A method for determining field- or area-specific sowing rates, depths, and timings for planting crops in agricultural fields, based on multiple data inputs such as crop, field, yield, weather, and / or soil data.

Abstract

1. A computer-implemented method for determining at least one process parameter selected from the group consisting of: (a) at least one rate (seeding rate) for planting at least one crop in a field or sub-field area, (b) at least one depth (seeding depth) for planting at least one crop in the field or sub-field area, and (c) at least one time window (seeding time) for planting at least one crop in the field or sub-field area, the method comprising: (step 1) receiving, by a computing unit, crop data for at least one crop to be planted in the field or sub-field area and static field data for the field or sub-field area from a database and / or from user input and / or from real-time measurements; (step 2) receiving, by the computing unit, from the database and / or from user input and / or from real-time measurements, (A) a rate for planting at least one crop in the field or sub-field area, (B) a seeding rate for planting at least one crop in the field or sub-field area, and (C) a seeding time window for planting at least one crop in the field or sub-field area. 1. A computer-implemented method comprising: (A) receiving at least one type of additional data selected from the group consisting of: (B) yield data for the sub-field area, (C) weather data for the field or the sub-field area, and (D) soil data for the field or the sub-field area; (B) receiving at least one type of additional data selected from the group consisting of: (A) yield data for the sub-field area, (B) weather data for the field or the sub-field area, and (C) soil data for the field or the sub-field area; (C) receiving at least one type of additional data selected from the group consisting of: (C) yield data for the sub-field area, (C) weather data for the field or the sub-field area, and (D) soil data for the field or the sub-field area; (C) receiving at least one type of additional data selected from the group consisting of: (D) yield data for the sub-field area, (D) weather data for the field or the sub-field area, and (C) soil data for the field or the sub-field area; (D) receiving at least one type of additional data selected from the group consisting of: (A) yield data for the sub-field area, (B) weather data for the field or the sub-field area, and (C) soil data for the field or the sub-field area; (C) receiving at least one type of additional data selected from the group consisting of: (C) yield data for the sub-field area,

Description

Technical Field

[0001] The present invention relates to a computer-implemented method for determining treatment parameters including seeding rate, seeding depth, and seeding time specific to a field or area for planting crops in an agricultural field, based on a plurality of data inputs such as crop, field, yield, weather, and / or soil data, a data processing system including means for executing such a computer-implemented method, the use of such treatment parameters for controlling agricultural equipment, and the use of such treatment parameters for treating an agricultural field.

Background Art

[0002] In practice, farmers or users often face the problem that they cannot systematically determine the optimal seeding rate, seeding depth, and seeding time specific to a field or area, even though all data or information, including elevation, altitude, historical yield potential, soil characteristics, soil moisture, and regulatory data, regarding different characteristics of the field or sub-field area is in principle available or can be made available. As a result, there arises the problem that the seeding rate, seeding depth, and seeding time selected by the farmer or user are inappropriate or inefficient for achieving any of the optimal yield, optimal crop value in terms of oil, protein, or nutrient content, or optimal sustainability effect in terms of minimizing the use of crop protection agents.

[0003] In the prior art, International Publication No. 2013 / 169349A1 pamphlet discloses a method for predicting the appropriate planting time based on weather data and soil temperature. International Publication No. 2013 / 169349A1 pamphlet does not disclose a systematic approach for determining the seeding rate, seeding depth, and seeding time specific to an area.

[0004] In view of the above problems and issues, it has been found that there is a need to improve and simplify the decision-making process of farmers or users in this regard.

Summary of the Invention

Problems to be Solved by the Invention

[0005] Taking the above into consideration, an object of the present invention is to provide a computer-aided method for determining processing parameters, including field or area-specific sowing rate, sowing depth, and sowing time, for planting crops in agricultural fields, based on multiple data inputs such as crop, field, yield, weather, and / or soil data. Another object of the present invention is to provide a computer-aided method for determining the above-mentioned processing parameters that helps agricultural workers or users make quick, real-time decisions regarding the treatment of agricultural fields. Another object of the present invention is to provide a computer-aided method for determining the above-mentioned processing parameters that enables the output of application maps that can be used to control agricultural equipment. Another object of the present invention is to provide a computer-aided method for improving the yield of crops planted in agricultural fields. Another object of the present invention is to provide a computer-aided method for improving the value of crops planted in agricultural fields, including oil content, protein content, or nutrient content. Another object of the present invention is to provide a computer-aided method for minimizing the use of crop protection agents, such as herbicides, fungicides, or insecticides, for growing crops in agricultural fields. [Means for solving the problem]

[0006] The object of the present invention is solved by the subject matter of the independent claims, and further embodiments are incorporated into the dependent claims. It should be noted that the aspects and embodiments of the present invention described below apply to methods, data processing systems, computer program products, and computer-readable storage media.

[0007] According to a first aspect of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining at least one processing parameter selected from the group consisting of, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for a field or sub-field area, (B) Meteorological data relating to the field or sub-field area, and (C) Soil data relating to the field or sub-field area A step of receiving additional data of at least one type selected from a group consisting of, (Step 3) Based on crop data and static field data and at least one type of additional data, (i) Data relating to crop data and / or static field data, (ii) data relating to at least one processing parameter, and (iii) Data relating to at least one type of additional data selected from the group consisting of yield data, meteorological data, and soil data. A step of starting and / or performing data processing in at least one database and / or database system, including (Step 4) A step to output at least one processing parameter based on the results of data processing. This relates to computer implementation methods, including those mentioned above.

[0008] According to a further embodiment of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, and (b) At least one depth (sowing depth) for planting at least one crop in a field or subfield area. A computer implementation method for determining both of the processing parameters selected from the group consisting of, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for a field or sub-field area, (B) Meteorological data relating to the field or sub-field area, and (C) Soil data relating to the field or sub-field area A step of receiving additional data of at least one type selected from a group consisting of, (Step 3) Based on crop data and static field data and at least one type of additional data, (i) Data relating to crop data and / or static field data, (ii) data relating to at least one processing parameter, and (iii) Data relating to at least one type of additional data selected from the group consisting of yield data, meteorological data, and soil data. A step of starting and / or performing data processing in at least one database and / or database system, including (Step 4) A step to output two processing parameters based on the results of data processing. This relates to computer implementation methods, including those mentioned above.

[0009] According to a further embodiment of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining at least two processing parameters selected from a group consisting of, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for a field or sub-field area, (B) Meteorological data relating to the field or sub-field area, and (C) Soil data relating to the field or sub-field area A step of receiving additional data of at least one type selected from a group consisting of, (Step 3) Based on crop data and static field data and at least one type of additional data, (i) Data relating to crop data and / or static field data, (ii) data relating to at least one processing parameter, and (iii) Data relating to at least one type of additional data selected from the group consisting of yield data, meteorological data, and soil data. A step of starting and / or performing data processing in at least one database and / or database system, including (Step 4) A step to output at least two processing parameters based on the results of data processing. This relates to computer implementation methods, including those mentioned above.

[0010] According to a further aspect of the present invention, the present invention (a) at least one rate (seeding rate) for planting at least one crop in a field or sub-field area, (b) at least one depth (seeding depth) for planting at least one crop in a field or sub-field area, and (c) at least one time window (seeding time) for planting at least one crop in a field or sub-field area A computer-implemented method for determining all three processing parameters selected from the group consisting of: (Step 1) A step of receiving crop data regarding at least one crop to be planted in a field or sub-field area and static field data regarding the field or sub-field area from a database, and / or from user input, and / or from real-time measurements by a computing unit, (Step 2) By a computing unit, from a database, and / or from user input, and / or from real-time measurements, (A) yield data regarding the field or sub-field area, (B) weather data regarding the field or sub-field area, and (C) soil data regarding the field or sub-field area Receiving at least one type of additional data selected from the group consisting of: (Step 3) Based at least on the crop data, the static field data, and the at least one type of additional data, (i) data regarding the crop data and / or data regarding the static field data, (ii) data regarding at least one processing parameter, and (iii) data regarding at least one type of additional data selected from the group consisting of yield data, weather data, and soil data In at least one database and / or database system including: starting and / or performing data processing, (Step 4) Outputting all three processing parameters based on the result of the data processing This relates to computer implementation methods, including those mentioned above.

[0011] According to a further embodiment of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining all three processing parameters selected from a group consisting of the following, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for a field or sub-field area, (C) Soil data relating to the field or sub-field area Steps to receive, (Step 3) Based at least on crop data, static field data, yield data, and soil data, (i) Data relating to crop data and / or static field data, (ii) data relating to at least one processing parameter, and (iii) Data relating to yield and data relating to soil In at least one database and / or database system, the steps include: starting and / or executing data processing; (Step 4) A step to output all three processing parameters based on the results of the data processing. Includes, - Crop data: Crop data includes species or diversity data for at least one crop planted in a field or sub-field area, and regulatory data relating to at least one crop planted in a field or sub-field area. - Soil data includes soil characteristics and soil moisture of the soil in the field or sub-field area. - Yield data is, a) Historical yield potential of a field or subfield area, preferably determined based on remotely sensed green foliage area or biomass data of the field or subfield area, and / or b) Historical yield of a field or sub-field area, determined based on the amount previously harvested from that field or sub-field area. Includes.

[0012] According to a further embodiment of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining at least one processing parameter selected from the group consisting of, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for the field or sub-field area, and (B) Meteorological data relating to the field or sub-field area Steps to receive, (Step 3) Based at least on crop data, static field data, yield data, and weather data, (i) Data relating to crop data and / or static field data, (ii) Data relating to at least one processing parameter, (iii) Data relating to yield data, and (iv) Data related to meteorological data In at least one database and / or database system, the steps include: starting and / or executing data processing; (Step 4) A step to output at least one processing parameter based on the results of data processing. This relates to computer implementation methods, including those mentioned above.

[0013] According to a further embodiment of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining at least one processing parameter selected from the group consisting of, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for the field or sub-field area, and (C) Soil data relating to the field or sub-field area Steps to receive, (Step 3) Based at least on crop data, static field data, yield data, and soil data, (i) Data relating to crop data and / or static field data, (ii) Data relating to at least one processing parameter, (iii) Data relating to yield data, and (iv) Data related to soil data In at least one database and / or database system, the steps include: starting and / or executing data processing; (Step 4) A step to output at least one processing parameter based on the results of data processing. This relates to computer implementation methods, including those mentioned above.

[0014] According to a further embodiment of the present invention, the present invention is (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining at least one processing parameter selected from the group consisting of, (Step 1) The computing unit receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area from a database and / or from user input and / or from real-time measurements. (Step 2) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (B) Meteorological data relating to the field or sub-field area, and (C) Soil data relating to the field or sub-field area Steps to receive, (Step 3) Based at least on crop data, static field data, meteorological data, and soil data, (i) Data relating to crop data and / or static field data, (ii) Data relating to at least one processing parameter, (iii) Data relating to meteorological data, and (iv) Data related to soil data In at least one database and / or database system, the steps include: starting and / or executing data processing; (Step 4) A step to output at least one processing parameter based on the results of data processing. This relates to computer implementation methods, including those mentioned above.

[0015] According to a further aspect of the present invention, crop data relating to at least one crop planted in a field or sub-field area is: a) Species or diversity data of at least one crop to be planted in the field or subfield area, and / or b) Crop value data for at least one crop planted in a field or sub-field area. c) Agricultural data relating to at least one crop planted in the field or sub-field area, and / or d) Regulatory data relating to at least one crop planted in a field or sub-field area Includes.

[0016] According to a further aspect of the present invention, crop data relating to at least one crop planted in a field or sub-field area is: a) Seed or diversity data of at least one crop to be planted in a field or sub-field area, preferably including genetic information of the crop, and / or b) Crop value data for at least one crop planted in a field or sub-field area, preferably including the oil content, protein content and / or nutrient content of the crop. c) Agricultural data relating to at least one crop planted in a field or sub-field area, preferably including the number of days to sexual maturity, vigor, and occurrence rate of the crop, and / or d) Regulatory data relating to at least one crop planted in a field or subfield area, preferably including local information relating to legal frameworks and / or regulations relating to treatment parameters, local information relating to product management measures, and information relating to biodiversity regulations or biodiversity measures. Includes.

[0017] According to a further aspect of the present invention, the crop data relating to at least one crop planted in a field or sub-field area is preferably species or diversity data of at least one crop planted in a field or sub-field area, and preferably includes species or diversity data that includes genetic information of the crop.

[0018] According to a further aspect of the present invention, the crop data relating to at least one crop planted in a field or sub-field area is preferably crop value data of at least one crop planted in a field or sub-field area, preferably including crop value data including the oil content, protein content and / or nutrient content of such crop.

[0019] According to a further aspect of the present invention, the crop data relating to at least one crop planted in a field or sub-field area is preferably agricultural data of at least one crop planted in a field or sub-field area, and preferably includes agricultural data such as the number of days to sexual maturity, virility, and occurrence rate of the crop.

[0020] According to a further aspect of the present invention, crop data relating to at least one crop planted in a field or sub-field area preferably includes regulatory data relating to at least one crop planted in a field or sub-field area, which preferably includes regionally specific information relating to legal frameworks and / or regulations relating to processing parameters, regionally specific information relating to product management measures, and information relating to biodiversity regulations or biodiversity measures.

[0021] In a preferred embodiment of the present invention, crop data relating to at least one crop planted in a field or sub-field area is received by a computing unit from a database. In a preferred embodiment of the present invention, crop data relating to at least one crop planted in a field or sub-field area is received by a computing unit from user input.

[0022] According to a further aspect of the present invention, static field data relating to a field or sub-field area is: a) GPS (Global Positioning System), geographic location data and / or geographic positioning data relating to the field or sub-field area, and / or b) Elevation, height, slope and / or relief data relating to the field or subfield area, and / or c) Data on the past application of crop protection products or crop nutrition products in the field or sub-field area, data on preseason soil treatment in the field or sub-field area, and / or data on the type of cultivation in the field or sub-field area. Includes.

[0023] According to a further aspect of the present invention, static field data relating to a field or sub-field area preferably includes GPS (Global Positioning System), geographic location data and / or geographic positioning data relating to the field or sub-field area.

[0024] According to a further aspect of the present invention, static field data relating to a field or sub-field area preferably includes elevation, altitude, slope and / or relief data relating to the field or sub-field area.

[0025] According to a further aspect of the present invention, static field data relating to a field or sub-field area preferably includes data relating to past application of crop protection products or crop nutrition products in the field or sub-field area, data relating to pre-season soil treatment in the field or sub-field area, and / or data relating to the type of cultivation in the field or sub-field area.

[0026] In a preferred embodiment of the present invention, static field data relating to a field or sub-field area is received from a database by a computing unit. In a preferred embodiment of the present invention, static field data relating to a field or sub-field area is received from user input by a computing unit.

[0027] According to a further aspect of the present invention, yield data relating to a field or sub-field area is: a) Historical yield potential of a field or subfield area, preferably determined based on remotely sensed green foliage area or biomass data of the field or subfield area, and / or b) Historical yield of a field or subfield area, determined based on the amount harvested in the past, and / or c) Predictable yield potential for a field or sub-field area Includes.

[0028] According to a further aspect of the present invention, the yield data relating to a field or sub-field area relates to the crop planted in the field or sub-field area. For example, if the crop planted in the field or sub-field area is soybeans, only the yield data relating to soybeans will be received by the computing unit, and yield data relating to, for example, corn or wheat will be excluded.

[0029] According to a further aspect of the present invention, yield data relating to a field or sub-field area is: a) Historical yield potential of a field or subfield area with respect to crops planted in the corresponding field or subfield area, preferably determined based on remotely sensed green foliage area or biomass data of the field or subfield area, and / or b) Historical actual yield of the field or subfield area with respect to the crop planted in the corresponding field or subfield area, determined based on the amount harvested in the past, and / or c) Predictable yield potential of the field or sub-field area with respect to the crops planted in the corresponding field or sub-field area. Includes.

[0030] According to a further aspect of the present invention, yield data relating to a field or subfield area includes chronological yield potential of the field or subfield area, preferably determined based on remotely sensed foliage area or biomass data of the field or subfield area. The chronological yield potential can preferably be represented by a chronological yield potential map showing the chronological yield potential of different subfield areas (e.g., “power area maps”). The chronological yield potential can preferably be determined based on remotely sensed foliage area or biomass data of the corresponding field or subfield area for the past two years or more, more preferably for the past four years or more, most preferably for the past six years or more, particularly for the past eight years or more, and particularly preferably for the past ten years or more. In this regard, the term “remotely sensed” preferably means remotely sensed by satellites, airplanes, unmanned aerial vehicles, drones, optical sensors or LiDAR sensors.

[0031] According to a further aspect of the present invention, yield data relating to a field or sub-field area includes historical actual yield of the field or sub-field area, which is determined based on the amount previously harvested from the field or sub-field area. Historical actual yield may be determined based on the amount harvested from the field or sub-field area in the past two years or more, more preferably four years or more, most preferably six years or more, particularly eight years or more, and particularly preferably ten years or more.

[0032] According to a further aspect of the present invention, yield data relating to a field or subfield area includes the predicted yield potential of the field or subfield area, the predicted yield potential is preferably estimated based on historical yield potential and / or historical actual yield, optionally based on weather forecasts (e.g., weather forecasts over the entire crop season using a specific weather model), or the predicted yield potential is estimated based on a yield prediction model, i.e., a prediction model for yield data.

[0033] According to a further aspect of the present invention, yield data relating to a field or sub-field area is: a) Historical yield potential of a field or sub-field area, preferably determined based on remotely sensed green foliage area or biomass data of the field or sub-field area, and b) Historical yield of a field or sub-field area, determined based on the amount previously harvested from that field or sub-field area. The historical yield potential ("HYP") and historical actual yield data ("HAY") are preferably combined with a predefined or user-defined weighting coefficient. In another preferred embodiment, the historical yield potential and historical actual yield data may be combined with a weight of X% for HYP and a weight of Y% for HAY. For example, the final yield data for a field or subfield area is calculated by the formula X% × HYP + Y% × HAY, where X% is preferably in the range of 10% to 90%, more preferably in the range of 20% to 80%, and most preferably in the range of 30% to 70%, and Y% is preferably in the range of 10% to 90%, more preferably in the range of 20% to 80%, and most preferably in the range of 30% to 70%, and X% and Y% together equal 100%. In a preferred embodiment, historical yield potential ("HYP") and historical actual yield ("HAY") data may be combined with a 50% weighting for each. For example, final yield data for a field or sub-field area is calculated using the formula 50% × HYP + 50% × HAY. In another preferred embodiment, historical yield potential and historical actual yield data may be combined with a 30% weighting for HYP and a 70% weighting for HAY. For example, final yield data for a field or sub-field area is calculated using the formula 30% × HYP + 70% × HAY. In yet another preferred embodiment, historical yield potential and historical actual yield data may be combined with a 70% weighting for HYP and a 30% weighting for HAY. For example, final yield data for a field or sub-field area is calculated using the formula 70% × HYP + 30% × HAY.

[0034] According to a further aspect of the present invention, when three input data, for example yield data, static field data, and soil data, are used, these data can be combined with predefined or user-defined weighting coefficients, particularly in the data processing step, where the weighting coefficients are A% weighting for the first input data (referred to as "AIP") (e.g., yield data) and B% weighting for the second input data (referred to as "BIP") (e.g., static field data such as elevation or gradient). The system has a percentage weighting and a C% weighting for a third input data (referred to as "CIP") (for example, soil data such as soil properties, soil organic matter, or soil moisture), where the final combination of the three input data is calculated by the formula A% × AIP + B% × BIP + C% × CIP, where A%, B%, and C% are preferably in the range of 10% to 90%, more preferably in the range of 20% to 80%, and most preferably in the range of 30% to 70%, and A%, B%, and C% sum to 100%. According to a further aspect of the present invention, when four input data, for example yield data, static field data, and soil data 1 and soil data 2, are used, these data can be combined, particularly in the data processing step, with predefined or user-defined weighting coefficients, where the weighting coefficients are: A% weighting for the first input data (referred to as "AIP") (e.g., yield data), B% weighting for the second input data (referred to as "BIP") (e.g., static field data such as elevation or gradient), and a third input data (referred to as "CIP"). The system has a C% weighting for soil data 1 (e.g., soil characteristics, soil organic matter, or soil moisture) and a D% weighting for a fourth input data (referred to as "DIP") (e.g., soil data 2, such as soil moisture), where the final combination of the four input data is calculated by the formula A%×AIP+B%×BIP+C%×CIP+D%DIP, where A%, B%, C%, and D% are preferably in the range of 10% to 90%, more preferably in the range of 20% to 80%, and most preferably in the range of 30% to 70%, and A%, B%, C%, and D% sum to 100%.

[0035] In a preferred embodiment of the present invention, yield data relating to a field or sub-field area is received from a database by a computing unit. In a preferred embodiment of the present invention, yield data relating to a field or sub-field area is received from user input by a computing unit.

[0036] In a further embodiment of the present invention, meteorological data relating to a field or sub-field area includes temperature, air temperature, soil temperature, soil surface temperature, canopy temperature, humidity, air humidity, relative humidity, precipitation, moisture, wind conditions, wind speed, and / or sunshine level data relating to the field or sub-field area. In a preferred embodiment, the meteorological data includes forecast meteorological data for the following days and weeks. In a preferred embodiment, the meteorological data includes meteorological data for at least four days, more preferably at least one week, most preferably at least two weeks, and particularly preferably at least three weeks, prior to planting the crops to be planted in the field or sub-field area. In a preferred embodiment, the meteorological data includes meteorological data for at least four days, more preferably at least one week, most preferably at least two weeks, and particularly preferably at least three weeks, following the planting of the crops to be planted in the field or sub-field area. In a preferred embodiment, the meteorological data includes meteorological data for several days or weeks prior to planting the crops to be planted in the field or sub-field area, and forecast meteorological data for several days or weeks after planting the crops to be planted in the field or sub-field area. In a preferred embodiment, the meteorological data includes temperature, relative humidity, and / or precipitation.

[0037] In a preferred embodiment of the present invention, meteorological data relating to a field or sub-field area is preferably received by a computing unit from real-time measurements using remote or nearby weather sensors.

[0038] In a preferred embodiment of the present invention, meteorological data relating to a field or sub-field area is generated by a predictive model for meteorological data.

[0039] According to a further aspect of the present invention, soil data relating to a field or sub-field area is preferably received by a computing unit from real-time measurements using remote or nearby soil sensors.

[0040] According to a further aspect of the present invention, soil data relating to a field or sub-field area is data indicating the biological, biochemical, chemical, and / or physical properties of the soil in the field or sub-field area.

[0041] According to a further aspect of the present invention, soil data relating to a field or sub-field area is: a) Soil organic matter, total carbon content, organic carbon content, inorganic carbon content, boron content, phosphorus content, potassium content, nitrogen content, sulfur content, calcium content, iron content, aluminum content, chlorine content, molybdenum content, magnesium content, nickel content, copper content, zinc content, manganese content and / or pH value of the soil in the field or sub-field area, and / or b) Soil quality, soil sandiness, soil moisture, soil humidity, soil temperature, soil surface temperature, soil density, soil characteristics, soil conductivity and / or water retention capacity of the soil in the field or sub-field area. Includes.

[0042] According to a further aspect of the present invention, soil data relating to a field or sub-field area includes soil organic matter, total carbon content, organic carbon content, and / or soil inorganic carbon content of the soil in the field or sub-field area. According to a further aspect of the present invention, soil data relating to a field or sub-field area includes nitrogen content of the soil in the field or sub-field area. According to a further aspect of the present invention, soil data relating to a field or sub-field area includes soil quality, soil sandiness, soil moisture, soil humidity, soil temperature, soil surface temperature, soil density, soil characteristics, soil conductivity, and / or water retention capacity, and nitrogen content of the soil in the field or sub-field area. According to a further aspect of the present invention, soil data relating to a field or sub-field area includes soil characteristics. According to a further aspect of the present invention, soil data relating to a field or sub-field area includes soil moisture. According to a further aspect of the present invention, soil data relating to a field or sub-field area includes soil characteristics and soil moisture.

[0043] According to a further aspect of the present invention, the soil data relating to a field or sub-field area includes at least two different types of soil data, for example, soil characteristics and soil moisture or for example, soil characteristics and soil organic matter.

[0044] In a further embodiment of the present invention, the time frame between receiving yield data, or weather data, or soil data and outputting processing parameters is 1 millisecond to 1 hour, preferably 1 millisecond to 60 seconds, more preferably 1 millisecond to 5 seconds. In a preferred embodiment of the present invention, the time frame between receiving yield data, or weather data, or soil data and outputting processing parameters is not more than 30 minutes, preferably 20 minutes, more preferably 10 minutes, most preferably 5 minutes, particularly 2 minutes, particularly preferably 60 seconds, particularly more preferably 30 seconds, particularly most preferably 15 seconds, for example 5 seconds, for example 3 seconds. In a preferred embodiment of the present invention, the time frame between receiving yield data, or weather data, or soil data and outputting processing parameters is at least 1 millisecond, preferably 10 milliseconds, more preferably at least 50 milliseconds, most preferably at least 100 milliseconds.

[0045] In a further embodiment of the present invention, at least steps (step 2), (step 3), and (step 4) are performed in real-time mode and are preferably performed in a time frame ranging from 1 millisecond to 2 minutes, more preferably from 1 millisecond to 60 seconds, most preferably from 1 millisecond to 30 seconds, particularly in a time frame ranging from 1 millisecond to 15 seconds, particularly preferably from 1 millisecond to 5 seconds, particularly more preferably from 1 millisecond to 3 seconds, for example, in a time frame ranging from 1 millisecond to 1 second. According to another preferred embodiment of the present invention, at least steps (step 2), (step 3), and (step 4) are preferably performed in less than 1 minute, more preferably from 10 to 45 seconds, most preferably from 1 to 10 seconds, more preferably from 0.5 to 1 second, most preferably from 100 to 500 milliseconds, particularly from 10 to 100 milliseconds.

[0046] According to a further aspect of the present invention, real-time measurement is a measurement performed at time X by a sensor, such as a remote sensor, a proximal sensor, a remote soil sensor, or a proximal soil sensor or other instrument, and a computing unit receives data obtained from those measurements at time Y which is greater than time X. The time difference between X and Y is preferably in a time frame of 1 millisecond to 2 minutes, more preferably in a time frame of 1 millisecond to 60 seconds, most preferably in a time frame of 1 millisecond to 30 seconds, particularly in a time frame of 1 millisecond to 15 seconds, particularly preferably in a time frame of 1 millisecond to 5 seconds, particularly more preferably in a time frame of 1 millisecond to 3 seconds, for example, in a time frame of 1 millisecond to 1 second.

[0047] According to a further aspect of the present invention, the processing parameters are output as control signals for agricultural equipment or are further processed, the agricultural equipment being preferably a seed drill or a seed planter.

[0048] According to a further aspect of the present invention, the processing parameters are output as control signals for agricultural equipment incorporated into a processing management system or are further processed, the agricultural equipment being preferably a seed drill or a seed planter.

[0049] According to a further aspect of the present invention, the processing parameters are output as an application map useful for controlling agricultural equipment, which is preferably a seed drill or a seed planter, and such an application map preferably shows different processing parameters or different combinations of two or more processing parameters for different subfield areas.

[0050] In a preferred embodiment of the present invention, agricultural equipment is - Ground robot with variable speed applicator, - Aerial applicators (e.g., unmanned aerial vehicles, drones), - Means for seed application, including equipment for broadcast sowing, spot sowing, post-plow sowing, drill sowing, hill dropping, row planting and transplanting. - For example, in a standard drill seeder, seeds are removed from the hopper by a specific circular plate, released onto a shank, and carried to the bottom of the furrow by gravity. - Other variable-speed seed applicators It may be possible or may include them.

[0051] According to a further aspect of the present invention, the processing parameter is the seeding rate, and the yield data is a) Historical yield potential of a field or sub-field area, preferably determined based on remotely sensed green foliage area or biomass data of the field or sub-field area, or b) Historical yield of a field or sub-field area, determined based on the amount previously harvested from that field or sub-field area. Includes.

[0052] According to a further aspect of the present invention, the processing parameter is the seeding rate, and the yield data is a) Historical yield potential of a field or sub-field area, preferably determined based on remotely sensed green foliage area or biomass data of the field or sub-field area, and b) Historical yield of a field or sub-field area, determined based on the amount previously harvested from that field or sub-field area. Includes.

[0053] According to a further aspect of the present invention, the processing parameter is the seeding rate, and the yield data is a) Historical yield potential of a field or sub-field area, preferably determined based on remotely sensed green foliage area or biomass data of the field or sub-field area, and b) Historical yield of a field or sub-field area, determined based on the amount previously harvested from that field or sub-field area. The historical yield potential and historical actual yield data are preferably combined with predefined or user-defined weighting coefficients.

[0054] According to a further aspect of the present invention, the processing parameter is the sowing depth, and the soil data includes the soil characteristics and soil moisture of the soil in the field or sub-field area.

[0055] According to a further aspect of the present invention, the processing parameter is the sowing time, the crop data includes species or diversity data of at least one crop to be planted in the field or sub-field area and regulatory data relating to at least one crop to be planted in the field or sub-field area, and the soil data includes soil moisture of the soil in the field or sub-field area.

[0056] According to a further aspect of the present invention, the processing parameters are the seeding rate, the seeding depth, and the seeding time. - Crop data includes species or diversity data for at least one crop planted in a field or sub-field area and regulatory data relating to at least one crop planted in a field or sub-field area. - Soil data includes soil characteristics and soil moisture of the soil in the field or sub-field area. - Yield data is, a) Historical yield potential of a field or subfield area, preferably determined based on remotely sensed green foliage area or biomass data of the field or subfield area, and / or b) Historical yield of a field or sub-field area, determined based on the amount previously harvested from that field or sub-field area. Includes.

[0057] According to a further aspect of the present invention, the present invention also relates to a data processing system including means for performing a computer implementation of the present invention.

[0058] According to a further aspect of the present invention, the present invention also relates to a computer program product including instructions, wherein when executed by a computer, the instructions cause the computer to execute the computer implementation method of the present invention.

[0059] In a further aspect of the present invention, the present invention also relates to a computer-readable storage medium containing instructions, wherein, when executed by a computer, the instructions cause the computer to execute a computer implementation method according to the present invention.

[0060] According to a further aspect of the present invention, the present invention also relates to the use of processing parameters determined by a computer implementation method of the present invention.

[0061] According to a further aspect of the present invention, the present invention also relates to the use of processing parameters determined by a computer implementation method of the present invention. [Brief explanation of the drawing]

[0062] [Figure 1] The workflow of an embodiment of the present invention is shown. [Figure 2] This shows an embodiment of the data flow of the computer implementation method of the present invention. [Figure 3] The processing management system 500 is shown in outline. [Modes for carrying out the invention]

[0063] In relation to the present invention, the term "includes" means "contains".

[0064] In relation to the present invention, the term “field” or “agricultural field” is understood to mean any area where crop plants are produced, grown, and sown, and / or where they are planned to be produced, grown, and sown. The term “field” or “agricultural field” may also include horticultural fields and forestry fields.

[0065] In relation to the present invention, the term “genetic information” is understood to mean any kind of information relating to the genetic properties of a crop or plant, including but not limited to DNA sequences, RNA sequences, sites of DNA and / or RNA sequences, molecular structures of DNA and / or RNA, epigenetic information (e.g., methylation of DNA sites), information about gene mutations, information about gene copy number variations, information about gene overexpression, information about gene expression levels, information about gene shifts, information about the ratio of wild-type to mutants, information about the ratio between different mutants, information about the ratio between mutants and other variants (e.g., epigenetic variants), information about the ratio of different variants (e.g., epigenetic variants), plant diseases (e.g., leaf blight, yellow rust, soybean rust), or other diseases.

[0066] In relation to the present invention, the term “yield” is understood to mean the biomass of harvested plants or crops per unit area (e.g., expressed in hectares or square meters) and per vegetation period (e.g., season) (e.g., expressed in tons or kilograms), and yield is expressed, for example, as tons per hectare or kilograms per hectare. In particular, the term “yield” in this disclosure may mean both so-called “biological yield” and so-called “economic yield.” Preferably, “yield” means biological yield. “Biological yield” is defined as “total plant mass (biomass), including roots, produced per unit area and per growing period.” Regarding "economic yield," only "plant organs or components" "near the part of the plant where it grows" are considered, and "high biological yield is the basis for high economic yield" (see Hans Mohr, Peter Schopfer, Lehrbuch der Pflanzenphysiologie, 3rd edition, Berlin / Heidelberg 1978, pp. 560-561).

[0067] In relation to the present invention, the term "data processing" is understood to be any operation on data performed by a computer system for generating or outputting meaningful information. Data processing includes, but is not limited to, data validation, data analysis, data aggregation, data sorting, data classification, data summation, data transformation, data modification, data updating, etc. Data processing in a database or database system may also include automated requests in the database or database system and the automated output of the results of such requests.

[0068] In relation to the present invention, the term “database” is understood as any organized collection of data that is stored and can be accessed electronically from a computer system, and includes, but is not limited to, relational databases, non-relational databases, graph databases, network databases, cloud databases, in-memory databases, active databases, data warehouses, deductive databases, distributed databases, embedded databases, end-user databases, hypertext or hypermedia databases, knowledge databases, mobile databases, operational databases, parallel databases, probabilistic databases, real-time databases, spatial databases, time databases, term-oriented databases, and Excel databases. In preferred embodiments of the present invention, the database is at least one of the following databases: relational databases, non-relational databases, graph databases, network databases, cloud databases, in-memory databases, active databases, data warehouses, deductive databases, distributed databases, embedded databases, end-user databases, hypertext or hypermedia databases, knowledge databases, mobile databases, operational databases, parallel databases, probabilistic databases, real-time databases, spatial databases, time databases, term-oriented databases, and Excel databases.

[0069] In relation to the present invention, the term "database system" is understood to mean a system comprising two or more databases connected to one another, and includes, but is not limited to, a federated database system, an array database management system, and other database management systems.

[0070] In relation to the present invention, the terms "to be planted" or "to plant" (in the sense of "to be planted") are understood to mean placing seeds or seedlings on or in the soil.

[0071] In relation to the present invention, the “rate at which at least one crop is planted in a field or subfield area” is also referred to as the “sowing rate” and is understood to be the amount of seeds planted per unit area (i.e., square meters, hectares, or acres), particularly in terms of number or weight (e.g., in kilograms). The sowing rate is also called the sowing population or sowing density.

[0072] In relation to the present invention, "the depth for planting at least one crop in a field or subfield area," also referred to as "sowing depth," is understood to be the distance (for example, in centimeters) between the soil surface and the location in the soil where the planted seeds must be placed.

[0073] In a preferred embodiment of the present invention, the sowing depth may be 1 cm to 8 cm, more preferably 2 cm to 7 cm, and most preferably 3 cm to 6 cm.

[0074] In connection with the present invention, the time window for planting at least one crop in a field or sub-field area may range from 10 days to 1 hour, more preferably 7 days to 3 hours, most preferably 5 days to 5 hours, particularly preferably 3 days to 8 hours, particularly more preferably 2 days to 12 hours, particularly 36 hours to 16 hours, for example, 28 hours to 20 hours. The “time window for planting at least one crop in a field or sub-field area” is also called the “sowing time.”

[0075] In relation to the present invention, the term "application map" is preferably a map showing the two-dimensional spatial distribution of sowing rates, sowing depths, or sowing times to be applied to different locations or areas within a field. In relation to the present invention, the term "sub-field area" is understood to be a managed area or portion of an agricultural field. That is, an agricultural field can be spatially divided into two or more sub-field areas, each sub-field area may have different properties such as different biomass levels or different soil characteristics. In particular, an application map can show that different sowing rates and / or sowing depths must be applied or implemented in different sub-field areas. For example, an application map may show that in a first area the sowing rate should be, for example, 100,000 seeds per acre, and in a second area the sowing rate should be, for example, 200,000 seeds per acre.

[0076] In relation to the present invention, the chronological order between (Step 1) and (Step 2) may be simultaneous, or (Step 1) may be followed by (Step 2), or (Step 2) may be followed by (Step 1).

[0077] In relation to the present invention, the data relating to agricultural crop data is - For example, any data in a database or database system that is connected to or related to crop data, - Any data that can be converted or transformed into agricultural product data, - Identifiers related to crop data, or - Such agricultural data It is possible.

[0078] In relation to the present invention, data relating to static field data is: - For example, any data in a database or database system that is connected to or relating to static field data, - Any data that can be converted or transformed into static field data, - Identifiers related to static field data, or - Such static field data It is possible.

[0079] In relation to the present invention, the data relating to additional data is: - For example, any data in a database or database system that is connected to or related to additional data, - Any data that can be converted or transformed into additional data, - Identifiers related to additional data, or - Such additional data It may include.

[0080] In relation to the present invention, the data relating to yield data is: - For example, any data in a database or database system that is connected to or related to yield data, - Any data that can be converted or transformed into yield data, - Identifiers related to yield data, or - Such yield data It is possible.

[0081] In relation to the present invention, data relating to meteorological data is: - For example, any data in a database or database system that is connected to or related to weather data, - Any data that can be converted or transformed into weather data, - Identifiers related to weather data, or - Such weather data It is possible.

[0082] In relation to the present invention, data relating to soil data is - For example, any data in a database or database system that is connected to or related to soil data, - Any data that can be converted or transformed into soil data, - Identifiers related to soil data, or - Soil data It is possible.

[0083] In relation to the present invention, data relating to at least one processing parameter is: - For example, any data in a database or database system that is connected to or related to at least one processing parameter, - Any data that can be converted or transformed into at least one processing parameter, - An identifier relating to at least one processing parameter, or - At least one such processing parameter It is possible.

[0084] Since spatial variations in soil data are a source of uncertainty regarding seed performance, the measurement of one or more soil data points can be used together with or in combination with a predictive model to generate predicted soil data, which can then be further used in the computer-aided implementation of the present invention.

[0085] As used herein, the term “predictive model” may refer to a model that uses mathematical and computational methods to predict an event or outcome. For example, a predictive model is a trained computational predictive model, such as a machine learning model, which may be trained using “training data” to recognize patterns, classify data, and predict future events. Field trials may be conducted to obtain training data for a machine learning model. In another example, a predictive model is a parameterized mathematical method that uses an equation-based model to describe a phenomenon, for example, the effect of soil data on seed performance. Mathematical models are used to predict outcomes in some future state or time based on changes in model inputs. Sample data from field trials may be used to fit the parameters of a formula, and then the formula may be used to generate predicted seed performance (e.g., yield) from measured soil data.

[0086] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out to determine or output at least one processing parameter with the objective of achieving the best possible and most efficient growth of the crop plant, for example, the highest yield, or biomass or crop value, or crop quality.

[0087] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out according to the following logic: For subfield areas with higher yield data, e.g., higher historical yield potential or higher historical actual yield, the sowing rate may be increased accordingly. For subfield areas with lower yield data, e.g., lower historical yield potential or lower historical actual yield, the sowing rate may be decreased accordingly. This logic is called the "normal sowing rate logic" and is used, for example, for maize.

[0088] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out according to the following logic: For subfield areas with higher yield data, e.g., higher historical yield potential or higher historical actual yield, the planting rate may be reduced accordingly. For subfield areas with lower yield data, e.g., lower historical yield potential or lower historical actual yield, the planting rate may be increased accordingly. This logic is called the "reverse planting rate logic" and is used, for example, for soybeans.

[0089] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out according to the following logic: Depending on the crop data indicating crop type, crop species, crop diversity, or genetic variant, it is first determined whether the “normal yield seeding rate logic” (where crop yield generally increases with increasing crop seeding rate, which is applicable, for example, to maize) or the “reverse yield seeding rate logic” (where crop yield generally decreases with increasing crop seeding rate, which is applicable, for example, to soybeans) is applicable to a particular crop type, crop species, crop diversity, or genetic variant, and if the “normal yield seeding rate logic” is determined, In subfield areas with yield data indicating high historical yield potential or higher historical actual yield, the sowing rate can be increased accordingly, while in subfield areas with yield data indicating lower historical yield potential or lower historical actual yield, the sowing rate can be decreased accordingly. When the "reverse yield sowing rate logic" is determined, in subfield areas with yield data indicating higher historical yield potential or higher historical actual yield, the sowing rate can be decreased accordingly, while in subfield areas with yield data indicating lower historical yield potential or lower historical actual yield, the sowing rate can be increased accordingly.

[0090] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out according to the following logic: Depending on the crop data indicating crop species, crop diversity, or genetic variant, it is first determined whether the “normal yield planting rate logic” (where crop yield generally increases with increasing crop planting rate, which is applicable, for example, to maize) or the “reverse yield planting rate logic” (where crop yield generally decreases with increasing crop planting rate, which is applicable, for example, to soybeans) is applicable to a particular crop type, crop species, crop diversity, or genetic variant, and this determination is made a) Existing yield-sowing rate correlation data indicating whether the "normal yield-sowing rate logic" or the "reverse yield-sowing rate logic" is applicable to a specific crop type, crop species, crop diversity, or genetic variant, or b) Automatic or semi-automatic database search for yield-sowing rate correlation data, indicating whether the "normal yield-sowing rate logic" or the "reverse yield-sowing rate logic" is applicable to a specific crop type, crop species, crop diversity, or genetic variant. c) Simulated yield-sowing rate correlation data indicating whether the "normal yield-sowing rate logic" or the "reverse yield-sowing rate logic" is applicable to a particular crop type, crop species, crop diversity, or genetic variant, wherein the simulation is based on corresponding data for other crop types, crop species, crop diversity, or genetic variants having similar biological and / or physiological and / or morphological properties, and optionally uses machine learning techniques. The process is carried out based on the following: If the "normal yield seeding rate logic" is determined, the seeding rate may be increased in subfield areas with yield data indicating higher historical yield potential or higher historical actual yield, while the seeding rate may be decreased in subfield areas with yield data indicating lower historical yield potential or lower historical actual yield. If the "reverse yield seeding rate logic" is determined, the seeding rate may be decreased in subfield areas with yield data indicating higher historical yield potential or higher historical actual yield, while the seeding rate may be increased in subfield areas with yield data indicating lower historical yield potential or lower historical actual yield.

[0091] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out according to the following logic: For subfield areas with higher soil moisture, the sowing depth may be reduced accordingly. For subfield areas with lower soil moisture, the sowing depth may be increased accordingly. For subfield areas with soil characteristics having a large amount of clay, the sowing depth may be reduced accordingly. For subfield areas with soil characteristics having a large amount of sand, the sowing depth may be increased accordingly. For subfield areas where rain is expected (e.g., if there is a high probability of rainfall in the near future), the sowing depth may be reduced accordingly. For subfield areas where rain is not expected (e.g., if there is a very low probability of rainfall in the near future), the sowing depth may be increased accordingly.

[0092] In another preferred embodiment of the present invention, the data processing in (step 3) is carried out according to the following logic: If soil moisture is high, the sowing time will be determined to allow the use of this high soil moisture. If soil moisture is low and rainfall is expected in a few days, the sowing time will be determined to wait a few more days so that rainwater can be used as a water source and the seeds have enough water to grow. In any case, the sowing time will be determined to conform to regulatory data relating to the crop to be planted in the field or subfield area.

[0093] Figure 1 shows the workflow of an embodiment of the present invention.

[0094] In Figure 1, (a) at least one rate (sowing rate) for planting at least one crop in a field or subfield area, (b) at least one depth (sowing depth) for planting at least one crop in a field or subfield area, and (c) At least one time window (sowing time) for planting at least one crop in a field or subfield area. A computer implementation method for determining at least one processing parameter selected from the group consisting of, (Step 1) (102) The computing unit receives crop data relating to at least one crop planted in a field or subfield area and static field data relating to the field or subfield area from a database and / or from user input and / or from real-time measurements. (Step 2) (104) The computing unit extracts data from the database and / or from user input and / or from real-time measurements. (A) Yield data for a field or sub-field area, (B) Meteorological data relating to the field or sub-field area, and (C) Soil data relating to the field or sub-field area A step of receiving additional data of at least one type selected from a group consisting of, (Step 3) (106) Based at least on crop data and static field data and at least one type of additional data, (i) Data relating to crop data and / or static field data, (ii) Data relating to at least one processing parameter, (iii) Data relating to at least one type of additional data selected from the group consisting of yield data, meteorological data, and soil data. A step of starting and / or performing data processing in at least one database and / or database system, including (Step 4) (108) Step of outputting at least one processing parameter based on the results of data processing. A computer implementation method including this is shown.

[0095] Figure 2 Figure 2 shows an embodiment of the data flow of the computer implementation method of the present invention.

[0096] In the first data flow section corresponding to (Step 1) and (Step 2) of the computer implementation method of the present invention, a data source 101 becomes available. The data source may be, for example, a user device 103, a database 105, and / or a sensor 107. The term “user input device” is understood to be a computer, smartphone, tablet, smartwatch, monitor, data storage device, or any other device through which users, including humans and robots, can input or move data into the field management system 112. The term “input database” is understood to be any organized collection of data, which is stored, electronically accessible from the computer system, and from which data can be input or moved into the field management system 112. The term “sensor” is understood to be any kind of physical or virtual device, module, or machine capable of detecting or receiving real-world information and transmitting this real-world information to another system, including temperature sensors, humidity sensors, moisture sensors, pH sensors, pressure sensors, soil sensors, crop sensors, water sensors, and cameras.

[0097] In a further data flow section, data originating from one of the data sources 101 is optionally preprocessed in a data preprocessing section 110. Such data preprocessing may include data calibration, data transformation (e.g., to a different format), data correction, data validation, and data verification.

[0098] In a further data flow section, data originating from one of the data sources 101 and optionally preprocessed in the data preprocessing section 110 is input into the field management system 112 as, for example, crop data 122, static field data 124, yield data 126, soil data 128, or weather data 130.

[0099] In a further data flow section corresponding to (step 3) of the computer implementation method of the present invention, the above-described data is processed by the field management system of the data processing section 120 using, for example, one or more crop-related models 142, one or more field-related models 144, one or more yield-related models 146, one or more soil-related models 148, one or more weather models 150, or a combination of such models. The crop-related model 142 is an algorithm capable of determining, predicting and / or simulating crop species, crop biophenology, crop growth, crop development, and other crop-related characteristics based on specific input data. The field-related model 144 is an algorithm capable of determining, predicting and / or simulating field soil characteristics or other field-related characteristics based on specific input data. The yield-related model 146 is an algorithm capable of determining, predicting and / or simulating crop yield, including crop biophenology and crop biomass, based on specific input data. The agricultural recommendation model 148 is an algorithm capable of determining one or more methods, products (particularly seeds or seedlings), dosages (particularly sowing rates), time windows, or other processing parameters for achieving specific real-world agricultural objectives, particularly sowing, based on specific input data. The meteorological model 150 is an algorithm capable of determining, predicting, and / or simulating any meteorological parameters, including their development, such as temperature, precipitation, moisture, humidity, sunshine, or wind speed. Within the data processing section 120, the output of one of the above-described models may also be used directly as input to another of the above-described models. Within the data processing section 120, at least two, preferably at least three, of the above-described models may operate in either a parallel configuration, a sequential configuration, or a combination of a parallel and a sequential configuration.

[0100] In a further data flow section corresponding to (step 4) of the computer implementation method of the present invention, the final output of the model in the data processing section 120 is transferred from the field management system to the data output layer 160 and output, for example, to the output database 164 on the user device 162 or as a control file 166. The term “user output device” is understood to be a computer, smartphone, tablet, smartwatch, monitor, data storage device or any other device through which users, including humans and robots, can receive data from the field management system 112. The term “output database” is understood to be any organized collection of data, which is stored, electronically accessible from the computer system, and can receive data output or transferred from the field management system 112. The term “control file” is understood to be any binary file, data, signals, identifiers, codes, images or any other machine-readable or machine-detectable element that helps to control a machine or device, such as an agricultural processing device.

[0101] Figure 3 Figure 3 schematically shows the processing management system 500. Processing parameters determined by the computer implementation method of the present invention will be output as control signals for agricultural equipment incorporated into the processing management system 500 or will be further processed, the agricultural equipment being preferably a seed drill or seed planter. The processing management system 500 may include a seed drill or seed planter 510, a data management system 520, a field management system 112, and a client computer 540. The seed drill or seed planter 510 may be a ground robot having a variable-speed applicator or other variable-speed applicator for applying seed products (particularly seeds and seedlings) to a field 502.

[0102] In the embodiment shown in Figure 3, the seed drill or planter 510 is embodied as a smart agricultural machine. The smart agricultural machine 510 may be a smart seed drill or smart planter and includes a connectivity system 512. The connectivity system 512 may be configured to communicatively connect the smart agricultural machine 510 to a distributed computing environment. The connectivity system may be configured to provide data collected by the smart agricultural machine 510 to a data management system 520, a field management system 112, and / or client computers 540 in the distributed computing environment.

[0103] The data management system 520 may be configured to transmit data to or receive data from the smart agricultural machinery 510. For example, a detected map or applied map, including data recorded during application to field 502, may be transmitted from the smart agricultural machinery 510 to the data management system 520. For example, the data management system 520 may include georeference data for different fields and related processing maps.

[0104] The field management system 520 may be configured to provide control protocols, activation codes, or decision logic to the smart agricultural machinery 510, or to receive data from the smart agricultural machinery 510. Such data may also be received via the data management system 520.

[0105] The field computer 540 may be configured to receive user input and provide a field identifier and optional processing directives to the field management system 112. Alternatively, the field identifier may be provided by a seed drill or planter 510. Alternatively, optional processing directives may be determined using, for example, a growth stage model, weather modeling, or neighboring field incidence rates. Based on the field identifier and optional processing directives, the field management system 112 may retrieve the corresponding agricultural field and the associated processing map in the data management system 520. The field computer 540 may be further configured to receive client data from the field management system 112 and / or smart agricultural machinery 510. Such client data may include, for example, field analysis data that provides insights into application schedules implemented on a particular field using smart agricultural machinery 510 or the health status of a particular field.

[0106] The processing device 510, the data management system 520, the field management system 112, and the client computer 540 may be associated with a network. For example, the network may be the Internet. Alternatively, the network may be any other type and number of networks. For example, the network may be implemented by several local area networks connected to a wide area network. The network may include any combination of wired networks, wireless networks, wide area networks, local area networks, etc.

[0107] The data processing system of the present invention may be implemented as a field management system 112, within the field management system 112, or as part of the field management system 112, to provide control data to the smart agricultural machinery 510 by performing the methods described above. For example, the field management system 112 may receive seed drill / seeder configuration data from the seed drill or seeder 510 via the connection system 512. The field management system 112 may monitor environmental data by receiving geo-dependent environmental data (e.g., temperature, moisture, humidity, and / or wind speed) from one or more sensors installed on the seed drill or seeder 510. Alternatively or in addition, the field management system 112 may receive geo-dependent environmental data from a weather service.

Claims

1. A computer implementation method for determining processing parameters, (Step 1) (102) The computing unit in the field management system (112) receives crop data relating to at least one crop planted in a field or sub-field area and static field data relating to the field or sub-field area via a database (105) and / or via a user device (103) and / or via a sensor (107). (Step 2) (104) The computing unit, via the database (105) and / or via the user device (103) and / or via the sensor (107), (A) Yield data relating to the field or sub-field area, (B) Meteorological data relating to the field or sub-field area, and (C) Soil data relating to the field or sub-field area A step of receiving one type of additional data selected from a group consisting of, (Step 3) (106) A step of determining, predicting, or simulating the processing parameters in the data processing section (140) of the field management system (112) using at least one of the crop-related models, field-related models, soil-related models, yield-related models, weather-related models, and / or agricultural models, based on the crop data, the static field data, and one type of additional data, wherein the crop-related model, the field-related model, the soil-related model, the yield-related model, the weather-related model, and / or agricultural model are trained machine learning models, (Step 4) (108) Based on the results of the step of determining, predicting, or simulating the processing parameters, (a) at least one seeding rate for planting at least one crop in the field or sub-field area, (b) at least one sowing depth for planting at least one crop in the field or subfield area, (c) at least one sowing time for planting at least one crop in the field or sub-field area, A step of outputting the processing parameters selected from the group consisting of the following: A computer implementation method including

2. The crop data relating to the at least one crop planted in the field or sub-field area is: a) Seed or diversity data of at least one crop to be planted in the field or sub-field area, preferably seed or diversity data including genetic information of the crop, and / or b) Crop value data of the at least one crop planted in the field or sub-field area, preferably including the oil content, protein content and / or nutrient content of the crop. c) Agricultural data relating to the at least one crop planted in the field or sub-field area, preferably including the number of days to sexual maturity, virility, and occurrence rate of the crop, and / or d) Regulatory data relating to the at least one crop planted in the field or sub-field area, preferably including local information relating to the legal framework and / or regulations relating to the processing parameters, local information relating to product management measures, and information relating to biodiversity regulations or biodiversity measures. The computer implementation method according to claim 1, including the method described in claim 1.

3. The static field data relating to the aforementioned field or sub-field area is, a) GPS (Global Positioning System), geographic location data and / or geographic positioning data relating to the field or the sub-field area, and / or b) Elevation, height, slope and / or relief data relating to the field or sub-field area, and / or c) Data relating to the past application of crop protection products or crop nutrition products in the field or sub-field area, data relating to preseason soil treatment in the field or sub-field area, and / or data relating to the type of cultivation in the field or sub-field area. A computer implementation method according to claim 1 or 2, including the method described in claim 1 or 2.

4. The yield data relating to the aforementioned field or sub-field area is, a) Historical yield potential of the field or sub-field area, preferably determined based on remotely sensed green foliage area or biomass data of the field or sub-field area, and / or b) The historical actual yield of the field or sub-field area, determined based on the amount previously harvested from the field or sub-field area, and / or c) Predicted yield potential of the field or sub-field area. A computer implementation method according to any one of claims 1 to 3, including the method described in any one of claims 1 to 3.

5. The yield data relating to the aforementioned field or sub-field area is, a) Historical yield potential of the field or sub-field area, preferably determined based on remotely sensed green foliage area or biomass data of the field or sub-field area, and / or b) Historical yield of the field or sub-field area, determined based on the amount previously harvested from the field or sub-field area. The computer implementation method according to any one of claims 1 to 4, wherein the historical yield potential and historical actual yield data are preferably combined with a predefined or user-defined weighting coefficient.

6. The additional data of the type further includes meteorological data of the field or the subfield area, The computer implementation method according to any one of claims 1 to 5, wherein the meteorological data relating to the field or sub-field area includes temperature, air temperature, soil temperature, soil surface temperature, canopy temperature, humidity, air humidity, relative humidity, precipitation, moisture, wind conditions, wind speed, and / or sunshine level data relating to the field or sub-field area.

7. The additional data of the type further includes soil data of the field or the subfield area, The computer implementation method according to any one of claims 1 to 6, wherein the soil data relating to the field or sub-field area is data indicating the biological, biochemical, chemical and / or physical properties of the soil in the field or sub-field area.

8. The soil data relating to the aforementioned field or sub-field area is, a) The soil organic matter, total carbon content, organic carbon content, inorganic carbon content, boron content, phosphorus content, potassium content, nitrogen content, sulfur content, calcium content, iron content, aluminum content, chlorine content, molybdenum content, magnesium content, nickel content, copper content, zinc content, manganese content and / or pH value of the soil in the field or sub-field area, and / or b) Soil quality, soil sandiness, soil moisture, soil humidity, soil temperature, soil surface temperature, soil density, soil characteristics, soil conductivity and / or water retention capacity of the soil in the field or sub-field area. The computer implementation method according to claim 7, including the method described in claim 7.

9. The computer implementation method according to any one of claims 1 to 8, wherein the time frame between receiving the yield data, or the weather data, or the soil data, and the output of the processing parameters is 1 millisecond to 1 hour.

10. The computer implementation method according to any one of claims 1 to 9, wherein at least the steps (step 2), (step 3), and (step 4) are performed in real time mode.

11. The computer implementation method according to any one of claims 1 to 10, wherein the processing parameters are output as control signals for agricultural equipment or are further processed, and the agricultural equipment is preferably a seed drill or a seed planter.

12. The computer implementation method according to any one of claims 1 to 11, wherein the processing parameters are output as an application map useful for controlling agricultural equipment, and the agricultural equipment is preferably a seed drill or a seed planter.

13. A data processing system comprising means for performing the computer implementation method described in any one of claims 1 to 12.

Images