the availability of the resource units.

[0018] In a further, particularly preferred embodiment according to claim 8, the forecasting unit generates a hit list with multiple resource lists based on the optimization criterion. The user is thus provided with various options for selecting the resource units, adapted to the respective requirements.

[0019] Claims 9 and 10 preferably relate to data included in the field data whose influence on resource forecasting is particularly relevant. Claim 11 relates to preferred embodiments of the resource units.

[0020] According to a further teaching as claimed in claim 12, which has independent significance, a method for determining a resource forecast for an agricultural field, in particular by means of a proposed assistance system, is claimed. Reference may be made to all details relating to the proposed assistance system.

[0021] The invention will now be explained in more detail with reference to a drawing that illustrates only one embodiment. The drawing shows Fig. 1 a schematic representation of a proposed assistance system.

[0022] The invention relates to an assistance system 1 for determining a resource forecast of an agricultural field F. The assistance system can, for example, be a computer-based system such as a server or a system consisting of several servers and / or clients.

[0023] Assistance system 1 includes a database 2, which is stored on non-volatile memory within assistance system 1. Assistance system 1 also includes a forecasting unit 3, which is configured to determine a resource forecast for agricultural field F, as further explained below. Here, and preferably, the forecasting unit 3 includes a memory containing program instructions and at least one processor for executing the program instructions, wherein the memory and the program instructions are configured, together with the processor, to determine the resource forecast.

[0024] Database 2 comprises historical field data 4, current field data 5, and expected field data 6 for agricultural field F. Historical field data 4 generally refers to field data recorded in past agricultural periods, primarily stored by assistance system 1. These past periods mainly relate to harvest periods but can also include other periods oriented towards agricultural cycles. Current field data 5 refers to field data from the current agricultural period. Expected field data 6 can be a forecast of the future development of field data 6, generated by assistance system 1 and / or provided by an external system 7.

[0025] Field data 4, 5, and 6 comprise field condition data, which are representative of the properties of field F, in particular the crops and / or the soil of field F. The field environment data are representative of the properties of the immediate and indirect surroundings of field F, which can influence agricultural aspects, in particular the crop composition, of field F.

[0026] Based on a planning specification 8 and depending on the field data 4, 5, 6, the forecasting unit determines a resource forecast using a predefined forecasting model 9. Planning specification 8 is a specification for at least one future agricultural aspect of field F, in particular regarding the variety of crops to be cultivated and / or regarding agricultural actions on the field, such as sowing, fertilization, the use of plant protection products, or harvesting. Planning specification 8 can indicate that one or more agricultural aspects are to be implemented in general, for example, that a specific crop is to be cultivated. Planning specification 8 can also contain time-related specifications for agricultural aspects, for example, that sowing and / or fertilization are to be carried out within specific time periods.Planning guideline 8 may also contain specifications regarding the agricultural resources used to carry out agricultural activities, for example, which seeds and / or active ingredients are used. Planning guideline 8 may also stipulate that certain resources are excluded from use, for example, to comply with legal requirements or the requirements of organic farming.

[0027] The resource forecast calculated reflects the anticipated need for agricultural resources, at least auxiliary equipment and operating resources, to carry out agricultural activities in the field in accordance with planning guideline 8. The resource forecast indicates, based on the field data, the expected use of resources to comply with planning guideline 8. The resource forecast preferably includes a list of the type and quantity of resources required, as well as the time period during which each resource is to be used in the respective agricultural activity.

[0028] The term "agricultural inputs" refers to resources that have a direct impact on field yield. These include seeds and active ingredients applied to the field, particularly to the crops and / or soil, such as fertilizers, growth regulators, harvest aids, fungicides, pesticides, herbicides, and the like. "Operating inputs" refers to resources that enable the yield-generating process. These include, among other things, the use of agricultural machinery and its components, tools, wear parts, and the like.

[0029] The resource forecast is determined using the predefined forecasting model 9. In a simple case, the forecasting model 9 can include an extrapolation based on predefined mathematical functions, which is performed in particular on the basis of the historical field data 4 and the current field data 5. In the forecasting model 9, for example, the expected resource requirements are further determined based on the expected field data. Preferably, the forecasting model 9 includes at least one plant growth model, which in particular takes into account the expected field environment data, and / or at least one mechanism of action model of the resources, in particular the auxiliary materials.

[0030] It is essential that the database 2 contains resource data which specifies the availability of resource units 10, 11 for providing the resources, and that the forecasting unit 3 assigns resource units 10, 11, which are specified as available in the resource data, to the determined resource forecast based on a predefined optimization criterion in a resource list 12.

[0031] Resource units 10 and 11 refer to the concrete units that can provide the resources, for example, a specific agricultural machine or a specific quantity of a particular product containing an active ingredient. Fig. 1 Resource units are represented here in the form of auxiliary equipment 10 and in the form of operating resources 11.

[0032] Resource data can be provided, for example, as a data pool, with various economic entities such as traders, lessors, and producers of resource units 10, 11 contributing the availability of their resource units 10, 11 to the data pool. The resource data specifies, among other things, the period during which the respective resource unit 10, 11 is available. For example, the resource unit 10, 11 could be a resource unit 10, 11 available for rent, such as agricultural machinery, in which case the resource data indicates the period during which the resource unit 10, 11 is already reserved or still available. Similarly, the resource unit 10, 11 could be a resource unit 10, 11 available for purchase, in which case the resource data specifies the quantity of the resource unit 10, 11 that can be delivered and within what timeframe.

[0033] The optimization criterion comprises several sub-criteria, which are optimized within the framework of the resource list 12 to be created. These sub-criteria can be weighted. Preferably, the optimization criterion, or at least one sub-criterion thereof, involves maximizing, minimizing, maintaining a minimum value, or maintaining a maximum value of a quantity, such as the field yield or a probability. The forecasting unit identifies those resource units 10, 11 that are designated as available in the resource data and that provide the resources for which there is a projected demand according to the resource forecast. The selection of the resource units 10, 11 is further carried out based on the optimization criterion.For example, the resource units 10, 11, which are designated as available, are first selected, and from these, those resource units are selected which meet the optimization criterion.

[0034] The forecasting unit generates a resource list 12, which contains an allocation of the resource units 10, 11 determined based on the optimization criterion to the resource forecast. The resource list 12 is thus, in effect, a compilation of specific resource units 10, 11, indicating the resources likely required to implement the planning specifications, while simultaneously taking the optimization criterion into account. The availability specified in the resource data ensures that the specific resource units 10, 11 are actually available during the respective period intended for resource use.

[0035] The planning specification is defined by the user according to a configuration. The user can enter the planning specification via a user interface (not shown), which may be part of or communicate with assistance system 1. Alternatively, in one configuration, the planning specification can be determined at least partially by the assistance system, for example, based on field data 4, 5, and 6.

[0036] According to a further embodiment, the resource forecast includes a yield forecast of a field's crop based on field data. The forecasting unit 3 generates the yield forecast using the forecasting model 9, preferably a plant growth model. Yield is understood to mean, in particular, the mass or volume of the crop input from field F, relative to the area or the entire field F. The yield forecast represents an expected yield, specifically linked to an uncertainty for the yield and / or a probability of this yield occurring. Furthermore, in this embodiment, the optimization criterion preferably relates to the yield forecast, wherein, for example, a sub-criterion of the optimization criterion is defined by maximizing the yield from the yield forecast or ensuring it meets a minimum value.The forecasting unit 3 then makes an allocation of the resource units 10, 11 designated as available, for example, such that from the resource units 10, 11 designated as available, those resource units 10, 11 are selected which, according to the yield forecast, will yield the maximum yield.

[0037] In addition to the previously described designation of the availability of resource units 10, 11, the resource data can further include resource impact data assigned to the respective resource units 10, 11. The resource impact data specifies characteristic parameters for the effect of the respective resource unit 10, 11. The resource impact data preferably includes resource cost data, which, for example, specifies the purchase price, delivery costs, and / or maintenance costs of the respective resource unit 10, 11. Preferably, the resource impact data also includes environmental impact data, for example, concerning the resulting pollution of the soil, groundwater, and / or air when using the resource unit 10, 11. The resource impact data can also address possible interactions between individual resource units 10, 11, such as incompatibility of active ingredients or the like.

[0038] According to another preferred embodiment, forecasting unit 3 uses the resource forecast to determine a profit forecast for the field based on resource cost data. Forecasting model 9 addresses technical, plant-biological, and monetary relationships that are responsible for generating profit. The profit forecast is preferably determined based on the yield forecast described above. In addition to yield, the profit forecast can, for example, also use environmental impact data to consider the sustainability of the resource units 10 and 11 to be used and their influence on the value of field F.

[0039] The optimization criterion preferably concerns the profit forecast, whereby, for example, a sub-criterion of the optimization criterion is defined by maximizing the profit from the profit forecast. Forecasting unit 3 then allocates the resource units 10, 11 designated as available, preferably such that those resource units 10, 11 are selected from the available resource units 10, 11 which, according to the profit forecast, will generate the maximum profit.

[0040] According to a further preferred embodiment, the forecasting unit 3 uses the resource forecast to determine an environmental impact assessment of the field based on the environmental impact data, and the optimization criterion preferably relates to the environmental impact assessment. The environmental impact assessment determines, for example, the expected magnitude of the impact on the soil, groundwater, and / or air when using the resource units 10, 11, such as the amount of residues, emissions, energy required, or the like. The forecasting unit 3 then allocates the resource units 10, 11, for example, by selecting from the available resource units 10, 11 those that minimize the impact or keep the impact below a limit value.

[0041] As already mentioned, the optimization criterion can have several sub-criteria, each of which is optimized within the framework of the resource list 12 to be created. Preferably, the sub-criteria relating to yield forecasting, profit forecasting, and / or environmental impact forecasting can be combined. For example, the optimization criterion can aim to maximize yield while simultaneously ensuring that environmental impact meets certain requirements. Similarly, sub-criteria can be weighted, thereby achieving a compromise between yield and environmental impact with a predetermined weighting of these sub-criteria. Furthermore, the optimization criterion can relate to the probability of a specific yield and / or profit occurring, and, for example, those resource units 10, 11 can be selected that are most likely to lead to the specific yield and / or profit.

[0042] According to another preferred embodiment, the assistance system uses a reservation routine to mark the resource units assigned in resource list 12 as unavailable in the resource data. This reservation routine can be triggered automatically when resource list 12 is generated, ensuring that resource units 10 and 11 from resource list 12 remain available to the user. Alternatively, the reservation routine can be triggered manually by the user. For example, the assistance system 1 sends resource list 12 to the user. The user may be prompted to confirm resource list 12 and preferably also given the opportunity to make manual changes to it. After confirmation, resource units 10 and 11 are reserved by marking them as unavailable in the resource data.

[0043] According to a further preferred embodiment, the forecasting unit 3 generates a hit list with several resource lists 12 of resource units 10, 11 designated as available, based on the optimization criterion. For example, there may be several possible assignments of resource units 10, 11 that satisfy the optimization criterion, such as when the optimization criterion concerns compliance with a minimum value. The assistance system 1 outputs the hit list to the user or initiates its output, so that the user can select one of the resource lists 12. Preferably, the hit list is ordered according to a probability of fulfilling the optimization criterion, as represented in the resource forecast.

[0044] According to a further preferred embodiment, the field condition data include active ingredient data, crop data and / or soil data, in particular nutrient data, and / or pest data. Historical active ingredient data includes, for example, which active ingredients have already been used in connection with field F. The field condition data are preferably acquired by at least one sensor 13. Fig. 1 A plurality of sensors 13 are shown, each assigned to a section of field F. The sensors 13 can be static sensors. Preferably, however, at least one sensor 13 of an agricultural machine is provided, which determines field condition data.

[0045] Preferably, the field environment data includes weather data and / or pest data. The weather data relates, for example, to temperature, air pressure, precipitation, solar radiation, wind, or the like at one or more locations within field F and / or in the vicinity of field F. The weather data, in particular expected weather data, can be provided, for example, by an external system 7 such as a weather service provider.

[0046] In another preferred embodiment, the resource units comprise agricultural machinery, tools, seeds, pesticides and / or fertilizers.

[0047] According to another doctrine, which has independent significance, a method for determining a resource forecast of an agricultural field F is claimed as such.The method is carried out using an agricultural assistance system 1, preferably using a proposed assistance system 1, wherein the assistance system 1 comprises a database 2 and a forecasting unit 3, wherein the database 2 includes historical field data 4, current field data 5 and expected field data 6 of the agricultural field F, wherein the field data 4, 5, 6 include field condition data and field environment data, wherein, using the forecasting unit 3, a resource forecast is determined based on a planning specification 8 and depending on the field data 4, 5, 6 using a predefined forecasting model 9, which depicts the expected need for agricultural resources, at least auxiliary equipment and operating resources, for carrying out agricultural activities on the field F in accordance with the planning specification 8.Essentially, database 2 contains resource data specifying the availability of resource units 10 and 11 for providing the resources. Using forecasting unit 3, resource units 10 and 11, identified as available in the resource data, are assigned to the determined resource forecast in a resource list 12 based on a predefined optimization criterion. Reference is made to the above explanations regarding the proposed assistance system 1.

[0048] According to one implementation of the proposed procedure, the agricultural actions on field F are carried out using resource units 10 and 11 from the resource forecast, as specified in resource list 12. The resource list 12 generated by assistance system 1 is thus ultimately implemented. Reference symbol list

[0049] 1 Assistance system 2 Database 3 Forecasting unit 4 Historical field data 5 Current field data 6 Expected field data 7 External system 8 Planning specification 9 Forecasting model 10 Auxiliary equipment 11 Operating resources 12 Resource list 13 Sensor Field

Claims

1. Assistance system for determining a resource forecast for an agricultural field (F), wherein the assistance system (1) has a database (2) and a forecast unit (3), wherein the database (2) comprises historical field data (4), current field data (5) and expected field data (6) relating to the agricultural field (F), wherein the field data (4, 5, 6) comprise field condition data and field environment data, wherein the forecast unit (3), on the basis of a planning specification (8) and depending on the field data (4, 5, 6), uses a predefined forecast model (9) to determine a resource forecast representing the foreseeable need for agricultural resources, at least aids and equipment, for carrying out agricultural actions on the field (F) according to the planning specification (8), characterized in that the database (2) comprises resource data that specify the availability of resource units (10, 11) for providing the resources, and in that the forecast unit (3) uses a predefined optimization criterion to assign resource units (10, 11) that are specified as available in the resource data to the determined resource forecast in a resource list (12), wherein the agricultural actions on the field (F) are carried out from the resource forecast using the resource units (10, 11) according to the resource list (12).

2. Assistance system according to Claim 1, characterized in that the planning specification (8) is specified by the user and / or determined by the assistance system.

3. Assistance system according to Claim 1 or 2, characterized in that the resource forecast comprises a yield forecast of a field-grown crop of the field (F) based on the field data and the optimization criterion relates to the yield forecast.

4. Assistance system according to one of the preceding claims, characterized in that the resource data further comprise resource effect data, preferably resource cost data and / or environmental compatibility data, assigned to the respective resource units (10, 11).

5. Assistance system according to one of the preceding claims, characterized in that the forecast unit (3) uses the resource forecast to determine a profit forecast of the field (F) based on the resource cost data, and in that the optimization criterion relates to the profit forecast.

6. Assistance system according to one of the preceding claims, characterized in that the forecast unit (3) uses the resource forecast to determine an environmental compatibility forecast for the field (F) based on the environmental compatibility data and the optimization criterion relates to the environmental compatibility forecast.

7. Assistance system according to one of the preceding claims, characterized in that the assistance system (1) in a reservation routine specifies the resource units (10, 11) assigned in the resource list (12) as unavailable in the resource data.

8. Assistance system according to one of the preceding claims, characterized in that the forecast unit (3) uses the optimization criterion to generate a hit list with a plurality of resource lists (12) of resource units (10, 11) specified as available, preferably in that the hit list is ordered based on a probability represented in the resource forecast for satisfying the optimization criterion.

9. Assistance system according to one of the preceding claims, characterized in that the field condition data comprise active ingredient data, fruit data and / or soil data, in particular nutrient data, and / or pest data, and / or in that the field condition data are determined by at least one sensor (13), in particular a sensor (13) of an agricultural work machine.

10. Assistance system according to one of the preceding claims, characterized in that the field environment data comprise weather data and / or pest data.

11. Assistance system according to one of the preceding claims, characterized in that the resource units (10, 11) comprise agricultural work machines, tools, seeds, crop protection agents and / or fertilizers.

12. Method for determining a resource forecast for an agricultural field (F) by means of an agricultural assistance system (1), wherein the assistance system (1) has a database (2) and a forecast unit (3), wherein the database (2) comprises historical field data (4), current field data (5) and expected field data (6) relating to the agricultural field (F), wherein the field data (4, 5, 6) comprise field condition data and field environment data, wherein a resource forecast representing the foreseeable need for agricultural resources, at least aids and equipment, for carrying out agricultural actions on the field (F) according to a planning specification (8) is determined by means of the forecast unit (3) on the basis of the planning specification (8) and depending on the field data (4, 5, 6) using a predefined forecast model (9), characterized in that the database (2) comprises resource data that specify the availability of resource units (10, 11) for providing the resources, and in that resource units (10, 11) that are specified as available in the resource data are assigned to the determined resource forecast in a resource list (12) by means of the forecast unit (3) using a predefined optimization criterion, wherein the agricultural actions on the field (F) are carried out from the resource forecast using the resource units (10, 11) according to the resource list (12).