DECISION SYSTEM FOR CROP EFFICIENCY PRODUCT APPLICATION USING REMOTE SENSING BASED SOIL PARAMETERS

Abstract

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

Claims

1. A computer-implemented method for applying a crop efficiency product to at least one crop in a field, the method comprising: collecting (S10), by a data interface (110), remotely-sensed data of the field before an application of the crop efficiency product in the field; determining (S20), by a parameter determination unit (120), based on the collected remotely-sensed data, at least one soil parameter at a plurality of locations in the field; generating (S30), by a yield prediction unit (130), for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product; and deciding (S40), by a decision unit (140), for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response, and outputting information indicative of the decision useable to activate at least one treatment device to comply with the decision.

2. The method according to claim 1, further comprising: controlling (S50), by a controlling unit (150), at least one treatment device to comply with the decision based on the outputted information.

3. The method according to claim 1, wherein the at least one soil parameter comprises at least one of the following: a soil moisture, preferably measured at a sub-field resolution in a timeframe in days before the application of the crop efficiency product; and/or a soil surface temperature, preferably measured during a particular time period, preferably during winter.

4. The method according to claim 3, wherein the soil surface temperature is predicted by weather forecast data.

5. The method according to claim 1, wherein determining (S20) at least one soil parameter at a plurality of locations in the field further comprises: determining (S21), based on the collected remotely-sensed data, at least one vegetation parameter, preferably measured at a sub-field level resolution; and wherein generating (S30) a predicted yield response to the application of the crop efficiency product further comprises: generating (S31), for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter, the at least one vegetation parameter, and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter, different values of the at least one vegetation parameter, and associated yield responses for the at least one crop under the application of the crop efficiency product.

6. The method according to claim 1, wherein deciding (S40), for each of the plurality of locations, whether to treat or not, further comprises: evaluating (S41), based on the predicted yield response, whether a treatment i) deteriorates a growth of the at least one crop, ii) does not affect the growth of the at least one crop, or iii) improves the growth of the at least one crop; determining (S42), for each of the plurality of locations, whether the predicted yield response is above a positive reference value; and deciding (S43), for each of the plurality of locations, whether to treat or not based on the determination result.

7. The method according to claim 1, wherein deciding (S40), for each of the plurality of locations, whether to treat or not, further comprises: deciding (S44) on a dose of the crop efficiency product to be applied for each of the plurality locations.

8. The method according to claim 7, wherein the dose of the crop efficiency product is decided based on at least one of the following factors at each of the plurality of locations: a leaf area index; a biomass; or a stress level.

9. The method according to claim 1, wherein controlling (S50) at least one treatment device to comply with the decision is conducted based on: i) a generation of an application map indicative of the decision, for each of the plurality of locations, whether to treat or not, and a delivery of the application map to the at least one treatment device; and/or ii) an algorithm embedded on the at least one treatment device adapted for being run in real time for the location the at least one treatment device passes.

10. A decision-support (100) system for controlling a treatment device for applying a crop efficiency product to at least one crop in a field, the decision-support system comprising: a data interface (110); a parameter determination unit (120); a yield prediction unit (130); a decision unit (140); a controlling unit (150); and a treatment control interface (160); wherein the parameter determination unit is configured to determine, from remotely-sensed data received from the data interface, at least one soil parameter at a plurality of locations in the field; wherein the yield prediction unit is configured to generate, at each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product; wherein the decision unit is configured to decide, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response; and wherein the controlling unit is configured to generate a treatment control signal comprising information indicative of the decision and to output the treatment control signal to the treatment control interface, which when transmitted causes an activation of at least one treatment device to comply with the decision.

11. The decision-support system according to claim 10, wherein the parameter determination unit is further configured to determine, from the received remotely-sensed data, at least one vegetation parameter, preferably measured at a sub-field level resolution; and wherein the yield prediction unit is configured to generate, at each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter, the at least one determined vegetation parameter, and a prediction model, wherein the prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter, different values of the at least one vegetation parameter, and associated yield responses for the at least one crop under the application of the crop efficiency product.

12. The decision-support system according to claim 10, wherein the decision unit is further configured to decide on a dose of the crop efficiency product to be applied for each of the plurality of locations.

13. A treatment device (200) for applying a crop efficiency product to at least one crop in a field, comprising: a treatment control interface (260); a treatment controlling unit (210); and a treatment arrangement (220) with one or a plurality of treatment units (221, 222, 223, 224); wherein the treatment control interface of the treatment device is connectable to the treatment control interface of the decision-support system according to claim 10 to receive a treatment control signal; and wherein the treatment controlling unit is configured to regulate respective ones of treatment units of the treatment arrangement to apply the crop efficiency product at respective locations based on the received treatment control signal.

14. The treatment device according to claim 13, wherein the treatment controlling unit is configured to run an algorithm embedded on the treatment controlling device in real time for a location the treatment device passes based on the treatment control signal.

15. A system (300) for applying a crop efficiency product to at least one crop in a field, comprising: a remote sensing device (50); a decision-support system according to claim 10; and at least one treatment device; wherein the remote sensing device is configured to collect remotely-sensed data of the field; wherein the decision-support system is configured to decide, based on the collected remotely-sensed data of the field, whether to treat or not, and preferably, to decide on a dose of the crop efficiency product to be applied for each of a plurality of locations in the field; and wherein the at least one treatment device is configured to be controlled by the decision-support system to comply with the decision.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Exemplary embodiments of the invention will be described in the following with reference to the following drawings:

[0042] FIG. 1 shows a schematic drawing of a method according to an exemplary embodiment of the present disclosure.

[0043] FIG. 2 shows a schematic drawing of a field according to an exemplary embodiment of the present disclosure.

[0044] FIG. 3 shows a schematic drawing of a decision-support system according to an exemplary embodiment of the present disclosure.

[0045] FIG. 4 shows a schematic drawing of a treatment device according to an exemplary embodiment of the present disclosure.

[0046] FIG. 5 shows a schematic drawing of a system according to an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION OF DRAWINGS

[0047] FIG. 1 shows a block diagram of an embodiment of a computer-implemented method for applying a crop efficiency product in a field 10. An example of the field 10 is illustrated in FIG. 2. In step S10, remotely-sensed data of the field may be collected before an application of the crop efficiency product in the field. The remotely-sensed data may be collected using satellite, drone, or radar platforms. To collect the remotely-sensed data, drones may be fitted with visual, IR, NIR, and/or thermal sensors. In another example, passive or active remote sensing of radar rays may be used to collect remotely-sensed data.

[0048] In step S20, at least one soil parameter at a plurality of locations in the field is determined based on the collected remotely-sensed data. For example, as illustrated in FIG. 2, the field 10 is divided into a plurality of grids in form of a rectangular array of squares 12a, 12b, 12c of equal size. The at least one soil parameter may be determined at the plurality of locations, e.g. at the plurality of squares 12a, 12b, 12c.

[0049] The soil parameter may include e.g. pH, electrical conductivity, texture, moisture, temperature, soil organic matter, available nitrogen, phosphorus and/or potassium. The soil parameters can be determined by several methods. For example, passive or active remote sensing of radar rays reflected in the soil can be used to estimate close to surface moisture, e.g. 3 to 10 cm, and surface temperature of the soil and crop. In another example, drones may be fitted with an IR camera for detecting heat signatures of soils, which allows obtaining a map depicting soil heat and moisture variations.

[0050] Preferably, the at least one soil parameter may comprise a soil moisture. Preferably, the soil moisture may be measured at sub-field resolution in a timeframe in days before the application of the crop efficiency product. Crop efficiency products may influence the crops reaction and memory to drought stress later in season (greening effect). Soil water content does indicate how much water- and heat stress a plant suffers. The soil moisture is preferably measured at a sub-field resolution of around 100 m.

[0051] Preferably, the at least one soil parameter may comprise a soil surface temperature. Preferably, the soil surface temperature may be measured during a particular time period, preferably during winter, e.g. in February and March. Winter conditions define the survival rate of spores from previous season, leading to a better yield response if more spores have survived winter. It is believed that spore survival rate correlates with soil moisture and land temperature during winter. Additionally or alternatively, the soil surface temperature may be predicted by whether forecast data. In this way, it is not required to perform the in-season measurements.

[0052] In step S30, it is generated, for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model. The prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product. For example, as illustrated in FIG. 2, a predicted yield response may be calculated for each squares 12a, 12b, 12c. The plain squares 12a may denote locations with negative predicted yield responses. The patterned squares 12b may denote locations with predicted yield responses of a low positive value. The patterned squares 12 c may denote locations with predicted yield responses of a positive value above a reference value.

[0053] In other words, previous data and current measurements of soil parameters may serve in yield forecasting. In an example, the yield prediction model is a machine learning model. Machine learning algorithms build a mathematical model of training data from field trials, in order to make predictions or decisions based on the at least one determined soil parameter without being explicitly programmed to perform the task. In another example, the yield prediction model comprises a mathematical equation for correlating the predicted yield response with the at least one determined soil parameter.

[0054] In step S40, it is decided, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response. Information indicative of the decision is outputted useable to activate at least one treatment device to comply with the decision. For example, a positive predicted yield response at a location may indicate that the location is worth to be treated, whereas a negative predicted yield response at a location may indicate that the location is not worth to be treated. For example, as illustrated in FIG. 2, the patterned squares 12b and 12c have a positive yield response and thus may be worth to be treated. On the other hand, the plain squares 12a may have a negative yield response and thus may not be worth to be treated. In optional step S41, it is evaluated, based on the predicted yield response, whether a treatment i) deteriorates a growth of the at least one crop, ii) does not affect the growth of the at least one crop, or iii) improves the growth of the at least one crop. For example, as illustrated in FIG. 2, the patterned squares 12b and 12c have a positive yield response and thus improve the growth of the at least one crop. On the other hand, the plain squares 12a have a negative yield response and thus may deteriorate the growth of the at least one crop.

[0055] In optional step S42, it is determined, for each of the plurality of locations, whether the predicted yield response is above a positive reference value. As discussed above, the patterned squares 12b and 12c have a positive yield response and thus improve the growth of the at least one crop. However, the patterned squares 12b denote locations with predicted yield responses of a low positive value. In other words, although positive yield responses can be seen for these locations, the positive return on the investment are relatively low. Thus, it may not be reasonable to apply the crop efficiency product to these locations. On the other hand, the patterned squares 12c denote locations with predicted yield responses of a positive value above a reference value. Thus, a more positive return on investment can be seen for these locations. It may be reasonable to apply the crop efficiency product to the locations denoted with patterned squares 12c.

[0056] In optional step S43, it is decided, for each of the plurality of locations, whether to treat or not based on the determination result. Thus, the crop efficiency product is applied to the locations denoted with patterned squares 12c.

[0057] In optional step S50, at least one treatment device is controlled to comply with the decision based on the outputted information. The at least one treatment device may include a common sprayer or a crop duster, such as an airplane spraying chemicals. For example, the at least one treatment device may be controlled to apply the crop efficiency product only at locations denoted with patterned squares 12c.

[0058] Controlling at least one treatment device to comply with the decision may be conducted based on a generation of an application map indicative of the decision, for each of the plurality of locations, whether it is worth to treat or not, and a delivery of the application map to the at least one treatment device. For example, as illustrated in FIG. 2, each square 12a, 12b, 12c in the field 10 may be provided with a GPS coordinate. The squares 12a, 12b, 12c and the corresponding GPS coordinates may form an application map, which can guide GPS-guided ground robots or GPS-guided aerial sprayers to apply the crop efficiency product at the desired locations, e.g. locations denoted with patterned squares 12c. Alternatively or additionally, an algorithm embedded on the at least one treatment device may be run in real time for the location the at least one treatment device passes such that the at least one treatment device is controlled to comply with the decision.

[0059] Optionally, determining S20 at least one soil parameter at a plurality of locations in the field further comprises the step of determining S21, based on the collected remotely-sensed data, at least one vegetation parameter, preferably measured at a sub-field level resolution. The vegetation parameter may comprise SPI, VOD, NDVI, and/or EVI. The vegetation parameter may be obtained by analysing the spectral signatures of the crop and soil in the image data collected using optical remote sensing techniques. Generating S30 a predicted yield response to the application of the crop efficiency product further comprises the step of generating S31, for each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter, the at least one vegetation parameter, and a prediction model. The prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter, different values of the at least one vegetation parameter, and associated yield responses for the at least one crop under the application of the crop efficiency product. In other words, an additional parameter, i.e. at least one vegetation parameter, may be provided as a further parameter input for the prediction model, such as a machine learning model or a mathematical equation. This additional parameter may increase the accuracy in predicting the yield response to the application of the crop efficiency product.

[0060] Optionally, deciding S40, for each of the plurality of locations, whether to treat or not, further comprises the step of deciding S44 on a dose of the crop efficiency product to be applied for each of the plurality locations. The dose of the crop efficiency product is decided based on at least one of the following factors at each of the plurality of locations: a leaf area index, a biomass, and a stress level. For example, lower biomass zones may be applied with the crop efficiency product with a lower dose. For example, if a non-linear yield response to these factors is assumed, a lower dose of the crop efficiency product may be applied to a lower stress zone, whereas a higher dose may be applied to a higher stress zone.

[0061] FIG. 3 schematically shows an embodiment of a decision-support system 100 for controlling a treatment device for applying a crop efficiency product in a field. An example of the decision-support system 100 in form of a computer system is illustrated in FIG. 2. The decision-support system 100 may be a remote server that provides a remote service e.g. via internet, to facilitate upload and management of remotely-sensed data from many different plantation fields collected by the farmers. The remote server may include an interface through which a user (e.g. a farmer) can manage the remotely-sensed data and related information. For example, the decision-support system 100 may interface with users with webpages served by the decision-support system to facilitate the management of the remotely-sensed data and related decisions. The related decision may include e.g. one or more target areas to be treated, an optimum dose of the crop efficiency product to be applied for these areas, etc. The related decision may be part of configuration data, which may be loaded onto the one or more treatment devices in the plantation field e.g. via a network, to enable the one or more treatment devices to perform a treatment on the target areas. Alternatively, the decision-support system 100 may be a local computing device, such as a personal computer (PC).

[0062] The decision-support system 100 comprises a data interface 110, a parameter determination unit 120, a yield prediction unit 130, a decision unit 140, a controlling unit 150, and a treatment control interface 160.

[0063] The data interface 110 may be a secure digital (SD) memory card interface, a universal serial bus (USB) interface, a Bluetooth interface, a wireless network interface, etc. suitable to receive the remotely-data collected using satellite, radar or drone platforms. The remotely-sensed data may comprise radar image data or optical image data. The remotely-sensed data may also comprise GPS data adapted for providing guidance of the treatment devices to the target areas.

[0064] The parameter determination unit 120 is configured to determine, based on remotely-sensed data received from the data interface, at least one soil parameter at a plurality of locations in the field. The at least one soil parameter may comprise a soil temperature and/or a soil moisture. A variety of remote sensing techniques for soil moisture retrieval may be used based on their different electromagnetic spectrum. In an example, if active remote sensing of radar rays is used, the soil moisture or soil surface temperature may be determined from the remotely-sensed data based on backscatter coefficient and dielectric properties. In another example, if visible sensors are used, soil moisture and soil surface temperature may be determined from the remotely-sensed data based on soil albedo index of refraction. In a further example, if thermal infrared sensors are used, soil moisture may be determined from the remotely-sensed data by measuring soil surface temperature.

[0065] Optionally, the parameter determination unit 120 is further configured to determine, from the received remotely-sensed data, at least one vegetation parameter, such as SPI, VOD, NDVI, and/or EVI, preferably measured at a sub-field level resolution.

[0066] The yield response unit 130 is configured to generate, at each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter and a prediction model. The prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter and associated yield responses for the at least one crop under the application of the crop efficiency product. In an example, the yield prediction model is a machine learning model using training data from field trials. In another example, the yield prediction model is a mathematical equation for correlating the predicted yield response with the at least one soil parameter. Optionally, the yield prediction unit 130 is configured to generate, at each of the plurality of locations, a predicted yield response to the application of the crop efficiency product for the at least one crop based on the at least one determined soil parameter, the at least one determined vegetation parameter, and a prediction model. The prediction model is parametrized or trained based on a sample set including a plurality of different values of the at least one soil parameter, different values of the at least one vegetation parameter, and associated yield responses for the at least one crop under the application of the crop efficiency product.

[0067] The decision unit 140 is configured to decide, for each of the plurality of locations in the field, whether to treat or not based on the predicted yield response. Optionally, the decision unit 450 is further configured to decide on a dose of the crop efficiency product to be applied for each of the plurality of locations.

[0068] The controlling unit 150 is configured to generate a treatment control signal indicative of the decision and to output the treatment control signal to the treatment control interface 160, which when transmitted causes an activation of at least one treatment device to comply with the decision.

[0069] Thus, the parameter determination unit 120, the yield response unit 130, the decision unit 140, and the controlling unit 150 may be part of, or include a general-purpose processing unit, a graphics processing unit (GPU), a microcontroller and/or microprocessor, a field programmable gate array (FPGA), a digital signal processor (DSP), and equivalent circuitry, alone or in combination. Furthermore, the above-described units may be connected to volatile or non-volatile storage, display interfaces, communication interfaces and the like as known to a person skilled in the art.

[0070] FIG. 4 schematically shows an embodiment of a treatment device 200 for applying a crop efficiency product in a field. The treatment device 200 comprises a treatment control interface 260, a treatment controlling unit 210, and a treatment arrangement 220 with one or a plurality of treatment units 221, 222, 223, 224.

[0071] The treatment device 200 may be e.g. ground robots with variable-rate applicators, aerial sprayers, or other variable-rate herbicide applicators. The treatment device 200 may also be a common sprayer. An example of the treatment device 200 in form of a crop duster is illustrated in FIG. 2. The treatment arrangement 220 may be a nozzle arrangement comprising a plurality of nozzles as treatment unit 221, 222, 223, 224.

[0072] The treatment control interface 260 of the treatment device is connectable to the treatment control interface 160 of the decision-support system 100 as discussed in FIG. 3. This may be done wirelessly, thus allowing a remote control of the treatment device 200 via the decision-support system 100. The treatment control interface 260 is configured to receive a treatment control signal indicative of the decision, for each of the plurality of locations, whether to treat or not. Optionally, the decision may include a dose to be applied for each of the plurality locations.

[0073] The treatment controlling unit 210 is configured to regulate respective ones of treatment units 221, 222, 223, 224 of the treatment arrangement 220 to apply a crop efficiency product to respective locations based on the received treatment control signal. Optionally, the treatment controlling unit 210 is configured to run an algorithm embedded on the treatment controlling device in real time for a location the treatment device passes based on the treatment control signal.

[0074] FIG. 5 schematically shows an embodiment of a system 300 for applying a crop efficiency product in a field. The system comprises a remote sensing device 50, a decision-support system 100 as described above and at least one treatment device 200 as described above. The remote sensing device 50, the decision-support system 100 and the at least one treatment device 200 may be associated with a network. For example, the network may be the internet. The network may alternatively 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 comprise any combination of wired networks, wireless networks, wide area networks, local area networks, etc. In some implementations, the decision-support system 100 may be a server to provide a web service to facilitate management of a plantation field. The user (e.g. a farmer) may collect remotely-sensed data with a drone in his plantation field. He may uploaded the remotely-sensed data e.g. via the network to the decision-support system 100 for further analysis. The decision-support system 100 may output a treatment control signal comprising the configuration information of the treatment devices for activating these treatment devices to comply with the decision.

[0075] The remote sensing device 50 is configured to collect remotely-sensed data of a field. The remote sensing device 50 may be e.g. a satellite, a radar, or a drone. An example of the remote sensing device 50 in form of a satellite is illustrated in FIG. 2. Optical remote sensing may be carried out to make use of e.g. visible, IR, NIR or multispectral sensors to form images of surface of the field by detecting the solar radiation reflected from targets on the ground. Satellite sensors or radars operating at microwaves, both active and passive, for the remote sensing monitoring of surface of the field.

[0076] The decision-support system 100 is configured to decide, based on the collected remotely-sensed data of the field, whether to treat or not, and preferably, to decide on a dose of the crop efficiency product to be applied for each of a plurality of locations in the field.

[0077] The treatment device 200 is configured to be controlled by the decision-support system to comply with the decision.

[0078] It has to be noted that embodiments of the invention are described with reference to different subject matters. In particular, some embodiments are described with reference to method type claims whereas other embodiments are described with reference to the device type claims. However, a person skilled in the art will gather from the above and the following description that, unless otherwise notified, in addition to any combination of features belonging to one type of subject matter also any combination between features relating to different subject matters is considered to be disclosed with this application. However, all features can be combined providing synergetic effects that are more than the simple summation of the features.

[0079] While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing a claimed invention, from a study of the drawings, the disclosure, and the dependent claims.

[0080] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. A single processor or other unit may fulfil the functions of several items re-cited in the claims. The mere fact that certain measures are re-cited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

REFERENCE LIST

[0081] 10 Field [0082] 12a location in the field [0083] 12b location in the field [0084] 12c location in the field [0085] 50 remote sensing device [0086] 100 decision-support system [0087] 110 data interface [0088] 120 parameter determination unit [0089] 130 yield prediction unit [0090] 140 decision unit [0091] 150 controlling unit [0092] 160 treatment control interface [0093] 200 treatment device [0094] 210 treatment controlling [0095] 220 treatment arrangement [0096] 221 treatment unit [0097] 222 treatment unit [0098] 223 treatment unit [0099] 224 treatment unit [0100] 260 treatment control interface [0101] 300 system [0102] S10 collecting remotely-sensed data [0103] S20 determining at least one soil parameter [0104] S21 determining at least one vegetation parameter [0105] S30 generating a predicted yield response [0106] S31 generating a predicted yield response [0107] S40 deciding whether to treat [0108] S41 evaluating whether a treat deteriorates, does not affect or improves the growth [0109] S42 determining whether the predicted yield response is above a positive reference value [0110] S43 deciding whether to treat [0111] S44 deciding on a dose [0112] S50 controlling at least one treatment device