EP3668311A1 - Utilisation de données provenant d'essais sur le terrain dans la protection de plantes pour l'étalonnage et l'optimisation de modèles de prévision - Google Patents

Utilisation de données provenant d'essais sur le terrain dans la protection de plantes pour l'étalonnage et l'optimisation de modèles de prévision

Info

Publication number
EP3668311A1
EP3668311A1 EP18753202.3A EP18753202A EP3668311A1 EP 3668311 A1 EP3668311 A1 EP 3668311A1 EP 18753202 A EP18753202 A EP 18753202A EP 3668311 A1 EP3668311 A1 EP 3668311A1
Authority
EP
European Patent Office
Prior art keywords
field information
information
observation point
field
computer system
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP18753202.3A
Other languages
German (de)
English (en)
Inventor
Holger Hoffmann
Bjoern Kiepe
Gang Zhao
Peter Howard Davies
Hans-Juergen Rosslenbroich
Hubert Schmeer
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BASF Agro Trademarks GmbH
Original Assignee
BASF Agro Trademarks GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BASF Agro Trademarks GmbH filed Critical BASF Agro Trademarks GmbH
Publication of EP3668311A1 publication Critical patent/EP3668311A1/fr
Pending legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01N—PRESERVATION OF BODIES OF HUMANS OR ANIMALS OR PLANTS OR PARTS THEREOF; BIOCIDES, e.g. AS DISINFECTANTS, AS PESTICIDES OR AS HERBICIDES; PEST REPELLANTS OR ATTRACTANTS; PLANT GROWTH REGULATORS
    • A01N25/00—Biocides, pest repellants or attractants, or plant growth regulators, characterised by their forms, or by their non-active ingredients or by their methods of application, e.g. seed treatment or sequential application; Substances for reducing the noxious effect of the active ingredients to organisms other than pests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention is concerned with the control of harmful organisms that may occur in the cultivation of crops.
  • the present invention relates to methods for the targeted acquisition of field information and to methods for calibrating and / or optimizing predictive models.
  • the subject matter of the present invention is also a computer system and a computer program product which make data obtained in field tests available for the calibration and optimization of predictive models for the infestation of fields, field zones or individual plants with harmful organisms and thus allow the development of improved models.
  • predictive models can be used that use data and models to predict the spread of harmful organisms. These can be models for infestation with diseases, insect infestation or weediness.
  • the forecasting tool "expert” uses culture data (developmental stage, growing conditions, crop protection measures), weather conditions (temperature, duration of sunshine, wind speed, precipitation) as well as pests / diseases (economic limits, pest / disease pressure).
  • culture data developmental stage, growing conditions, crop protection measures
  • weather conditions temperature, duration of sunshine, wind speed, precipitation
  • pests / diseases economic limits, pest / disease pressure
  • the technical task on which the present invention is based was to make high-quality data for further development, optimization and / or calibration of forecasting tools available in a simple and efficient manner.
  • the detection of the Data targeted and thus adapted to the further development, optimization and / or calibration of forecasting tools.
  • a first subject of the present invention is a method preferably for calibrating and / or optimizing predictive models, comprising the steps
  • a further subject of the present invention is a system preferably for calibrating and / or optimizing predictive models
  • a mobile computer system preferably for acquiring field information by means of a first application
  • a server preferably for providing predictive models or predicting field conditions or recommending agricultural measures
  • server is configured to associate the transmitted field information with further data, in particular weather data.
  • a further subject of the present invention is a computer program product comprising a computer-readable data medium and program code or a first application which is stored on the data carrier and which, when executed on a mobile computer system, causes the mobile computer system to carry out the following steps: determining or Acquire field information about
  • a further subject matter of the invention relates to a method for the targeted acquisition of field information with the aid of a mobile computer system comprising the steps: a) receiving or providing at least one observation point and at least one information protocol which is assigned to the observation point,
  • a further subject matter of the invention relates to a method for calibrating and / or optimizing prediction models, comprising the steps of: a) receiving or providing field information which has been specifically recorded on the basis of an observation point and an information protocol associated with the observation point,
  • a further subject matter of the invention relates to a method for calibrating and / or optimizing predictive models, comprising the steps of: a) receiving or providing field information,
  • Another object of the invention relates to a method for generating a prediction of field information, field conditions or for recommending agricultural measures, in particular for generating a prediction of infestation of crops with harmful organisms or for generating an agricultural measure comprising applying pesticides, preferably based on a pesticide need , an application time of the plant protection product or an application amount of the plant protection product, the method comprising the steps of: a) acquiring field information according to one of the methods described herein, wherein the field information is selectively or untargeted, and optionally, in the case of targeted or non-targeted detection , Prediction models are optimized and / or calibrated according to one of the methods described herein, followed by a targeted acquisition of field information according to a de r methods described herein
  • a further subject of the invention is a computer program or an application with instructions which, when stored on one or more computers, in particular a decentralized computer system with one or more mobile computer system (s) and / or a server running the procedures described here.
  • a further subject matter is a computer program product having instructions stored on a machine-readable carrier, the methods described herein being performed when the instructions are stored on one or more computers, in particular a distributed computer system having one or more mobile computer systems / or a server.
  • a further subject matter of the invention relates to a mobile computer system for the targeted acquisition of field information comprising: a) an interface which is configured to provide or receive at least one observation point and at least one information protocol associated with the observation point,
  • an activation module configured to enable targeted data collection based on the information protocol
  • a detection module configured to acquire field information based on the targeted data acquisition according to the information protocol
  • a further subject matter of the invention relates to a system, in particular a server, for calibrating and / or optimizing prediction models, comprising: a) an interface which is configured to provide or receive field information which is specifically based on an observation point and an information protocol that was assigned to the observation point were recorded,
  • a prediction module configured to provide a result of a predictive model based on the observation point
  • a verification module configured to calculate a difference between the field information associated with the observation point and the result or the prediction of the
  • a generation module configured to generate at least one further observation point and the information protocol associated with the further observation point when the difference exceeds a threshold
  • a further subject of the invention relates to a system, in particular a server,
  • Calibrate and / or optimize predictive models including the steps: a) an interface configured to provide or receive field information,
  • a verification module configured to determine a data density of the field information for a plurality of classes of field information
  • a generation module configured to generate at least one observation point and an information protocol associated with the observation point for the class of field information for which the data density falls below a threshold value
  • Another object of the invention relates to a system for generating a prediction of field information, field conditions or for recommending agricultural measures, in particular for generating a prediction of infestation of crops with harmful organisms or for generating an agricultural measure comprising applying pesticides preferably based on a crop protection product need, an application time of the plant protection product or an application amount of the plant protection product, the system comprising: a) one or more mobile computer system (s) configured to detect field information untargeted or targeted according to the methods described herein, b) optionally, for the A case of targeted or untargeted detection, a system for optimizing and / or calibrating predictive models that is configured to optimize the predictive models according to one of the methods described herein and / or calibrate and trigger targeted acquisition of field information according to the methods described herein; c) a system for updating the predictive model configured to advance the predictive model at regular or irregular intervals, particularly during a growing period, based on the acquired field information update and generate a prediction based on the updated predictive model.
  • the provision of a first application for capturing field information occurs over a network such as the Internet. So the application of a Network server on which it is deposited, downloaded via a network connection to the mobile computer system.
  • the search for reference or test fields comprises the provision of an observation point, in particular geo-coordinates of a reference or test field for the search of reference or test fields by the users, wherein the geo-coordinates are provided on the mobile computer system and are preferably visualized by means of the first application , for example as part of a navigation function.
  • the geocoordinates of a reference or trial field may be provided from a database storing reference or trial field data for a given set of reference and trial fields.
  • time data can be provided in addition to the geocoordinates.
  • the field information is acquired by means of the first application of the mobile computer system.
  • the detection of field information can be targeted or untargeted.
  • a specific acquisition of field information can be carried out in particular according to the method described here for the targeted acquisition of field information with the aid of a mobile computer system.
  • field information on the reference or experimental fields, cultivated on the reference or experimental fields crops and possibly present harmful organisms using the first application of the mobile computer system to a server of a provider of forecasts for the infestation of crops with harmful organisms based transmitted by predictive models.
  • further data in particular weather data
  • further data for linking the transmitted field information with the further data, in particular the weather data
  • the field information can be transmitted to the server together with time data and the geographic coordinates of the mobile computer system. Subsequently, the field information can be linked to weather data via the transmitted time data and geo-coordinates.
  • the predictive models are calibrated and / or optimized on the server based on the transmitted field information and other data used for linking, such as weather data, wherein the calibration and / or optimization of the predictive models according to the calibration method described herein and / or optimizing predictive models.
  • an optimized prediction based on the optimized and / or calibrated prediction model is transmitted from the provider's server to another mobile computer system on which preferably a second application for determining forecasts is provided, wherein further preferably the second application provides a prediction for the prediction - case of crops with harmful organisms on the basis of the optimized and / or calibrated predictive model.
  • the system for calibrating and / or optimizing predictive models comprises: a mobile computer system, preferably for acquiring field information by means of a first application, and
  • a server preferably for providing predictive models or predicting field conditions or recommending agricultural measures
  • the mobile computer system is configured to provide geographic coordinates of a reference or trial field for searching reference or trial fields by the users on the mobile computer system via a first application, and preferably to capture field information using the first application of the mobile computer system, the following field information is recorded:
  • the mobile computer system being configured to transmit the collected field information to the server
  • the Server is configured to link the transmitted field information with other data, in particular weather data.
  • the server is preferably further configured such that prediction models provided on the server are calibrated and / or optimized on the server on the basis of the transmitted field information and further data used for linking. More preferably, the server is configured to transmit optimized predictions based on the optimized and / or calibrated predictive model from the server to another mobile computer system on which preferably a second application is provided for determining forecasts.
  • Field tests which are carried out for the testing of pesticides, are used in accordance with the invention to collect data for the emergence and propagation of harmful organisms in crop plants and to use them for optimizing prediction tools. For certain questions, it is also common to create untreated fields, partial areas, field strips or small parcels with or without repetition in order to estimate the infestation with harmful organisms without the use of pesticides.
  • a "harmful organism” is understood below to mean an organism that can be found in cultivation of crops and damage the crop, adversely affect the crop harvest or compete with the crop for natural resources Pests such as beetles, caterpillars and worms, fungi and pathogens (eg bacteria and Viruses). Although viruses do not belong to the organisms from a biological point of view, they should nevertheless fall under the term harmful organism in the present case.
  • cultiva plant is understood to mean a plant that is purposefully cultivated by the intervention of humans as a useful or ornamental plant.
  • plant protection product is understood to mean a product which can effectively combat harmful organisms and / or prevent their spread
  • a plant protection product is usually a formulation containing one or more active substances against one or more harmful organisms
  • the herbicide is the active substance for controlling the weeds or grass weeds
  • the herbicidal formulation is a herbicidal formulation
  • the harmful organisms are a fungus
  • the fungicide is a fungicide and the plant protection product a fungicidal formulation.
  • Plant protection products do not necessarily have only beneficial effects on plant production. Their use may also present risks to humans, animals and the environment, in particular if they are placed on the market and / or used improperly without testing and without official approval.
  • the testing of plant protection products is i.a. made in field trials.
  • the purpose of a field trial may be, for example, to determine the effect of a plant protection product, to compare the effect with other plant protection products or to determine an optimal amount of plant protection product or an optimal time to deploy a plant protection agent.
  • reference fields are created that are not exposed to pesticides. By comparing a test field or field in which a pesticide has been used, for example, to control a harmful organism, with the reference field can be used to gain information about the efficacy of the pesticide.
  • field is understood to mean a spatially delimitable area of the earth's surface which is used for agriculture by cultivating crops in such a field, possibly supplying them with nutrients and, if necessary, harvesting them.
  • a field can be characterized by its geographical position and / or the Be defined field boundaries.
  • test field is understood to mean a field for field trials which comprises several subareas for different test sequences according to a test protocol.
  • the test sequence may include different pesticides for different patches.
  • the test sequence may comprise different amounts of the plant protection product or different times for the application of the plant protection product on the different partial surfaces.
  • a subarea of the test field may be an untreated subarea serving as the reference field.
  • reference field is understood to mean a field or a partial area of a test field on which crop plants are planted and which is used as a reference in a field trial for a plant protection product, in contrast to other fields or partial areas of the test field which are part of the field trial no pesticides are used on the reference field.
  • the reference or experimental fields are used to obtain data for the propagation of harmful organisms under real conditions and to use these for the optimization, further development and / or calibration of prognostic tools.
  • a first application for a mobile computer system is provided according to the invention, which can be used by persons who are involved in a field trial and have access to a reference or trial field.
  • the application according to the invention is software that can usually be loaded from a website and / or a so-called app store on a mobile computer system and installed there. Accordingly, the application can be made available via a network such as the Internet.
  • the application can be deployed on a server and downloaded over the network to the mobile computer system.
  • the provision of the application can be linked to an authorization in order to make the application accessible only to a selected group of users. In the present case, the selected group of users may be restricted to authorized personnel in field trials in order to ensure a high degree of reliability in collecting the data.
  • the mobile computer system may be, for example, a laptop, a notebook or a smartphone.
  • the use of a smartphone as a mobile computer system has the advantage that today almost every person owns such a smartphone and leads with it as a constant companion.
  • a smartphone typically has all the features and means required to practice the present invention.
  • the search for reference or test fields can be performed using position data or geo-coordinates.
  • the position data or geo-coordinates may be provided via a location sensor or GPS sensor of the mobile computer system.
  • the target position or the geocoordinates of the reference or trial field can be provided in order to generate a navigation path between the current position data or the current location of the mobile computer system and the target position.
  • the application according to the invention is intended to assist the user in collecting information on the reference or experimental fields which he regularly visits in the context of the field trial. In particular, the acquisition of field information should be targeted. It is conceivable that all information or part of the information must be entered by the user in the application.
  • the input can be made in the form of a text entry.
  • the geo-coordinates of the position at which the user is in the execution of the application according to the invention by means of GPS sensor, which most smartphones nowadays have, automatically detected. It is also conceivable that the user defines his location on a virtual map.
  • the user is asked to name the cultivated crop grown in a reference or trial field (plant variety). It is conceivable that this information, for example, on a sign in the field is kept in machine-readable form. It is conceivable, for example, that the information about the respective cultivated crop is present in the form of an optical code (eg a barcode, data matrix code, QR code or a comparable code). In such a case, it is conceivable that the user uses his smartphone to read the optical code by means of the built-in camera function and to transfer the information into the application according to the invention. It is also conceivable that the user is asked to take a photograph of the plant culture (eg of a single plant).
  • an optical code eg a barcode, data matrix code, QR code or a comparable code
  • the respective plant culture is automatically recognized with the aid of image analysis and object recognition methods. It is conceivable that the automatic recognition is performed on the mobile computer system of the user; It is also conceivable that the photographic recording, for example, transmitted via the mobile network to an external server and analyzed there. It is conceivable that the result of the analysis is transmitted to the user. sowing date
  • the sowing date may be entered by the user, read in from an external source (e.g., optical code sign), or automatically determined from the growth stage of the plant by photographic capture using image analysis techniques.
  • an external source e.g., optical code sign
  • the type of soil present and the tillage operations carried out are preferably likewise queried / determined by the application according to the invention. history
  • the growth stage in which the plants grown on the reference or trial field can be entered by the user or automatically determined by means of a photographic image using image analysis methods. It is also possible to transmit predictions of the growth model from the server to the mobile computer system, to give the user an indication of the BBCH stage of the culture and thus to provide the optimal time for collecting field information.
  • the predictions of the growth model are made available to the mobile computer system, for example by means of an API interface.
  • a harmful organism has spread in the reference or experimental field and, if so, what extent the propagation has already assumed. It is therefore preferably requested by the application and / or determined whether a harmful organism is recognizable to which harmful organism it is, how much the plants are already affected and how the harmful organism has spread / spread (eg in the form of nests or starting from a certain field boundary or similar). Photographic images can also be used to determine this information, for example in order to detect a pest present and / or to estimate / quantify the severity of an infestation. After the information has been collected using the application according to the invention, they are transmitted from the mobile computer system to an external server.
  • the transmission preferably takes place via a mobile radio network.
  • the provider of the forecasting tool has Access to this server and can see the transmitted data. It is also conceivable that not the provider itself has access to the server, but a developer whose development / service the provider comes to good. It is also conceivable that other people are involved, work on the data and pass it on. For simplicity's sake, however, the invention will be described as having only one instance (the vendor), which handles the development, optimization, calibration, and distribution of forecasting tools or forecasts, even though different instances may be involved in reality. This simplification is therefore not to be understood as a limitation of the invention. If the information is stored on the server, the information provided by the user is linked to other data.
  • the transmitted information includes geo-ordinates and temporal data (date, time).
  • This data can be linked, for example, to the weather data at the corresponding location at the corresponding time or within a defined period of time prior to the corresponding time.
  • This link reveals how the weather has developed in a defined period of time prior to the time the information was collected by the user at the location of the reference or trial field.
  • This information is important because weather patterns usually have a major impact on the spread of harmful organisms.
  • the further linkage with the cultivated crop and any harmful organisms found provides information on the conditions under which the harmful organisms have developed and spread on the existing crop in the present case. This information can be used to compare with existing models, to calibrate existing models, to optimize existing models, and / or to develop new models.
  • the application according to the invention is available worldwide via an Internet site and / or an App Store and many users are present who are involved in field trials, it is possible to collect large amounts of data on different crop plants, weather conditions and harmful organisms.
  • the predictor or forecasting tool provider is thus able to provide steadily improved forecasts to its customers.
  • the provision of field information on the server can be carried out by the described method for the targeted acquisition of field information with the aid of a mobile computer system.
  • a corresponding decentralized system includes one or more mobile computer systems and the system described above, in particular in the form of the server.
  • the observation point specifies geocoordinates and time data.
  • the geocoordinates can specify a defined subarea of a reference or trial field.
  • the geocoordinates can determine a specific location of one or more individual plants, for example in a defined subarea of the reference or reference area. Specify this search box.
  • the geocoordinates can be generated according to a regular or randomized spatial pattern.
  • the time data may specify a specific time, a plurality of specific times or a predetermined frequency of specific times in a growing period.
  • a cultivation period refers to a management period in one season, for example, the period between sowing and harvest.
  • the times can be specified regularly or randomized.
  • the time can be linked via the BBCH code (Federal Biological Research Center, Bundessortenamt and Chemical Industry Code) with the morphological growth stage of the crop.
  • the scatter in the field information can be reduced.
  • the field information acquired at specific times may be correlated with the BBCH code of the crop, and the detection may occur at predetermined BBCH stages.
  • the information protocol specifies the field information to be acquired.
  • the information protocol specifies, for example, that in addition to the plant culture, a growth stage, a recognizable harmful organism and a spreading or spreading threshold of a harmful organism infestation are to be recorded.
  • the harmful organism may be a disease, a weed or an insect.
  • the activation takes place on the basis of the observation point and / or the associated information protocol.
  • the activation takes place on the basis of a current time and / or current position data of the mobile computer system with respect to the observation point.
  • the activation comprises a navigation function that generates a navigation path to the observation point and in particular to geocodings defined therein based on position data of the mobile computer system.
  • a navigation path can be generated to different geo-coordinates defined in the observation point. For example, if the geocoordinates specify a regular or randomized spatial pattern, the navigation path may be generated in sections for each geographic coordinate at which field information is to be collected.
  • the user can be guided step by step to the individual geo-coordinates, at which a recording of the field information is to take place. This allows simplified and targeted data acquisition tailored to the further development, optimization or calibration of predictive models.
  • an optical code such as a barcode or a QR code
  • a transponder such as an RFID tag
  • the plant culture can be read out via the optical code or the transponder which is attached to the reference field, to the test field or to a partial surface of the test field.
  • field information according to the information protocol can be selected for selection, for example as a drop-down list on a touch-sensitive screen. sensitive display. By detecting a touch at a position on the touch-sensitive display that corresponds to the displayed field information, it can be received by the mobile computer system.
  • the field information to be selected can be predefined, so that only standardized values can be selected according to defined criteria. Such preselection increases the data quality since the detection of the field information is uniform.
  • a photographic image is provided for detecting the field information with the aid of the mobile computer system, and the field information is extracted by means of an image analysis method.
  • photographic images of individual plants can be analyzed for the plant culture, the growth stage or the harmful organisms attack.
  • image analysis methods can be used, for example, for the classification and quantification of diseases, insects or weeds.
  • a prediction accuracy may be determined from the determined difference between the field information associated with the observation point and the result of the predictive model based on the observation point.
  • the predictive model may be used to predict field information on normally-managed fields. In contrast to reference or experimental fields, normally managed fields are not managed according to a defined framework for field trials, for example according to a defined experimental protocol.
  • the forecast serves to generate recommendations for the management of the field or agricultural measures, such as the treatment with plant protection products.
  • the prediction can be transmitted to a mobile computer system, wherein additionally the prediction accuracy of the prediction model determined from the difference is transmitted.
  • the provision of forecasting accuracy allows the user of the prediction at any time to provide a measure of the accuracy of the prediction and thus facilitates the deriving of decisions regarding, for example, the recommended agricultural measures.
  • the predictive models can be calibrated and / or optimized at regular or irregular time intervals, in particular during the growing period, on the basis of the detected field information, the field information being detected selectively or untargeted.
  • the provision of the field information can take place instantaneously or immediately after the field information has been acquired and thus in real time.
  • the provision of the field information may also be delayed with acquiring the field information if the network connection of the mobile computer system to the server is disrupted. The deployment is triggered in this case as soon as the network connection is restored.
  • the instantaneous or immediate transmission allows seamless optimization, calibration or updating of the predictive model, on the basis of which a prediction can be generated.
  • the existing forecasting model and / or the forecasting accuracy can be updated in real time, for example by using the predictive model dell and / or the forecast accuracy can be optimized or adjusted immediately after receiving the field information in real time.
  • the update can be done with any new field information being provided, and thus in real time
  • updating may occur immediately after transmitting or providing the selectively acquired field information based on the selectively acquired field information in accordance with the methods for calibrating and / or optimizing predictive models described herein.
  • watchpoints and the associated information logs are generated for one or more classes of field information.
  • a class of field information refers to information that specifies the growth stage, soil type, and pest infestation.
  • observation points and the associated information protocols are generated for a class of field information, for example those specifying the growth stage or the harmful organism infestation.
  • observation points and associated informational protocols are generated for multiple classes of field information, such as those specifying the growth stage and the pest infestation.
  • the observation points can be determined from reference or experimental field data such as geodesics, climate zone data, experimental protocols, soil data or plant culture data.
  • the reference or trial field data includes geodesics.
  • a reference or trial field may be stored via a geocoordinate and an associated reference or trial field boundary or via a set of geographic coordinates identifying the reference or trial field boundary.
  • field trial-specific data for a trial field may be stored in the database as reference or trial field data.
  • reference or trial field data may be stored, which specify different partial areas of the trial field. Each subarea may be assigned a specific test protocol, for example a test sequence with a specific treatment intensity or treatment frequency with a plant protection product.
  • observation points can be generated based on the database, which includes reference and trial field data.
  • FIG. 1 shows an exemplary decentralized computer system comprising a server and a mobile computer system
  • FIG. 2 shows an exemplary method for the targeted acquisition of field information
  • FIG. 3 shows an example method for calibrating and / or optimizing prediction models based on field information that has been specifically acquired
  • FIG. 4 shows another exemplary method for calibrating and optimizing prediction models based on field information that has been specifically acquired.
  • FIG. 1 shows an exemplary distributed computer system 10 for calibrating and / or optimizing predictive models including a server 12 and a mobile computer system 14.
  • the server 12 may be a cloud server having an IT infrastructure for storage, computing power or application software.
  • the server 12 may be accessed by computer systems 14 such as a desktop computer or mobile computer systems 14 such as a smart phone, a portable digital assistant (PDA), a laptop or a tablet via a network 16 such as the Internet.
  • observation points and information protocols may be communicated from server 12 to mobile computer systems 14 or field information from mobile computer systems 14 to server 12.
  • the mobile computer system 14 includes:
  • a communication interface 26 configured to provide at least one observation point and at least one information protocol associated with the observation point
  • an activation module 28 in communication with the interface 26 configured to activate targeted data collection based on the information protocol
  • a collection module 30 in communication with the activation module 28 configured to capture field information based on the targeted data collection in accordance with the information protocol .
  • the server 12 includes:
  • a communication interface 32 configured to receive field information collected untargeted or targeted, or to transmit observation points and associated information protocols to the mobile computer system 14,
  • a prediction module 18 configured to provide a result of a predictive model based on the observation point
  • a verification module 20 configured to determine a difference between the field information and the result of the predictive model or a data density
  • a generation module 22 configured to generate observation points and associated information protocols when the difference exceeds a threshold, or when the data density for a class of field information falls below a threshold.
  • prediction models 18 are provided by means of a prediction module based on culture data such as developmental stage or growth conditions, weather data such as temperature, sunshine duration, wind speed or precipitation, or pest organism data such as economic limits or pest pressure, plant growth prediction or infestation Provide risk. Such forecasts can also be used to recommend agricultural measures such as the application of pesticides and, in particular, the time of treatment, the amount and the type of pesticide in a growing season. In addition, an assessment of past crop protection measures can be prepared and their impact on future crop protection measures or yield can be determined. Based on field information communicated from the mobile computer systems 14 to the server 12, the predictive models can be verified and falsified by means of a verification module 20.
  • pest infestation field information may be communicated from the mobile computer system 14 to the server 12.
  • the result of the prediction model for the pest infestation for the communicated geo-coordinate and the communicated point in time can be compared with the acquired pest infestation field information.
  • the detected field information can be linked to other data.
  • weather data for the communicated geo-coordinate and the communicated time point can be called up, for example, from external database 24 accessed by server 12 and included in the prediction.
  • reference field data or trial field data is provided on the server 12 or in a separate database 24 accessed by the server 12.
  • Geo coordinates may include coordinates of the field boundary or a base coordinate and associated field boundary shape.
  • additional test protocol data, soil data or data concerning the climate region can be stored for the available reference fields or trial fields.
  • Observation points and information protocols can be generated on the basis of this reference field data or test field data.
  • field information stored on the server or in a separate database 24 accessed by the server can be checked by means of the verification module 20 regarding the quality of the data stock.
  • the stored field information may be checked for the amount of data for different geographic coordinates, growth stages or weather conditions. If there is a quantitative deviation for a class of field information in the sense that there is a small amount of data for a climate region, for a range of growth stages or for certain weather conditions, further observation points and information protocols can be generated by means of the generation module 22 and sent to one or more mobile computer systems 14 are communicated. In this way, additional field information can be specifically recorded with which the predictive model can be further developed and improved.
  • FIG. 2 shows an exemplary method for the targeted acquisition of field information which is acquired with the aid of a mobile computer system 14.
  • a first step S1 at least one observation point and at least one information protocol associated with the observation point are provided on the mobile computer system 14. These may have been transmitted by the server 12 and provided at the interface 32.
  • the observation point preferably comprises geocoordinates and time data.
  • the geocoordinates specify a subarea of the reference or trial field.
  • the geodesists may specify one or more locations of a crop on the reference or trial field.
  • the time data may specify a specific time in a growing period, which may be linked to the morphological growth stage of the crop via the BBCH code.
  • the time data may also specify several specific times in the growing period.
  • the information protocol specifies the field information to be acquired.
  • a targeted data acquisition is activated on the basis of the information protocol.
  • the activation can be done manually by the user, for example by opening the application.
  • the activation may be automatic, such as by detecting the current time on the mobile computer system 14 and the current location of the mobile computer system 14. This allows the current time and position of the mobile computer system to be tems 14 are provided via integrated sensors or functions of the mobile computer system 14.
  • the position may be detected via a location sensor integrated in the mobile computer system 14, such as a GPS sensor.
  • the targeted data acquisition is activated when the server-side provided geocoordinates and time data with the computer system side provided position and time in a given frame match.
  • a warning or message may be issued on the mobile computer system 14 if a decreasing distance to the position determined by the geographic coordinates is detected based on the position of the mobile computer system 14.
  • a navigation function can be triggered, which leads the user to the specific position specified by the geo-coordinates.
  • a third step S3 field information is received based on the targeted data acquisition.
  • the data acquisition results from the information protocol. So geo coordinates of the reference or trial field can be detected. Preferably, the geo-coordinates of the position at which the user is in the execution of the application according to the invention, by means of GPS sensor, which most smartphones nowadays have, automatically detected. It is also conceivable that the user defines his location on a virtual map. Furthermore, data on the plant culture can be collected. The user may be asked to name the cultivated crop grown in a reference or trial field (plant variety). It is conceivable that this information, for example, on a sign in the field is kept in machine-readable form.
  • the information about the respective cultivated plant is present in the form of an optical code (for example, a bar code, data matrix code, QR codes or a comparable code).
  • an optical code for example, a bar code, data matrix code, QR codes or a comparable code.
  • the user uses, for example, a smartphone in order to read the optical code by means of the built-in camera function and to transmit the information into the application according to the invention.
  • the user is asked to take a photograph of the plant culture (e.g., of a single plant).
  • the respective plant culture is automatically recognized with the aid of image analysis and object recognition methods.
  • the automatic recognition is carried out by means of, for example, image analysis and object recognition methods on the mobile computer system of the user; it is also conceivable that the photographing be made e.g. transmitted via the mobile network to an external server and analyzed there. It is conceivable that the result of the analysis is transmitted to the user.
  • sowing date or soil can be collected.
  • the sowing date can be entered by the user, read in from an external source, eg via a sign with optical code or via an RFID tag, or automatically determined from the growth stage of the plant by means of a photographic image using image analysis methods.
  • Type of existing soil and the soil Beitungsnch are preferably also queried / determined by the application of the invention.
  • data on the history can be recorded. It may also be advantageous to obtain information on the history of the reference or trial field, for example which plants have been previously grown and / or which plant protection measures have been previously taken. This data may be requested by the user or may be consulted from other sources at a later time (e.g., if the information has already been transferred to an external server).
  • the growth stage in which the plants grown on the reference or trial field can be entered by the user or automatically determined by means of a photographic image using image analysis methods.
  • the received field information is transmitted to the server 12 and can be used to calibrate or optimize the predictive models.
  • FIG. 3 shows an exemplary method for calibrating and optimizing predictive models based on field information that has been specifically acquired.
  • field information is provided which has been specifically detected according to the method described above.
  • the field information for a geocoordinate at a time that may be linked to the morphological growth stage of the crop via the BBCH code may include pest infestation information.
  • the predictive models are verified or falsified based on the provided field information. For this purpose, a difference between the field information and the result of the prediction for an observation point is determined. For example, the result of the prediction model for pest infestation for the communicated geo-coordinates and the communicated time can be compared with the detected pest infestation field information.
  • a third step S7 at least one further observation point and a corresponding information protocol are generated if the difference exceeds a threshold value.
  • the generated observation points and information protocols are communicated to one or more mobile computer system (s) 14. In this way, additional field information can be specifically recorded with which the predictive model can be further developed and improved.
  • FIG. 4 shows another exemplary method for calibrating and optimizing predictive models based on field information that has been acquired selectively or untargeted.
  • a first step S9 historical field information is provided.
  • Such field information may be stored on the server 12 or in a separate database 24 accessed by the server 12.
  • the quality of the data is checked by checking field information for the quality of the data.
  • the stored field information may be checked for the amount of data for different geocoradians, growth stages or weather conditions.
  • a third step S1 at least one further observation point and an associated information protocol are generated if the difference exceeds a threshold value.
  • the generated observation points and information protocols are communicated to one or more mobile computer system (s) 14. In this way, additional field information can be specifically recorded with which the predictive model can be further developed and improved.

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Abstract

La présente invention concerne la lutte contre des organismes nuisibles qui peuvent affecter la culture de plantes cultivées. L'objet de la présente invention concerne un procédé, un système informatique et un programme informatique qui donnent accès à des données, qui ont été obtenues lors d'essais sur le terrain, à l'étalonnage et à l'optimisation de modèles de prévision pour l'attaque de plantes par des organismes nuisibles et qui permettent ainsi le développement de modèles améliorés.
EP18753202.3A 2017-08-18 2018-08-17 Utilisation de données provenant d'essais sur le terrain dans la protection de plantes pour l'étalonnage et l'optimisation de modèles de prévision Pending EP3668311A1 (fr)

Applications Claiming Priority (2)

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EP17186861 2017-08-18
PCT/EP2018/072351 WO2019034785A1 (fr) 2017-08-18 2018-08-17 Utilisation de données provenant d'essais sur le terrain dans la protection de plantes pour l'étalonnage et l'optimisation de modèles de prévision

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EP3668311A1 true EP3668311A1 (fr) 2020-06-24

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EP (1) EP3668311A1 (fr)
CN (1) CN110998642A (fr)
AR (1) AR112857A1 (fr)
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CA (1) CA3071932A1 (fr)
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US11589509B2 (en) * 2018-10-26 2023-02-28 Deere & Company Predictive machine characteristic map generation and control system
BE1030162B1 (fr) * 2022-01-05 2023-07-31 Medinbio Sprl Procédé de génération d'un plan de traitement adapté pour le développement d'une surface cultivée sans pesticides d'origine chimique, modèle d'apprentissage et système associés

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EP1843656B1 (fr) * 2005-11-23 2010-08-11 Pioneer Hi-Bred International, Inc. Dispositif et procede pour deceler des caracteristiques de resistance au vent parmi une population de plantes
US8862630B2 (en) * 2009-06-03 2014-10-14 Pioneer Hi-Bred International Inc Method and system for the use of geospatial data in the development, production, and sale of agricultural seed
EP2535781A1 (fr) * 2011-06-17 2012-12-19 ABB Research Ltd. Collecte de données dans une installation industrielle
US20140188573A1 (en) * 2012-12-31 2014-07-03 Pioneer Hi-Bred International, Inc. Agricultural input performance exploration system
US20150234767A1 (en) * 2013-09-23 2015-08-20 Farmobile, Llc Farming data collection and exchange system
US20160247082A1 (en) * 2013-10-03 2016-08-25 Farmers Business Network, Llc Crop Model and Prediction Analytics System
US10555461B2 (en) * 2016-01-04 2020-02-11 Tata Consultancy Services Limited Systems and methods for estimating effective pest severity index
CN205334168U (zh) * 2016-04-21 2016-06-22 贾如春 基于大数据病虫害监测预警系统
US10509872B2 (en) * 2017-03-08 2019-12-17 The Climate Corporation Location selection for treatment sampling
US11257172B2 (en) * 2017-04-26 2022-02-22 International Business Machines Corporation Cognitive based decision support system for agriculture
US10748081B2 (en) * 2017-05-12 2020-08-18 Harris Lee Cohen Computer-implemented methods, computer readable medium and systems for a precision agriculture platform that identifies generic anomalies in crops

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US20200250360A1 (en) 2020-08-06
RU2020110252A3 (fr) 2022-01-25
CN110998642A (zh) 2020-04-10
CA3071932A1 (fr) 2019-02-21
RU2020110252A (ru) 2021-09-20
BR112020003310A2 (pt) 2020-08-25
WO2019034785A1 (fr) 2019-02-21

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