Method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field

Abstract

Computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, comprising: providing (100) leaf area index data for the agricultural field; providing (110) chlorophyll content data for the agricultural field; providing (120) an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data; providing (130) nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model. The method further comprises determining stem weight data based on a provided crop specific leaf weight ratio and determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration. Providing nitrogen uptake data of plants and / or plant parts of the agricultural field is further based on the determined stem nitrogen data.

Description

[0001] METHOD FOR PROVIDING NITROGEN UPTAKE DATA OF PLANTS AND / OR PLANT PARTS OF AN AGRICULTURAL FIELD

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, a system for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, an apparatus for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, a fertilizer application device for applying a fertilizer product on an agricultural field, a corresponding computer program element and a respective use of data.

[0004] TECHNICAL BACKGROUND

[0005] The general background of this disclosure is the treatment of an agricultural field with a fertilizer product. Farmers apply fertilizer products, e.g. urea, ammonium nitrate, ammonium sulfate, calcium ammonium nitrate, manure, slurry etc. which contain nitrogen forms such as ammonium, nitrate, and / or organic nitrogen. Nitrogen is an essential element for plant growth, plant health and reproduction. Some part of the plant available nitrogen in soils (ammonium and nitrate) originate from decomposition processes (mineralization) of organic nitrogen compounds such as humus, plant and animal residues and organic fertilizers. Another part derives from rainfall. On a global basis, by far the biggest part (according to some sources around 90%), however, are supplied to the plant by organic and inorganic (so-called mineral) nitrogen fertilizers. The mainly used inorganic nitrogen fertilizers comprise urea and / or ammonium compounds or derivatives thereof, i.e. nearly 90% of the nitrogen fertilizers applied worldwide is in the urea and / or Nf form (cf. Subbarao et al., 2012, Advances in Agronomy, 114, 249-302). However, it is often difficult for a farmer to make an objective decision, if and how the respective fertilizer products should be applied.

[0006] Mineral fertilizer nitrogen (N) is pivotal to meet global food demand. More than 50% of the global protein production relies on mineral N fertilizer production and use. However, excess N fertilizer use threatens the quality of atmospheric, aquatic, marine and terrestrial pools and contributes to global warming. The methods and systems of present disclosure will contribute to a more sustainable use of fertilizer applications. As will be understood from the description below, this can be achieved, for example, by allowing for determining in-situ crop N uptake during crop growth season.

[0007] In the present disclosure, it is referred to the article ..Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval “, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 11 . Issue: 5. May 2018).

[0008] It has been found that there is a further need to provide objective means to assist a famer in the use of fertilizer products. In particular, a further need exists to provide objective means to avoid overfertilization in practice.

[0009] SUMMARY OF THE INVENTION

[0010] In one aspect of the present disclosure, a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field is disclosed, comprising: providing leaf area index data for the agricultural field; providing chlorophyll content data for the agricultural field; providing an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data; providing nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model.

[0011] In particular, in some embodiments, the method may optionally comprise determining stem weight data based on a provided crop specific leaf weight ratio; determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration. According to the present disclosure, providing nitrogen uptake data of plants and / or plant parts of the agricultural field is further based on the determined stem nitrogen data.

[0012] In particular, the crop specific leaf weight ratio may be used as an input for the nitrogen uptake model or determined by a growth model, e.g. as part of the nitrogen uptake model.

[0013] A further aspect of the present disclosure relates to a system for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, comprising: a leaf area index data providing unit configured to provide leaf area index data for the agricultural field; a chlorophyll content data providing unit configured to provide chlorophyll content data for the agricultural field; an nitrogen uptake model providing unit configured to provide an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data.

[0014] The system may optionally further comprise: a stem nitrogen concentration determination unit configured to determine stem weight data based on a provided crop specific leaf weight ratio and determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration; and an nitrogen uptake data providing unit configured to provide nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model, and further based on the determined stem nitrogen data.

[0015] A further aspect of the present disclosure relates to an apparatus for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: providing leaf area index data for the agricultural field; providing chlorophyll content data for the agricultural field; providing an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data.

[0016] The steps may optionally further comprise: determining stem weight data based on a provided crop specific leaf weight ratio; and determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration; providing nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model and further based on the determined stem nitrogen data.

[0017] In particular, the crop specific leaf weight ratio may be used as an input for the nitrogen uptake model.

[0018] A further aspect of the present disclosure relates to a fertilizer application device for applying a fertilizer product on an agricultural field, wherein the control data for the fertilizer application device are at least partially provided according to a disclosed computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field. It is noted that a fertilizer application device may be a specific form of an application device, e.g., a spraying device. The application device may comprise a processor configured to receive, via a communication interface, the control data and / or data for carrying out one or more of the method steps for providing the control data, and / or configured to carry out at least some or all of the method steps for providing the control data.

[0019] A further aspect of the present disclosure relates to a computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the disclosed computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field.

[0020] A further aspect of the present disclosure relates to a use of leaf area index data, chlorophyll content data, nitrogen uptake model and / or satellite images in a computer- implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field.

[0021] In an aspect according to the disclosure,

[0022] This and embodiments described herein relate to the method, the system, the agricultural device, the use, the computer program element lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa. As used herein “determining" also includes “estimating, calculating, initiating or causing to determine", “generating" also includes “initiating or causing to generate", and “providing” also includes “initiating or causing to determine, generate, select, send, query or receive”.

[0023] Leaf area index data may, in particular, include a leaf area index, and optionally other data. Ch lorphy II content data may, in particular, include chlorophyll content, and optionally other data. Nitrogen uptake data may, in particular, include a nitrogen uptake, and optionally other data. Stem weight data may, in particular, include stem weight, and optionally other data.

[0024] The chlorophyll content data may be provided as leaf chlorophyll content (LC) data, which may include leaf chlorophyll content and optionally other data, and / or canopy chlorophyll content (CCHL) data, which may include canopy chlorophyll content and optionally other data.

[0025] The Leaf Area Index is a commonly used term in the art. It may be understood as an area of leaf per area of ground (e.g. m2leaf per m2ground). It may be derived from proximal or remote sensing devices, such as from satellite images, as is explained in detail below.

[0026] The LAI may be converted to leaf mass (also referred to as LDM; kg per m2ground, for example). This can be done by multiplication of the LAI with a specific leaf area (referred to as SLA; m2leaf per kg leaf DrM (dry mass)). SLA values may be crop and growth stage specific and are derived from a database or crop growth model. Stem mass (also referred to SDM; kg per m2ground) may be derived from a leaf weight ratio (referred to as LWR; kg LDM / (kg LDM+SDM)).

[0027] The LWR, which may be considered to be a biomass allocation coefficient, is crop and growth stage specific and may be derived from a database or from a crop growth model.

[0028] Chlorophyll concentration (referred to as CHL; pg Chi per cm2of leaf area) may be derived from proximal or remote sensing devices, for example satellite images.

[0029] CHL can be converted to leaf nitrogen concentration (LNC; kg N per kg LDM) by using chlorophyll-to-N coefficient (referred to as Chl_N, mass N per mass Chi) and molecular mass (MM; kg per mol) of chlorophyll.

[0030] Crop-specific Chl_N and MM may, for example, be retrieved or derived from a data source. Published data exist that can be retrieved to that end.

[0031] According to the present disclosure, the leaf weight ratio, LWR, may be a dynamic value, i.e. , a value that changes over time, e.g. throughout a season. As an example, a daily leaf weight ratio may be used. The leaf weight ratio is an example of a dynamic biomass allocation coefficient.

[0032] The LWR may be dependent on geographic position. In particular, the LWR may be dependent on time and location. The leaf weight ratio may depend on at least one of geographic position, weather conditions, and agricultural practices / parameters, such as sowing date and variety.

[0033] The leaf weight ratio may be determined using a model. For example, the model may take into account at least one of geographic position, weather conditions, internal logics of the model and agricultural practices / parameters, such as sowing date and variety.

[0034] The use of the leaf weight ratio in determining the nitrogen uptake allows for higher precision determinations of nitrogen uptake. As an example a (crop) model configured to provide a site-specific crop organ allocation coefficient, such as leaf-weight ratio (LWR) may be employed to obtain the LWR. LWR, provided by a model, is affected by geographic position, weather conditions, internal logics of the model and agricultural practices, such as sowing date and variety.

[0035] It will be understood from the above that the present disclosure may provide a (nitrogen uptake) model that provides data indicative of dry mass of one or more crop organs (e.g., at least one of root mass data, stem weight data, leave mass data, seeds mass data and fruits mass data) based on a dynamic biomass allocation coefficient (such as LWR and / or root-to-shoot and / or stem-to-shoot), particularly on a daily basis throughout the season.

[0036] The present disclosure may provide a (nitrogen uptake) model that is a process-based model and may take, as input data, at least one of crop type, variety, seeding I planting date, and site-specific soil and weather data. This is the case when a growth model as disclosed hereinbelow is employed (details thereon are provided below), e.g. a plantspecific growth model by which stem weight data are determined by a plant-specific growth model. Such a growth modes may receive at least one of crop type, variety, seeding I planting date, and site-specific soil and weather data as input data and output, as an example, the stem weight data.

[0037] It is an object of the present disclosure to provide objective means to assist a famer in the use of fertilizer products. It is in particular an object of the present disclosure to provide objective means to avoid overfertilization in practice. It is further an object of the present disclosure to provide data with which control data for a fertilizer application device can be provided.

[0038] These and other objects, which become apparent upon reading the following description, are solved by the subject matters of the independent claims. The dependent claims refer to preferred embodiments of the invention.

[0039] The term “agricultural field” as used herein is to be understood broadly in the present case and presents any area, i.e. , surface and subsurface, of a soil to be treated with a fertilizer product. The agricultural field may be any plant or crop cultivation area, such as a farming field, a greenhouse, or the like. A plant may be a crop, a weed, a volunteer plant, a crop from a previous growing season, a beneficial plant or any other plant present on the agricultural field. The agricultural field may be identified through field data referring to its geographical location or geo-referenced location data. A reference coordinate, a size and / or a shape may be used to further specify the agricultural field. The field data may be used to calculate the application rate / application amount for the agricultural field. The field data may further be used to specify in which climate region an agricultural field is located. The field data may further be used, in particular the geographical location of the agricultural field, for providing weather data, e.g. historical, actual and / or forecast weather data. Notably, the field data may further be used to provided soil parameter data, topography data, and any further data which may be used to fine-tune the emission calculation model.

[0040] The “leaf area index (LAI)” is defined, as the total one-sided leaf area per unit ground area. LAI is one of the most important biophysical parameters characterizing a canopy. The “chlorophyll content” may be provided as chlorophyll content (LC) data and / or canopy chlorophyll content (CCHL) data. In this respect, the leaf area index (LAI) data and the chlorophyll content data for the agricultural field may be obtained by using a leaf area and chlorophyll content model configured to provide leaf area index data and chlorophyll content data based on surface reflectance bands of at least one satellite image. The leaf area index and chlorophyll content model are preferably adapted to different crop varieties, crop types, growth stage, soil conditions and / or data sources.

[0041] The LAI data may be provided in a netCDF file format, e.g., as global, multi-band NetCDF4 file with metadata according to the Climate and Forecast (CF) conventions. In addition, or alternatively, the LAI data may be provided as INSPIRE-compliant metadata files in XML format, corresponding XSLT for XML viewing and a subsampled, colored quicklook in GeoTiff format.

[0042] Moreover, the leaf area and chlorophyll content model may be machine learning model and the leaf area index data and the chlorophyll content data for the agricultural field may obtained by utilizing the leaf area and chlorophyll content model. That is, for example, the prediction of the machine learning model may be the leaf area index data and the chlorophyll content data. The machine learning model may preferably: an artificial neural network (ANN), multiple linear regression, random forest regression, or an approach that is able to establish a statistical relationship to predict leaf area index data and chlorophyll content data. The leaf area and chlorophyll content model may be provided as one integral machine learning model or as two separate machine learning models, one directed to the leaf area index, and one directed to the chlorophyll content. In case of two separate machine learning models, as an example, one may predict the leaf area index data and the other may predict the chlorophyll content.

[0043] The term “machine learning algorithm” may comprise decision trees, naive bayes classifications, nearest neighbors, neural networks, convolutional or recurrent neural networks, transformers, generative adversarial networks, support vector machines, linear regression, logistic regression, random forest and / or gradient boosting algorithms. Preferably, the result of a machine learning algorithm is used to adjust the application rate decision logic. Preferably, the machine learning algorithm is organized to process an input having a high dimensionality into an output of a much lower dimensionality. Such a machine learning algorithm is termed “intelligent” because it is capable of being “trained.” The algorithm may be trained using records of training data. A record of training data comprises training input data and corresponding training output data. The training output data of a record of training data is the result that is expected to be produced by the machine learning algorithm when being given the training input data of the same record of training data as input. The deviation between this expected result and the actual result produced by the algorithm is observed and rated by means of a “loss function”. This loss function is used as feedback for adjusting the parameters of the internal processing chain of the machine learning algorithm. For example, the parameters may be adjusted with the optimization goal of minimizing the values of the loss function that result when all training input data is fed into the machine learning algorithm and the outcome is compared with the corresponding training output data. The result of this training is that given a relatively small number of records of training data as “ground truth”, the machine learning algorithm is enabled to perform its job well for several records of input data that higher by many orders of magnitude.

[0044] The term “control data” as used herein is to be understood broadly in the present case and presents any data being configured to operate and control an application device. The control data are provided by a control unit and may be configured to control one or more technical means of the application device, e.g., the drive control but is not limited thereto.

[0045] The term “fertilizer application device” used herein is to be understood broadly in the present case and represents any device being configured to fertilizers on the soil of an agricultural field or on the crop canopy of an agricultural field. The application device may be configured to traverse the agricultural field. The application device may be a ground or an air vehicle, e.g., a tractor, a rail vehicle, a robot, an aircraft, an unmanned aerial vehicle (UAV), a drone, or the like. The application device can be an autonomous or a non-autonomous application device.

[0046] The term “fertilizer” or “fertilizer product” as used herein is to be understood broadly and comprises any solid or liquid fertilizer products and combinations thereof. The term fertilizing / fertigation as used herein is to be understood broadly in the present case and presents any action to put, place or bring in fertilizers / a fertilizer product in a soil area of an agricultural field. A fertilizer is any material of natural or synthetic origin that is applied to soil or to plant tissues to supply plant nutrients. The fertilizer product may contain urea, NO3’, NH4+-ions, NH3and / or organic N or may be able produce NH4+ions or NH3in the soil by decomposition, e.g., hydrolysis. The term fertilizers may be understood as organic and / or chemical compounds applied to promote plant and fruit growth. Fertilizers are typically applied either through the soil (for uptake by plant roots), through soil substituents (also for uptake by plant roots), or by foliar feeding (for uptake through leaves). The term also includes mixtures of one or more different types of fertilizers as mentioned below. The term fertilizers may be subdivided into several categories including: a) organic fertilizers (composed of decayed plant / animal matter), b) inorganic fertilizers (composed of chemicals and minerals) and c) urea-containing fertilizers. Organic fertilizers may include manure, e.g. liquid manure, semi-liquid manure, biogas manure, stable manure or straw manure, slurry, worm castings, peat, seaweed, compost, sewage, and guano. Green manure crops are also regularly grown to add nutrients (especially nitrogen) to the soil. Manufactured organic fertilizers include e.g. compost, blood meal, bone meal and seaweed extracts. Further examples are enzyme digested proteins, fish meal, and feather meal. The decomposing crop residue from prior years is another source of fertility. In addition, naturally occurring minerals such as mine rock phosphate, sulfate of potash and limestone are also considered inorganic fertilizers. Inorganic fertilizers are usually manufactured through chemical processes (e.g. N from such as the Haber-Bosch process), also using naturally occurring deposits, while chemically altering them (e.g. concentrated triple superphosphate). Naturally occurring inorganic fertilizers include Chilean sodium nitrate, mine rock phosphate, limestone, and raw potash fertilizers. The inorganic fertilizer may, in a specific embodiment, be a “NPK fertilizer”, “NP fertilizers” and “NK fertilizers”. NPK fertilizers are inorganic fertilizers formulated in appropriate concentrations and combinations comprising the three main nutrients nitrogen (N), phosphorus (P) and potassium (K) as well as typically S, Mg, Ca and trace elements. NP fertilizers are inorganic fertilizers formulated in appropriate concentrations and combinations comprising two main nutrients nitrogen (N) and phosphorus (P) as well as typically S, Mg, Ca and trace elements. NK fertilizers are inorganic fertilizers formulated in appropriate concentrations and combinations comprising the two main nutrients nitrogen (N) and potassium (K) as well as typically S, Mg, Ca and trace elements Other inorganic fertilizers may include ammonium nitrate, calcium ammonium nitrate, ammonium sulfate nitrate, ammonium sulfate or ammonium phosphate. Urea-containing fertilizer may, in specific embodiments, be urea, formaldehyde urea, urea ammonium nitrate (UAN) solution, urea sulfur, stabilized urea, urea based NPK-fertilizers, or urea ammonium sulfate. Also envisaged is the use of urea as fertilizer. In case urea-containing fertilizers or urea are used or provided, it is particularly preferred that urease inhibitors as defined herein above may be added or additionally be present or be used at the same time or in connection with the urea- containing fertilizers. Urea-containing fertilizers are hydrolyzed by microorganisms, thereby releasing ammonia that in turn forms ammonium-ions. Urea-containing fertilizers may thus be considered as a storage form of ammonium. The fertilizer may be selected from solid or liquid ammonium- and / or nitrate-containing inorganic fertilizers, such as an NPK, NP and NK fertilizers, ammonium nitrate, calcium ammonium nitrate, ammonium sulfate nitrate, ammonium sulfate, calcium nitrate or ammonium phosphate; solid or liquid organic fertilizers, such as liquid manure, semi-liquid manure, stable manure, biogas manure and straw manure, worm castings, compost, seaweed or guano, or an urea- containing fertilizer such as urea, formaldehyde urea, urea ammonium nitrate (UAN) solution, urea sulfur, stabilized urea, urea based NPK-, NP- and NK-fertilizers, urea ammonium sulfate, or a mixture thereof. Preferably, the fertilizer contains NH4+-ions; more preferably the fertilizer is selected from solid or liquid ammonium-containing inorganic fertilizers. Fertilizers may be provided in any suitable form, e.g. as powders, crystals, solid coated or uncoated prills or granules, in liquid or semi-liquid form, or as sprayable fertilizer. The fertilizer may be applied in the uses and methods of application via fertigation. Coated fertilizers may be provided with a wide range of materials. Coatings may, for example, be applied to granular or prilled nitrogen (N) fertilizer or to multi-nutrient fertilizers. Typically, urea is used as base material for most coated fertilizers. Alternatively, ammonium, nitrate or NPK, NP and NK fertilizers are used is base material for coated fertilizers. The present disclosure, however, also envisages the use of other base materials for coated fertilizers, any one of the fertilizer materials defined herein. In certain embodiments, elemental sulfur may be used as fertilizer coating.

[0047] The term “control data” as used herein is to be understood broadly in the present case and presents any data being configured to operate and control an agricultural device and / or a part of an agricultural device. The control data may be provided by a control unit and may be configured to control one or more technical means of the agricultural device, e.g., the drive control, the steering, product output, flight height, etc. The control data may comprise meta data for controlling the amount of agricultural product which is applied onto the agricultural field at which position in the agricultural field. In this respect, the control data may further be configured to control nozzles, pumps, valves and / or dispenser discs of an application device. Such controlling may be performed in a so called on / off manner, i.e., in this case, an output control can be done in a so-called on / off manner, where the output means is fully open or fully closed, for example, a valve that is fully open or fully closed. Alternatively, an output control can also take place as a so-called variable control. In this case, an output means can also take output values between fully open and fully closed.

[0048] The term “providing” as used herein is to be understood broadly in the present case and represents any providing, receiving, querying, measuring, calculating, determining, transmitting of data, but is not limited thereto. Data may be provided by a user via a user interface, depicted / shown to a user by a display, and / or received from other devices, queried from other devices, measured other devices, calculated by other device, determined by other devices and / or transmitted by other devices. The term “data” as used herein is to be understood broadly in the present case and represents any kind of data. Data may be single numbers / numerical values, a plurality of a numbers / numerical values, a plurality of a numbers / numerical values being arranged within a list, 2 dimensional maps or 3 dimensional maps, but are not limited thereto.

[0049] In the following particularly preferred embodiments are disclosed, which may be combined with the above-disclosed methods, systems, apparatuses, devices and / or use cases.

[0050] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the chlorophyll content data is provided as leaf chlorophyll content (LC) data and / or canopy chlorophyll content (CCHL) data.

[0051] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the leaf area index data and the chlorophyll content data for the agricultural field are obtained by using a leaf area and chlorophyll content model configured to provide leaf area index data and chlorophyll content data based on surface reflectance bands of at least one satellite image, wherein the leaf area and chlorophyll content model is preferably adapted to different crop varieties, crop types, growth stage, soil conditions and / or data sources.

[0052] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the leaf area index and chlorophyll content models are machine learning model. The leaf area index data and the chlorophyll content data for the agricultural field are obtained by utilizing the leaf area index and chlorophyll content model, wherein the machine learning model is preferably: an artificial neural network (ANN), multiple linear regression, random forest regression, or an approach that is able to establish a statistical relationship to predict leaf area index data and chlorophyll content data. The machine learning model with respect to the leaf area index data may be a XGBBOOST regression model based on satellite image analysis. In linear regression, in general, the model makes a prediction based on features that are input in the model, for example by creating a weighted sum of the features. In the present case, predictions rely on features that are derivable from the satellite images. For obtaining leaf area index data and chlorophyll content data, one or more regression models may be used. There are machine learning models that can predict both, leaf area index data and chlorophyll content data, and alternatively using separate models is an option, which, as the skilled person will understand, depends on the situation at hand, e.g. model architecture.

[0053] In the present disclosure, features that are derivable from the satellite images and suitable for the regression model approach may contain spectral bands derived and / or a vegetation index derived from the satellite images. That is, the input for the regression model may be derived from pixel-based information from the satellite images (i.e., the remote sensing pictures). This deriving can be done using known methods and known vegetation indices, such as MSR_Red&Red_Edge may be employed. It is known that the Leaf Area Index can be derived from such vegetation indices. Different indices are conceivable. However, MSR_Red&Red_Edge is particularly suitable, as they yield very accurate predictions compared to other indices that can also be used, such as NDVI.

[0054] In this respect, as an example, a combination of six features may be used, wherein it is preferred that 5 spectral bands and one vegetation index, all of which can be derived from the satellite images, are used as features for the machine learning model, wherein Red Edge 2 (spectral band), Red Edge 3 (spectral band), Narrow NIR (spectral band), SWIR 1 (spectral band), SWIR 2 (spectral band), MSR_Red&Red_Edge (combined vegetation index) may be used as features, wherein the spectral bands may be defined as follows: Moreover, in this respect, it is further referred to the article ..Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 11. Issue: 5. May 2018).

[0055] The machine learning model with respect to the chlorophyll content data may be a XGBBOOST regression model based on satellite image analysis. In this respect, a combination of six features may be used, wherein it is preferred that 2 spectral bands and four vegetation indices are used as features for the machine learning model, wherein Red Edge 1 (spectral band). SWIR 2 (spectral band). NDWI (vegetation index; Normalized Difference Water Index). GNDVI (vegetation index; Green Normalized Difference Vegetation Index). REIP (vegetation index; Red Edge Inflection Point). Chlr_Red_Edge (combined vegetation index). Moreover, also in this respect, it is further referred to the article ..Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 11. Issue: 5. May 2018).

[0056] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the nitrogen uptake model is further based on a determined crop specific Chlorophyll-to-leaf nitrogen conversion factor. There is a relationship between the amount of chlorophyll and the nitrogen contained in the plant and / or plant part. By means of the conversion factor, an amount of chlorophyll may be converted into nitrogen amount. In this context, such a conversion factor may be determined crop-specifically, field-specifically and / or climate zone- specifically by means of respective field tests. The conversion factors may. for example, be determined for specific climatic regions by corresponding field trials, wherein field specific adjustments may be considered subsequently. Therefore, a crop specific and field specific Chlorophyll-to-leaf nitrogen conversion factor may be provided for each respective agricultural field. For example, a conversion factor of 250 in a moderate European climate zone for wheat was determined by respective field trails. This conversion factor may or may not be further refined on a field-specific level by means of respective field trials. As explained above, the method of the present disclosure comprises: determining stem weight data based on a provided crop specific leaf weight ratio; determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration; wherein providing nitrogen uptake data of plants and / or plant parts of the agricultural field is further based on the determined stem nitrogen data.

[0057] Optionally, stem weight data may also be determined by a plant-specific growth model. As input data for such a growth model, for example, plant data, weather data, climate data, etc. can be used. For example, such a plant-specific growth model was disclosed by Hunt et al. (“Effects of Nitrate Application on Amaranthus powellii Wats”, Plant Physiology, Volume 79, Issue 3, November 1985, Pages 619-624), which refers to Amaranthus powellii, a small grain crop, in particular known in the bioorganic segment and for cereals.

[0058] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the method further comprises: providing a nitrogen uptake map of the agricultural field based on the provided nitrogen uptake data, wherein the map preferably has a resolution between 1 and 50 m, 5 and 30 m, more preferably 10 m. In an example, the nitrogen uptake map reflects predicted N uptake in different color intensities from low to high and may provide a pixel based map with high granularity and / or a field zone based map, which spatially aggregate information into a number of zones which consider the fertilizer-spreader I machine- related restrictions in spatial resolution during the fertilizer application (e.g. as-applied map).

[0059] The unit of the uptake map is mass of N / unit area ground. The map provides spatially (geographic information system, GIS, related) resolved / explicit information.

[0060] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, method further comprises: providing crop specific nitrogen target value; providing nitrogen demand data based on the nitrogen uptake data and the nitrogen target value.

[0061] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the method further comprises: providing nitrogen demand map of the agricultural field based on the provided nitrogen demand data, wherein the map preferably has a resolution between 1 and 50 m, 5 and 30 m, more preferably 10 m.

[0062] Herein, nitrogen demand data, particularly a nitrogen demand, may be expressed as the difference between a target nitrogen uptake and an actual nitrogen uptake as determined as described herein. Alternatively, the nitrogen demand may be expressed as the actual nitrogen uptake divided by an optimal (i.e. target) nitrogen uptake. This is referred to as the Nitrogen Nutrition Index, NNI, which is known and established in agriculture as a metric for nitrogen demand.

[0063] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the method further comprises: providing control data for a fertilizer application device for variable applying a fertilizer product onto the agricultural field based on the nitrogen demand data.

[0064] In an embodiment of the computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the method further comprises: providing control data for an application device for variable applying an agricultural product onto the agricultural field based on the nitrogen demand data, wherein the agricultural product is: a crop protection product, a bio stimulant product, a growth regulator product and / or a desiccation product.

[0065] BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In the following, the present disclosure is further described with reference to the enclosed figures: Figure 1 illustrate example embodiments of a centralized and a decentralized computing environment with computing nodes;

[0067] Figure 2 illustrate example embodiments of a centralized and a decentralized computing environment with computing nodes;

[0068] Figure 3 illustrate an example embodiment of a distributed computing environment;

[0069] Figure 4 illustrates a flow diagram of a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field;

[0070] Figure 5 illustrates a system for providing nitrogen uptake data of plants and / or plant parts of an agricultural field;

[0071] Figure 6 is a further illustration of a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field;

[0072] Figure 7 shows an exemplary nitrogen uptake map of an agricultural field;

[0073] Figure 8 illustrates exemplarily the different possibilities to receive and process field data; and

[0074] Figure 9 illustrates an example of a leaf weight ratio (LWR) for different places and different years and its development over time.

[0075] DETAILED DESCRIPTION OF AN EMBODIMENT

[0076] The following embodiments are mere examples for implementing the method, the system, the apparatus, or application device disclosed herein and shall not be considered limiting. Figures 1 to 3 illustrate different computing environments, central, decentral and distributed. The methods, apparatuses, computer elements of this disclosure may be implemented in decentral or at least partially decentral computing environments. In particular, for data sharing or exchange in ecosystems of multiple players different challenges exist. Data sovereignty may be viewed as a core challenge. It can be defined as a natural person’s or corporate entity’s capability of being entirely self-determined with regard to its data. To enable this particular capability related aspects, including requirements for secure and trusted data exchange in business ecosystems, may be implemented across the chemical value chain. In particular, chemical industry requires tailored solutions to deliver chemical products in a more sustainable way by using digital ecosystems. Providing, determining or processing of data may be realized by different computing nodes, which may be implemented in a centralized, a decentralized or a distributed computing environment.

[0077] Figure 1 illustrates an example embodiment of a centralized computing system 20 comprising a central computing node 21 (filled circle in the middle) and several peripheral computing nodes 21.1 to 21. n (denoted as filled circles in the periphery). The term “computing system” is defined herein broadly as including one or more computing nodes, a system of nodes or combinations thereof. The term “computing node” is defined herein broadly and may refer to any device or system that includes at least one physical and tangible processor, and / or a physical and tangible memory capable of having thereon computer-executable instructions that are executed by a processor. Computing nodes are now increasingly taking a wide variety of forms. Computing nodes may, for example, be handheld devices, production facilities, sensors, monitoring systems, control systems, appliances, laptop computers, desktop computers, mainframes, data centers, or even devices that have not conventionally been considered a computing node, such as wearables (e.g., glasses, watches or the like). The memory may take any form and depends on the nature and form of the computing node.

[0078] In this example, the peripheral computing nodes 21.1 to 21. n may be connected to one central computing system (or server). In another example, the peripheral computing nodes 21.1 to 21. n may be attached to the central computing node via e.g. a terminal server (not shown). The majority of functions may be carried out by, or obtained from the central computing node (also called remote centralized location). One peripheral computing node 21. n has been expanded to provide an overview of the components present in the peripheral computing node. The central computing node 21 may comprise the same components as described in relation to the peripheral computing node 21 .n.

[0079] Each computing node 21 , 21.1 to 21. n may include at least one hardware processor 22 and memory 24. The term “processor” may refer to an arbitrary logic circuitry configured to perform basic operations of a computer or system, and / or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processor, or computer processor may be configured for processing basic instructions that drive the computer or system. It may be a semi-conductor based processor, a quantum processor, or any other type of processor configures for processing instructions. As an example, the processor may comprise at least one arithmetic logic unit ('ALLI"), at least one floatingpoint unit ("FPU)", such as a math coprocessor or a numeric coprocessor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an L1 and L2 cache memory. In particular, the processor may be a multicore processor. Specifically, the processor may be or may comprise a Central Processing Unit ("CPU"). The processor may be a (“GPU”) graphics processing unit, (“TPU”) tensor processing unit, ("CISC") Complex Instruction Set Computing microprocessor, Reduced Instruction Set Computing ("RISC") microprocessor, Very Long Instruction Word ("VLIW") microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing means may also be one or more special-purpose processing devices such as an Application-Specific Integrated Circuit ("ASIC"), a Field Programmable Gate Array ("FPGA"), a Complex Programmable Logic Device ("CPLD"), a Digital Signal Processor ("DSP"), a network processor, or the like. The methods, systems and devices described herein may be implemented as software in a DSP, in a micro-controller, or in any other side-processor or as hardware circuit within an ASIC, CPLD, or FPGA. It is to be understood that the term processor may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified. The memory 24 may refer to a physical system memory, which may be volatile, nonvolatile, or a combination thereof. The memory may include non-volatile mass storage such as physical storage media. The memory may be a computer-readable storage media such as RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, non-magnetic disk storage such as solid- state disk or any other physical and tangible storage medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by the computing system. Moreover, the memory may be a computer-readable media that carries computer- executable instructions (also called transmission media). Further, upon reaching various computing system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computing system RAM and / or to less volatile storage media at a computing system. Thus, it should be understood that storage media can be included in computing components that also (or even primarily) utilize transmission media.

[0080] The computing nodes 21 , 21.1 to 21. n may include multiple structures 26 often referred to as an “executable component, executable instructions, computer-executable instructions or instructions”. For instance, memory 24 of the computing nodes 21 , 21.1 to 21. n may be illustrated as including executable component 26. The term “executable component” or any equivalent thereof may be the name for a structure that is well understood to one of ordinary skill in the art in the field of computing as being a structure that can be software, hardware, or a combination thereof or which can be implemented in software, hardware, or a combination. For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component includes software objects, routines, methods, and so forth, that is executed on the computing nodes 21 , 21.1 to 21 .n, whether such an executable component exists in the heap of a computing node 21 , 21 .1 to 21 .n, or whether the executable component exists on computer-readable storage media. In such a case, one of ordinary skill in the art will recognize that the structure of the executable component exists on a computer- readable medium such that, when interpreted by one or more processors of a computing node 21 , 21.1 to 21. n (e.g., by a processor thread), the computing node 21 , 21.1 to 21 n is caused to perform a function. Such a structure may be computer-readable directly by the processors (as is the case if the executable component were binary). Alternatively, the structure may be structured to be interpretable and / or compiled (whether in a single stage or in multiple stages) so as to generate such binary that is directly interpretable by the processors. Such an understanding of example structures of an executable component is well within the understanding of one of ordinary skill in the art of computing when using the term “executable component”. Examples of executable components implemented in hardware include hardcoded or hard-wired logic gates, that are implemented exclusively or near-exclusively in hardware, such as within a field- programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other specialized circuit. In this description, the terms “component”, “agent”, “manager”, “service”, “engine”, “module”, “virtual machine” or the like are used synonymous with the term “executable component.

[0081] The processor 22 of each computing node 21 , 21.1 to 21. n may direct the operation of each computing node 21 , 21.1 to 21. n in response to having executed computerexecutable instructions that constitute an executable component. For example, such computer-executable instructions may be embodied on one or more computer-readable media that form a computer program product. The computer-executable instructions may be stored in the memory 24 of each computing node 21 , 21.1 to 21. n. Computerexecutable instructions comprise, for example, instructions and data which, when executed at a processor 21 , cause a general purpose computing node 21 , 21.1 to 21. n, special purpose computing node 21 , 21.1 to 21. n, or special purpose processing device to perform a certain function or group of functions. Alternatively or in addition, the computer-executable instructions may configure the computing node 21 , 21.1 to 21. n to perform a certain function or group of functions. The computer executable instructions may be, for example, binaries or even instructions that undergo some translation (such as compilation) before direct execution by the processors, such as intermediate format instructions such as assembly language, or even source code. Each computing node 21 , 21 .1 to 21 .n may contain communication channels 28 that allow each computing node 21.1 to 21. n to communicate with the central computing node 21 , for example, a network (depicted as solid line between peripheral computing nodes and the central computing node in Figure 1). A “network” may be defined as one or more data links that enable the transport of electronic data between computing nodes 21 , 21.1 to 21. n and / or modules and / or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computing node 21 , 21.1 to 21.n, the computing node 21 , 21.1 to 21. n properly views the connection as a transmission medium. Transmission media can include a network and / or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or special-purpose computing nodes 21 , 21 .1 to 21 .n. Combinations of the above may also be included within the scope of computer-readable media.

[0082] The computing node(s) 21 , 21.1 to 21 .n may further comprise a user interface system 25 for use in interfacing with a user. The user interface system 25 may include output mechanisms 25A as well as input mechanisms 25B. The principles described herein are not limited to the precise output mechanisms 25A or input mechanisms 25B as such will depend on the nature of the device. However, output mechanisms 25A might include, for instance, displays, speakers, displays, tactile output, holograms and so forth. Examples of input mechanisms 25B might include, for instance, microphones, touchscreens, holograms, cameras, keyboards, mouse or other pointer input, sensors of any type, and so forth.

[0083] Figure 2 illustrates an example embodiment of a decentralized computing environment 30 with several computing nodes 21.1 to 21 .n denoted as filled circles. In contrast to the centralized computing environment 20 illustrated in Figure 1 , the computing nodes 21.1 to 21. n of the decentralized computing environment are not connected to a central computing node 21 and are thus not under control of a central computing node. Instead, resources, both hardware and software, may be allocated to each individual computing node 21.1 to 21 .n (local or remote computing system) and data may be distributed among various computing nodes 21.1 to 21. n to perform the tasks. Thus, in a decentral system environment, program modules may be located in both local and remote memory storage devices. One computing node 21 has been expanded to provide an overview of the components present in the computing node 21. In this example, the computing node 21 comprises the same components as described in relation to Figure 1.

[0084] Figure 3 illustrates an example embodiment of a distributed computing environment 40. In this description, “distributed computing” may refer to any computing that utilizes multiple computing resources. Such use may be realized through virtualization of physical computing resources. One example of distributed computing is cloud computing. “Cloud computing” may refer a model for enabling on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). When distributed, cloud computing environments may be distributed internationally within an organization and / or across multiple organizations. In this example, the distributed cloud computing environment 40 may contain the following computing resources: mobile device(s) 42, applications 43, databases 44, data storage and server(s) 46. The cloud computing environment 40 may be deployed as public cloud 47, private cloud 48 or hybrid cloud 49. A private cloud 47 may be owned by an organization and only the members of the organization with proper access can use the private cloud 48, rendering the data in the private cloud at least confidential. In contrast, data stored in a public cloud 48 may be open to anyone over the internet. The hybrid cloud 49 may be a combination of both private and public clouds 47, 48 and may allow to keep some of the data confidential while other data may be publicly available.

[0085] Figure 4 illustrates a flow diagram of a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field. In a first step 100, leaf area index (LAI) data for the agricultural field are provided. In a further step 110, chlorophyll content data for the agricultural field are provided. In a further step 120, a nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data is provided. In a further step 130, nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model are provided. Figure 5 illustrates a system 10 for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, comprising: a providing unit 11 configured to provide leaf area index data for the agricultural field; a further providing unit 12 configured to provide chlorophyll content data for the agricultural field; a further providing unit 13 configured to provide an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data; and a further providing unit 14 configured to provide nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model.

[0086] Figure 6 is a further illustration of a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field. In a first step, satellite data / images 50 of an agricultural field may be provided. These satellite data are provided to the leaf area and chlorophyll content model 51 , which may be provided by at least one machine learning model. The machine learning model with respect to the leaf area index data 52 may be a XGBBOOST regression model based on satellite image analysis. In this respect, a combination of six features may be used, wherein it is preferred that 5 spectral bands and one vegetation index are used as features for the machine learning model, wherein Red Edge 2 (spectral band), Red Edge 3 (spectral band), Narrow NIR (spectral band), SWIR 1 (spectral band), SWIR 2 (spectral band), MSR_Red&Red_Edge (combined vegetation index) may be used as features. The machine learning model with respect to the chlorophyll content data 53 may also be a XGBBOOST regression model based on satellite image analysis. In this respect, a combination of six features may be used, wherein it is preferred that 2 spectral bands and four vegetation indices are used as features for the machine learning model, wherein Red Edge 1 (spectral band), SWIR 2 (spectral band), NDWI (vegetation index; Normalized Difference Water Index), GNDVI (vegetation index; Green Normalized Difference Vegetation Index), REIP (vegetation index; Red Edge Inflection Point), Chlr_Red_Edge (combined vegetation index). In this respect, it is further referred to the article ..Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Volume: 11 , Issue: 5, May 2018). The leaf area index data and the chlorophyll content data for the agricultural field is obtained by utilizing the leaf area and chlorophyll content model 51 .

[0087] In the illustrated example, a leaf area index of 1 .6 m2leaf per m2ground and a leaf chlorophyll concentration of 50 pg Chi per cm2leaf have been determined based on the satellite data / images. Both values may be used to determ ine / calculate the leaf nitrogen uptake 54. For the illustrated example, a leaf nitrogen uptake of 52 kg nitrogen per ha may be derived.

[0088] As explained above, according to the present disclosure, the stem nitrogen data, e.g. a stem nitrogen content, which is used for determining the nitrogen uptake, is determined based on the leaf weight ratio, specifically, stem weight data derived from the leaf weight ratio.

[0089] As an example, the stem nitrogen content may be estimated as follows: In a first step, the stem weight may be estimated based on a provided crop specific leaf weight ratio, i.e. the leaf area index may be converted into stem weight based on empirical values and / or experimental values. Subsequently, the stem weight may be converted into a crop specific stem nitrogen content considering a crop specific stem nitrogen concentration, which in turn may be derived by means of empirical values and / or experimental values.

[0090] Alternatively or in addition and as shown in Figure 6, stem weight data may also be determined by a plant-specific growth model 55. As input data for such a growth model, for example, weather data 56, soil data 57, etc. may be used. From the stem weight data obtained by model 55, the stem nitrogen uptake can be determined as explained above. For the illustrated example, a stem nitrogen uptake of 26 kg nitrogen per ha may be derived. As a result, for the illustrated example, a crop nitrogen uptake (“Crop N” 58) of 78 kg nitrogen per ha may be derived, which is the sum of the leaf nitrogen uptake and stem nitrogen uptake outlined above.

[0091] Here, reference is also already made Figure 9, discussed in detail below, which is an illustration that shows that the leaf weight ratio may be time-dependent and locationdependent. A leaf weight ratio for the time at which the satellite images are obtained may thus be determined, for the crop that is depicted by the satellite images, based on a lookup or based on the above-described growth model. To that end, acquisition time of the satellite images may be retrieved, e.g. from a time stamp of the images or a storage. Thus, it will be understood that the overall modelling entails bringing together determination of LAR 52 and chlorophyll content data 53, for example determined by a model 51 and derived from satellite images that provide input data for the model 51 , and stem weight data derived from the (time-dependent) LWR value, which in turn can be derived from a (growth) model 55 or may be looked up. As such, within the overall nitrogen uptake modelling, optionally models may be uses for different sub-steps. Alternatively to one model 51 , multiple models may be used for the respective determination of LAR and chlorophyll content data.

[0092] In Figure 7 an exemplary nitrogen uptake map of an agricultural field is shown, wherein the crop nitrogen uptake is derived as explained above. For example, such a plantspecific growth model was disclosed by Hunt et al. (“Effects of Nitrate Application on Amaranthus powellii Wats”, Plant Physiology, Volume 79, Issue 3, November 1985, Pages 619-624), which refers to Amaranthus powellii, a small grain crop, in particular known in the bioorganic segment and for cereals.

[0093] Figure 8 illustrates exemplarily the different possibilities to receive and process field data (e.g. image data, control data, etc.). For example, field data can be obtained by all kinds of agricultural equipment 300 (e.g. a tractor 300) as so-called as-applied maps by recording the application rate at the time of application. It is also possible that such agricultural equipment comprises sensors (e.g. optical sensors, cameras, infrared sensors, soil sensors, etc.) to provide, for example, a weed distribution map. It is also possible that during harvesting the yield (e.g. in the form of biomass) is recorded by a harvesting vehicle 310. Furthermore, corresponding maps / data can be provided by land- based and / or airborne drones 320 by taking images of the field or a part of it. Finally, it is also possible that a geo-referenced visual assessment 330 is performed and that this field data is also processed. Field data collected in this way can then be merged in a computing device 340, where the data can be transmitted and computed, for example, via any wireless link, cloud applications 350 and / or working platforms 360, wherein the field data may also be processed in whole or in part in the cloud application 350 and / or in the working platform 360 (e.g., by cloud computing). Figure 9 illustrates an example of a leaf weight ratio (LWR) for different places and different years and its development over time, here as a function time, such as, for example, of day of year (DOC). More specifically, Figure 9 illustrates, for two years (here for example 2021 and 2022 for illustrative purposes only) annual and spatial effects on seasonal dynamics of LWR for two specific geographic positions of fields (denoted to as Loc_1 and Loc_2). The x-axis relates to the time, in this example the time of year, for example Day Of Year (DOY), and the y-axis to the LWR.

[0094] To further emphasize and illustrate the role of the LWR, below the method of the present disclosure is reiterated:

[0095] As explained above, the present disclosure provides a computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field is disclosed, comprising: providing leaf area index data for the agricultural field; providing chlorophyll content data for the agricultural field; providing an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data; providing nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model. The method further comprises determining stem weight data based on a provided crop specific leaf weight ratio; determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration. According to the present disclosure, providing nitrogen uptake data of plants and / or plant parts of the agricultural field is further based on the determined stem nitrogen data.

[0096] The use of the leaf weight ratio in determining the nitrogen uptake allows for higher precision determinations of nitrogen uptake.

[0097] A (crop) model configured to provide a site-specific crop organ allocation coefficient, such as leaf-weight ratio (LWR) may be employed to obtain the LWR. LWR, provided by a model, is affected by geographic position, weather conditions, internal logics of the model and agricultural practices, such as sowing date and variety. It will be understood from the above that the present disclosure may provide a (nitrogen uptake) model that provides, as an intermediate step, data indicative of dry mass of one or more crop organs (e.g., at least one of root mass data, stem weight data, leave mass data, seeds mass data and fruits mass data) at a given time, e.g. the time when the satellite images are acquired, based on a dynamic biomass allocation coefficient (such as LWR and / or root-to-shoot and / or stem-to-shoot), particularly on a daily basis throughout the season. The dry mass, particularly the value of the LWR at that time, may be further used for predicting the nitrogen uptake.

[0098] More specifically, a model, such as a crop growth model. May provide the LWR and may provide an optimal N uptake (in g N per m2ground). Using data derived from the remote sensing, such as the satellite images, and data derived from the crop growth model, the nitrogen uptake model provides an actual N uptake (in g N per m2 ground).

[0099] As an example, when determining, for a given time, e.g. for a given day, the nitrogen uptake using the nitrogen uptake models, satellite images acquired at said time may be retrieved. The time at which the satellite images were acquired may be determined or retrieved, e.g. from a timestamp or database. Using said time and the time-dependency of the LWR value, the value of the LWR at said time can be determined. For example, the satellite image may be taken at a specific day and the LWR value for said day may be determined based on the time-dependency. An example of such a time dependency is illustrated in Fig. 9.

[0100] The present disclosure may provide a (nitrogen uptake) model that is a process-based model and may take, as input data, at least one of crop type, variety, seeding I planting date, and site-specific soil and weather data, particularly for use by the crop growth model that predicts the LWR.

[0101] Aspects of the present disclosure relates to computer program elements configured to carry out steps of the methods described above. The computer program element might therefore be stored on a computing unit of a computing device, which might also be part of an embodiment. This computing unit may be configured to perform or induce performing of the steps of the method described above. Moreover, it may be configured to operate the components of the above described system. The computing unit can be configured to operate automatically and / or to execute the orders of a user. The computing unit may include a data processor. A computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to carry out the method according to one of the preceding embodiments. This exemplary embodiment of the present disclosure covers both, a computer program that right from the beginning uses the present disclosure and computer program that by means of an update turns an existing program into a program that uses the present disclosure. Moreover, the computer program element might be able to provide all necessary steps to fulfill the procedure of an exemplary embodiment of the method as described above. According to a further exemplary embodiment of the present disclosure, a computer readable medium, such as a CD-ROM, USB stick, a downloadable executable or the like, is presented wherein the computer readable medium has a computer program element stored on it which computer program element is described by the preceding section. A computer program may be stored and / or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. However, the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network. According to a further exemplary embodiment of the present disclosure, a medium for making a computer program element available for downloading is provided, which computer program element is arranged to perform a method according to one of the previously described embodiments of the present disclosure.

[0102] The present disclosure has been described in conjunction with a preferred embodiment as examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims. Notably, in particular, the any steps presented can be performed in any order, i.e. the present invention is not limited to a specific order of these steps. Moreover, it is also not required that the different steps are performed at a certain place or at one node of a distributed system, i.e. each of the steps may be performed at a different nodes using different equipment / data processing units. In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.

Claims

Claims1. Computer-implemented method for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, comprising: providing (100) leaf area index data for the agricultural field; providing (110) chlorophyll content data for the agricultural field; providing (120) a nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts, wherein the input data of the nitrogen uptake model are the provided leaf area index data and the chlorophyll content data; providing (130) nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model, wherein the method comprises: determining stem weight data based on a provided crop specific leaf weight ratio; determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration; wherein providing nitrogen uptake data of plants and / or plant parts of the agricultural field is further based on the determined stem nitrogen data.

2. Computer-implemented method according to claim 1 , wherein the crop specific leaf weight ratio is used as an input for the nitrogen uptake model or determined by a growth model as part of the nitrogen uptake model.

3. Computer-implemented method according to claim 1 or 2, wherein the chlorophyll content data is provided as leaf chlorophyll content (LC) data and / or canopy chlorophyll content (CCHL) data.

4. Computer-implemented method according to any of the preceding claims, wherein the leaf area index data and the chlorophyll content data for the agricultural field are obtained by using a leaf area and chlorophyll content model (51 ) configured to provide leaf area index data and chlorophyll content data based on surface reflectance bands of at least one satellite image, wherein the leaf area andchlorophyll content model is preferably adapted to different crop varieties, crop types, growth stage, soil conditions and / or data sources.

5. Computer-implemented method according to claim 4, wherein the leaf area and chlorophyll content model is a machine learning model and the leaf area index data and the chlorophyll content data for the agricultural field is obtained by utilizing the leaf area and chlorophyll content model, wherein the machine learning model is preferably: an artificial neural network (ANN), multiple linear regression, random forest regression, or an approach that is able to establish a statistical relationship to predict leaf area index data and chlorophyll content data.

6. Computer-implemented method according to any one of the preceding claims, wherein the nitrogen uptake model is further based on a determined crop specific Chlorophyll-to-leaf nitrogen conversion factor.

7. Computer-implemented method according to any one of the preceding claims, wherein the method further comprises: providing a nitrogen uptake map of the agricultural field based on the provided nitrogen uptake data, wherein the map preferably has a resolution between 1 and 50 m, preferably 5 and 30 m, more preferably 10 m.

8. Computer-implemented method according to any one of the preceding claims, wherein the method further comprises: providing crop specific nitrogen target value; providing nitrogen demand data based on the nitrogen uptake data and the nitrogen target value.

9. Computer-implemented method according to claim 8, wherein the method further comprises: providing nitrogen demand map of the agricultural field based on the provided nitrogen demand data, wherein the map preferably has a resolution between 1 and 50 m, preferably 5 and 30 m, more preferably 10 m.

10. Computer-implemented method according to claim 8 or claim 9, wherein the method further comprises: providing control data for a fertilizer application device for variable applying a fertilizer product onto the agricultural field based on the nitrogen demand data.

11. Computer-implemented method according to any one of claims 8 to claim 10, wherein the method further comprises: providing control data for an application device for variable applying an agricultural product onto the agricultural field based on the nitrogen demand data, wherein the agricultural product is: a crop protection product, a bio stimulant product, a growth regulator product and / or a desiccation product.

12. Apparatus for providing nitrogen uptake data of plants and / or plant parts of an agricultural field, the apparatus comprising: one or more computing nodes; and one or more computer-readable media having thereon computer-executable instructions that are structured such that, when executed by the one or more computing nodes, cause the apparatus to perform the following steps: providing leaf area index data for the agricultural field; providing chlorophyll content data for the agricultural field; providing an nitrogen uptake model configured to provide nitrogen uptake data of plants and / or plant parts based on leaf area index data and chlorophyll content data; determining stem weight data based on a provided crop specific leaf weight ratio; determining stem nitrogen data based on the stem weight data and a provided crop specific stem nitrogen concentration; providing nitrogen uptake data of plants and / or plant parts of the agricultural field based on the provided leaf area index data and chlorophyll content data utilizing the nitrogen uptake model, wherein providing nitrogen uptake data of plants and / or plant parts of the agricultural field is further based on the determined stem nitrogen data.

13. Fertilizer application device for applying a fertilizer product on an agricultural field, wherein the control data for the fertilizer application device are at least partially provided according to claim 10 or claim 11 .

14. Computer program element with instructions, which, when executed on computing devices of a computing environment, is configured to carry out the steps of the computer-implemented method according to any one of the claims 1 to 11 , and / or in an apparatus according to claim 12.

15. Use of leaf area index data, chlorophyll content data, nitrogen uptake model and / or satellite images in a computer-implemented method according to any one of the claims 1 to 11 and / or in an apparatus according to claim 12.

Images