WO2022162246A1 - Système et procédé informatisé d'interprétation de données de localisation d'au moins un travailleur agricole, et programme d'ordinateur - Google Patents
Système et procédé informatisé d'interprétation de données de localisation d'au moins un travailleur agricole, et programme d'ordinateur Download PDFInfo
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- WO2022162246A1 WO2022162246A1 PCT/EP2022/052371 EP2022052371W WO2022162246A1 WO 2022162246 A1 WO2022162246 A1 WO 2022162246A1 EP 2022052371 W EP2022052371 W EP 2022052371W WO 2022162246 A1 WO2022162246 A1 WO 2022162246A1
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Classifications
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Definitions
- the present invention relates to methods for interpreting farm worker location data.
- This document describes the definition of a field from GPS data of the corners of the field. Then, it describes the measurement of the position of a worker by means of a GPS, and the attribution of the position to the presence in a field, by comparison with the coordinates defined for this field. This information is used to fill in the worker's agenda.
- the data is interpreted by a fuzzy logic algorithm, which associates certain measured data, depending on the date, with an agricultural task, for example "planting rice” associated with the fact that one is in June, and the farm worker is working at 0.41 m/s, with an efficiency of 5.8 a/h, and a field efficiency of 65%.
- a fuzzy logic algorithm which associates certain measured data, depending on the date, with an agricultural task, for example "planting rice” associated with the fact that one is in June, and the farm worker is working at 0.41 m/s, with an efficiency of 5.8 a/h, and a field efficiency of 65%.
- the invention relates to a computerized process for interpreting location data of at least one agricultural worker, in which: - a database of agricultural activities is provided comprising, for each agricultural activity, a diary of at least two agricultural tasks, each agricultural task being characterized by a location identifier and a position in the diary,
- an agricultural exploitation database comprising at least one agricultural parcel characterized by a location identifier and a location,
- a computerized interpretation module associates said temporal zoning data with an agricultural task by means of said temporal zoning data and said databases of agricultural activities and agricultural exploitation.
- the recognition of an agricultural task is based on a priori knowledge of the agricultural activities including agricultural tasks, which makes it possible to increase the rate of recognition of the agricultural task.
- the interpretation module applies a classifier relating to the agricultural activities defined for the agricultural holding and/or a classifier relating to the agricultural activities defined for a set of agricultural holdings and, in the case where the two classifiers are applied, the interpretation module selects an agricultural task from the results of the two classifiers.
- the computerized interpretation module associates the temporal zoning data of the agricultural worker with an agricultural task by means, in addition, of the temporal zoning data of the agricultural machine.
- the computerized interpretation module associates the agricultural worker's temporal zoning data with an agricultural task by also means of meteorological data relating to the agricultural operation obtained from a meteorological database.
- the method further comprises an update of the database of agricultural activities from at least the interpreted location data.
- the invention provides a method according to the invention in which the computerized interpretation module associates said time zoning data with an agricultural task using said temporal zoning data and said agricultural activity and farming databases according to the following steps:
- the computerized interpretation module constructs an image for a determined agricultural parcel from the temporal zoning data and said databases of agricultural activities and agricultural exploitation,
- the computerized interpretation module associates the constructed image with an agricultural task carried out in the agricultural plot using a convolutional neural network.
- the constructed image comprises a trace formed by the temporal zoning data and at least one geometric primitive parameterized according to the temporal zoning data data and said databases of agricultural activities and 'agricultural exploitation ; at least one intensity of the image being determined from the temporal zoning data and the agricultural and farm activity databases.
- the computerized interpretation module encodes at least one piece of information on one layer of a plurality of layers forming the image, this at least one piece of information being derived from or deduced from time zoning data and databases. farming and farming activity data,
- the encoded information can be expected to be:
- the computerized interpretation module can be expected to train the convolutional neural network with images associated with: - an agricultural task from the database of agricultural activities of a farm of the user or external farms,
- the invention relates to a computer program comprising program code instructions for the execution of the steps of this method when said program is executed on a computer.
- the invention relates to a computerized system for interpreting location data of at least one agricultural worker comprising a computerized interpretation module suitable for associating temporal zoning data of at least an agricultural worker received from a portable electronic device communicating from the agricultural worker to an agricultural task by means of an agricultural activity database comprising, for each agricultural activity, a diary of at least two agricultural tasks, each agricultural task being characterized by a location identifier and a position in the diary and an agricultural exploitation database comprising at least one agricultural parcel characterized by a location identifier and a location.
- the computerized system further comprises at least one portable electronic communicating device of the agricultural worker adapted to communicate time zoning data of the agricultural worker to the computerized interpretation module.
- the computerized system further comprises a communicating portable electronic device of an agricultural machine suitable for communicating time zoning data of the agricultural machine, and the computerized interpretation module is suitable for associating data from time zoning of the agricultural worker further from the time zoning data of the agricultural machine.
- FIG. 1 shows a top view of a cartography of a farm.
- FIG. 2 is a "smartphone" screenshot showing the user system at rest.
- FIG. 3 represents a same “smartphone” screenshot showing the active user system.
- FIG. 4 represents a first screen of the supervision interface.
- FIG. 5 presents a second screen of the supervision interface.
- FIG. 6 schematically represents a system according to one embodiment of the invention.
- FIG. 7 schematically represents a data processing architecture according to one embodiment.
- FIG. 10 represents an example of results of classification of an agricultural task by the computerized interpretation module during the training phase of the convolutional neural network.
- FIG. 11 represents an example of results of classification of an agricultural task by the computerized interpretation module once the convolutional neural network has been trained
- FIG. 12 represents a second screen of results of association of temporal zoning data with an agricultural task
- FIG. 13 represents an example of temporal zoning data collected and interpreted into an agricultural task.
- Agricultural means any activity relating to the cultivation of plants or the raising of animals, including forestry, oyster farming, myciculture, shellfish farming, mussel farming, or snail farming.
- the system comprises a central server 7 exchanging with several types of remote computer stations 4, 21 via a network 16 such as, for example, the Internet network.
- Computer station 4 is a stand-alone portable device. It can for example be a computerized mobile phone of a user, commonly designated by the Anglicism
- Such a “smartphone” comprises a processor 22.
- the processor 22 is suitable for executing computer programs residents of the computer station 4, and in particular the computer program which will be described in more detail below.
- the "smartphone” also includes a memory 14, in which can be stored a certain amount of information accessible by the processor 22.
- the “smartphone” still has a man-machine interface 8 allowing a user to interact with the “smartphone”.
- the man-machine interface 8 comprises for example a screen allowing information to be displayed to the user, and a keyboard, allowing the user to enter information intended for the “smartphone”.
- the information entered can be stored in the memory 4. If necessary, these two functionalities are grouped together in the form of a touch screen 9 superimposing the display and the entry of information.
- the “smartphone” still has a clock 12 giving the time.
- Clock data can be stored in memory 14.
- the “smartphone” also has a geolocation module 13.
- a geolocation module 13 is for example a system based on satellite positioning technology generally referred to as “GPS”, an acronym for the designation English “Ground Positioning System”, or “Système de position au sol”, in French.
- GPS satellite positioning technology
- Such a system is based on a chip which receives information from several satellites in geostationary orbit, and is capable of determining its position on the ground by triangulation from the information received from the satellites. The determined position or location is stored in memory 14.
- the "smartphone” also has a communication module 15, allowing the “smartphone” to communicate with the outside world, and in particular with the server 7, via a network 16. It can be act of a wireless communication module, or a wired communication module.
- the communication module 15 can, if necessary, communicate with the server 7 via the network 16 via a router 17.
- the server 7 is a computer server which includes a processor 23 capable of executing one or more computer programs.
- the server 7 notably comprises a communication module 6 adapted to communicate with the “smartphone”. Communication with the “smartphone” is two-way, but the transfer of information is done rather in the direction going from the “smartphone” to the server 7.
- the server 7 also accesses a database 5 in which are stored maps of the farms concerned by the service, which are defined in the manner described below.
- the database 5 stores the data, whether processed or not, coming from the various “smartphones”.
- the database 5 also stores association information from the various “smartphones” to one or more farms.
- the database 5 also includes models of agricultural activities specific to the agricultural operation.
- the server 7 includes a processing module 18.
- the processing module 18 is adapted to process the data received from the “smartphone”, according to pre-established methods which will be described below.
- the data stored in the database 5 can comprise raw data as received from the “smartphones”, and processed data resulting from the processing of the raw data by the processing module 18. If necessary, the exploitations are distributed over multiple servers.
- the system may also include a personal microcomputer 21.
- the personal microcomputer 21 is distinct from the “smartphone”. That said, in some cases, depending on the authorizations, a "smartphone" can be used to implement the functions provided by the microcomputer 21, and it can even be the same
- “smartphone” as the one used for the measurement of location data at a time.
- the personal microcomputer 21 includes a processor 24.
- the processor 24 is adapted to execute computer programs resident on the personal microcomputer 21.
- one of the computer programs in question is a network browser .
- the personal microcomputer 21 also includes a man-machine interface 25, such as a screen and a keyboard and/or a mouse.
- the personal microcomputer 21 includes a communication module 26 allowing it to communicate with the server 7 via the network 16.
- the network browser of the personal microcomputer 21 provides access to an Internet page stored on the server 7, and providing the personal microcomputer 21 with structured information developed by the processing module 18 from the data received from the different “smartphones”.
- the farm is mapped. It will be noted that the system can integrate several farms for the same user. In the following example, we describe the mapping of a farm, the configuration and the implementation for another farm of the same user being similar. In case of multiple operators, the server operates in parallel for each of the operators according to the functionalities below.
- the mapping of the farm includes a plurality of adjacent and/or disjoint agricultural plots 1.
- An agricultural parcel is generally polygonal, being bordered by rectilinear objects such as roads, hedges, etc..., or partially polygonal, when it is partially bordered by a natural element such as a wood, a watercourse or a body of water.
- the agricultural plot may in particular be a field dedicated to the cultivation of one or more plants, or a livestock plot.
- the agricultural plot may or may not include a building.
- the agricultural parcel is defined by its coordinates, in particular by the coordinates of the points of its perimeter or of certain remarkable points of its perimeter (corners).
- the coordinates in question can be defined in a geolocated system, such as the so-called “GPS” system, acronym for the English name “Ground Positioning System”.
- Each agricultural parcel is also characterized by an identifier configured by the user, such as, for example, an integer, or a character string, for example “Field 1; Field 2; Field 3; ... Breeding 1; ...”.
- the areas defined by the agricultural parcels taken together do not cover the entire surface. Indeed, in this area, there are also portions which do not correspond to agricultural plots such as, in particular, roads or paths, unexploited natural objects such as stretches of water, watercourses, unexploited woods, and unexploited buildings.
- the complementary zones 2 of the zones defined by the agricultural parcels taken together are characterized together by an identifier, such as, for example, "outside the parcel zones”.
- an identifier such as, for example, "outside the parcel zones”.
- outside plot areas we therefore refer to any point outside an agricultural plot as defined above.
- the combination of zones 1 of agricultural parcels and zones 2 "outside the parcel zone” paves the ground, within the meaning of the mathematical definition of "paving”.
- the configuration also includes the definition of one or more particular zones 3 associated with the agricultural operation. These particular zones are for example defined by geometric primitives independently of the topology of the ground. According to a example, a particular area is defined as a circle of configurable radius, centered on a particular point on the map. Other examples may include polygons of parameterizable size.
- the particular zone is also characterized by an identifier. For example, a particular zone 3 is defined around the farm office, and is characterized by the identifier “Headquarters”.
- zone 3 may, where appropriate, cover one or more zones defined by an agricultural parcel 1 , or a zone 2 identified as "outside parcel zones”.
- the installation further includes the configuration of portable systems 4 of the users.
- one user is described, but the configuration can also be carried out for several users on the same farm, or on various farms gathered in the same service.
- the user can be the operator. Alternatively, or in addition, a user can be an agricultural worker who is not the operator.
- the portable system 4 is a user's smart phone (referred to as a "smartphone").
- the “smartphone” is identified by an identifier which can, for example, be the call number of the “smartphone”. However, another identifier can be used, if necessary. The identifier is thus attached to the farm being configured.
- An agricultural activity is defined as a set of agricultural tasks.
- the agricultural tasks of an agricultural activity are distributed according to a schedule.
- Each agricultural task can be characterized by one and/or other of the following characteristics: an identifier, a duration, an agricultural plot identifier, a start time, an end time, a user identifier.
- an agricultural task “Pickup of female turkeys” is defined as being a task lasting eighteen hours, taking place in a “Breeding” place.
- an agricultural task “Unloading turkeys” is defined as being a task lasting 1 hour taking place in a place “Breeding”.
- An agricultural activity is defined from a plurality of agricultural tasks as defined above, distributed according to a schedule. For example, we can provide an agricultural activity, bearing the identifier “Elevage Dindes”, comprising an agricultural task
- the agricultural tasks of an agricultural activity can also be linked by their location.
- the "Livestock” of the agricultural step “Turkey female pickup” is the same agricultural parcel identifier as the “Breeding” of the “Turkey unloading” agricultural step.
- the agricultural holding includes several agricultural plots of the “Livestock” type, the agricultural activity “Breeding Turkeys” can be assigned to the agricultural plot “Breeding 1”, in which case the corresponding agricultural tasks are assigned to the agricultural plot “Breeding 1 ".
- An agricultural activity may thus include at least two agricultural tasks, as defined above, and typically at least five, or even at least ten agricultural tasks, as defined above.
- An agricultural activity can thus be represented, from a mathematical point of view, as a time series, i.e. for example a matrix comprising a certain number of vectors (“task identifier”; “plot ”), each vector corresponding to a time slice.
- a time series i.e. for example a matrix comprising a certain number of vectors (“task identifier”; “plot ”), each vector corresponding to a time slice.
- agricultural activities are defined, for example from predefined models of agricultural activities.
- an agricultural activity can be made specific to a farm by adjusting an agricultural stage, or by adjusting a relationship between agricultural stages of the same agricultural activity, for example an adjusted calendar compared to that of the model.
- the database 5 is configured to be able to receive and store information relating to the "smartphone", received by the communication module 6 from the central server 7.
- the “smartphone” contains a computer program relating to the service. If this computer program is not installed on the
- the commissioning button 11 of the computer program is automatically switched to "on", or the user controls this switch to "on” by an action, and the screen is then as represented in FIG. 3 according to an exemplary embodiment.
- the fact that the geolocation is in "on” mode is displayed on the screen of the "smartphone” by any appropriate means.
- the time supplied by clock 12 at this instant is recorded in the memory of the “smartphone”.
- the service requires the geolocation module 13 of the “smartphone” to be active. If necessary, the computer program checks that the geolocation module 13 of the “smartphone” is active and, failing that, informs the user or guides him to activate his geolocation module 13.
- the geolocation module 13 geolocates the “smartphone”.
- the location information of the “smartphone”, as well as the instant associated with this geolocation, determined by the clock 12 of the “smartphone”, are stored in the memory 14 of the “smartphone”. This step is for example implemented every second, every 10 seconds, every minute, or every 2 minutes.
- the user who is also the operator, switched the geolocation to "on" mode after his breakfast, which he took at his home, which is also the head office. of operation. He puts his “smartphone” in his pocket. He starts his day at the office for administrative procedures.
- the computer program can offer to cut off the geolocation module 13 of the “smartphone” and inform the user of this, or guide him to deactivate his geolocation module 13.
- the start and end of operation can be automatic, based on a pre-programmed start and end time via an interface.
- This communication is less frequent than the determination of geolocation, for example at least ten times less frequent. It is for example carried out once per hour, twice per day, or once per day. For example, it can be provided that it is carried out after the geolocation has been stopped at the end of the day by the user.
- the communication can for example take place when the “smartphone” finds to be in a coverage area of a network 16 allowing this communication. As an alternative or in addition, the communication can for example take place when the “smartphone” is placed in wired or wireless communication with a local router 17 having means of communication to the server 7.
- the data communicated includes the identifier of the “smartphone” as well as the associated location data and clock data.
- the data received by the communication module 6 from the server 7 is stored in the database 5. According to one embodiment, the “smartphone” does not carry out any pre-processing, so as to save battery power.
- the processing module 18 of the server 7 will process the data associated with the identifier.
- data is filtered prior to processing. It is possible to implement a computerized pre-processing module 27 which applies an extended Kalman filter.
- the identifier is associated with one or more farms.
- the location of the user can therefore be determined by comparing the location measured with the locations of the areas of the operation.
- the processing module 18 is able to identify an area within which the location of the user is located.
- a duration will be assigned to an area from the moment a sufficient number of consecutive location data are within the same area.
- the sufficient number can be determined either by a predefined number of measuring points or by a minimum duration. As an example, it is defined that 5 successive measurement points in the same zone are necessary before the processing module 18 considers that the measurement points belong to the same zone. Alternatively, 10 successive measurement points in the same area are required to confirm the area.
- the time spent in the zone is then calculated as the difference between the last instant spent in the zone and the first instant spent in the zone, and this duration is recorded associated with the identifier of the zone.
- Location data may also be processed to determine a type of user movement within the parcel. For example, according to the instantaneous speed of movement, and by comparison with pre-established models, one can assign the type of travel to an entry from a possible list of travel modes, which can include the entries "pedestrian”, “tractor”, “car”, ...
- the zone in question can be an agricultural parcel 1, or can be a zone 2 called "outside the parcel zones", in particular if the user spends time outside the farm (visiting a supplier outside the operation, or time spent on the road, for example).
- the processing module also determines time durations in transport zones by taking into account a succession of locations detected in different predefined zones.
- the processing module has determined a first duration of time in a first zone of agricultural parcel 1 or particular 3 "Headquarters”, and a second duration of time in a second zone of agricultural parcel 1 or particular 3 " Seat”, spaced between them by a certain duration of time in a zone 2 “outside plot zones”, said certain duration of time “outside plot zones” is determined as travel time.
- travel times are counted only if they correspond to transport related to the professional activity. Travel times that are not related to the professional activity are not counted. In particular, the transport times between a first agricultural parcel zone and a second agricultural parcel zone are counted. Also counted are transport times towards an agricultural parcel area from an area outside an agricultural parcel or from the head office, or from an area outside an agricultural parcel or from the head office to an area of an agricultural parcel. In particular, trips between an area outside the agricultural plot or the head office and an area outside the agricultural plot and the head office are not counted.
- the processing module determines pause time durations, corresponding to time durations where the “smartphone” is stationary, “stationary” being defined by reference to a predefined minimum of movement. This period of time of immobility is then not counted in the time spent in a zone corresponding to an agricultural parcel 1.
- the processing module 18 determines a time spent at the headquarters, a time spent in field #7, a time spent in field #3, a time spent in poultry farming #2, a transport time, and a time spent outside plot areas, as well as the start and end of each interval.
- the interpretation stage is implemented every day. It can be implemented each time the reception of location data indicates that a user has changed agricultural plots. This will be the case in particular if each task of an agricultural activity is associated with a single plot.
- the interpretation stage is implemented at a time when one already has a pre-populated calendar of agricultural tasks performed by users, as determined by previous implementations of the system. interpretation stage.
- the interpretation step comprises a classification of the temporal location data received, and an association of this data with an agricultural task.
- the association with an agricultural task is done for example by identifying a probable agricultural task corresponding to the temporal location data, and in particular a most probable agricultural task.
- This association is made by a computerized interpretation module 28 which digitally processes the temporal location data, the pre-filled calendars, and the agricultural activities defined for the farm.
- location data of the type: User #2 stayed between 8:17 a.m. and 12:14 p.m. in the agricultural plot "Elevage Dindes 3" can be processed as: Mr. Pierre Morin carried out between 8:17 a.m. and 12:14 p.m., i.e. for 3:57 a.m., the “Pickup Female Turkeys” stage in the agricultural plot “Elevage Dindes 3”.
- Mr. Pierre Morin carried out between 8:17 a.m. and 12:14 p.m., i.e. for 3:57 a.m., the “Pickup Female Turkeys” stage in the agricultural plot “Elevage Dindes 3”.
- This classification is made possible by the fact that, in the database, the identifier of the "smartphone" of user #2 is associated with "Mr.
- the likely farm task search matching the temporal location data may use interpreted or uninterpreted data associated with farm tasks, farm parcels, users, farm activities, and/or calendars.
- the computerized interpretation module 28 determines a most probable agricultural task for the temporal location data. For example, the computerized interpretation module determines, for a plurality of candidate agricultural tasks selected from among the available agricultural tasks, a distance between the agricultural activity model comprising this available agricultural task and agricultural activities constituted from the past agricultural tasks and information obtained via the time location data.
- the distance in question can be any suitable distance.
- temporal location data may only have a loose correlation with modeled agricultural activities. This may be the case in particular if the user starts a farming task earlier than expected in the farming activity models (for example to anticipate the fact that he will not be able to complete the farming task at the desired time, or because the climate pushes him to implement this agricultural task in advance), or later than expected in the models of agricultural activities (for example due to a punctual activity load).
- the computerized interpretation module can also take into account other agricultural activities of the calendar to interpret the current agricultural task.
- the computerized interpretation module takes into account the fact that an agricultural activity "breeding turkeys" has recently ended in the plot "Elevage Dindes 3" to determine that a new agricultural task in the plot "Elevage Dindes 3" corresponds to the first agricultural task of a new agricultural activity "Breeding Turkeys" in this plot.
- external data is meant data from data sources 29 external to the farm.
- a category of outdoor data particularly relevant to certain embodiments of the invention includes weather data relating to the location of the farm.
- the meteorological data may in particular comprise the existence, or even the intensity of rain during a given period. Indeed, the rain can have a impact on the practicality or ease of carrying out certain agricultural tasks.
- the meteorological data can be entered into the models of agricultural tasks: it is indicated that certain agricultural tasks can only be implemented in dry weather, or preferably in dry weather.
- the computerized interpretation module 28 receives meteorological data relating to the agricultural plot from the external data source.
- the interpretation module additionally takes into account the meteorological data to determine the agricultural task implemented.
- the weather data may include ambient temperature data.
- the interpretation module can be adapted to analyze meteorological data and deduce an index of allocation of agricultural activity schedules. Meteorological data is taken into account in the form of a modulation of the calendar of agricultural task models.
- the computerized interpretation module 28 may comprise a classifier 30 relating to the models of agricultural activities defined for the agricultural operation.
- the computerized interpretation module 28 may include a classifier 31 relating to the models of agricultural activities defined for all of the agricultural holdings.
- the computerized interpretation module 28 implements these two classifiers 30, 31, and a selector module 32 applies a computerized selection step making it possible to determine an agricultural task from the results of the two classifiers.
- This selection is for example carried out by using a level of confidence in the result provided by each classifier. Indeed, as each classifier determines an agricultural task likely to be associated with the collected data, a probability of association can be determined simultaneously. This probability of association is used for selection.
- the selection history 34 for the user or the operation can also be used for the selection.
- the system which has just been described may also comprise a computerized learning module 33.
- the computerized learning module 33 is used, from time to time, to determine patterns of agricultural activities from the information of agricultural activities collected by the server relating to the agricultural operation.
- the computerized learning module 33 is suitable for determining agricultural activity models from agricultural activity information collected by modifying pre-existing agricultural activity models from the collected agricultural activity information.
- the computerized learning module 33 is specific to the farm. In doing so, the specificities of the farm are taken into account.
- the computerized learning module can be common to several farms, so as to collect information on agricultural activities from several farms, and to take into account all of this information for the modification of the models. . In doing so, a model of agricultural activity that is statistically more common to all farms is determined.
- the classification is performed by a convolutional neural network.
- the invention proposes to reduce the technical problem of classifying the time series of data collected to a technical problem of classifying an image representative of this series for which the interpretation module 28 uses a convolutional neural network.
- the first step consists in creating the associated image from the data collected .
- the constructed image represents the location data collected to which other information specific to the time period covered by the location data collected is added.
- Each image is constructed by superimposing several layers. These superimposed layers construct the image.
- Figures 8 and 9 illustrate examples of constructed images.
- From the location data to be interpreted it is possible to represent the GPS track on the image, ie the operator's trajectory. Ideally, it is possible to vary the intensity of the locate point according to the corresponding timestamp. In this way, the direction of the trajectory is represented on the image and the speed of movement also.
- the GPS track can be drawn in levels of white on all the other layers of the image or be the subject of a specific layer with a specific light intensity and tint. .
- the other layers make it possible to represent other information specific to the time period over which the time-stamped location data collected is spread. This information can come from or be deduced from location data and databases of farms and agricultural activities or represent external data.
- Each layer of the image has an intensity and a hue. This intensity is determined from the information to be encoded on the layer.
- each layer is formed by a tessellation according to two different intensities of a given hue, for example forming a checkerboard.
- the interest is to obtain a greater number of information on each layer. Indeed, a single intensity can simply vary from 0 to 255 which offers 256 possibilities while a checkerboard based on two intensities allows 256x256 possibilities. In this way, it is, for example, possible to assign to each type of plot a pair of intensity
- each agricultural task corresponds to an intensity of one shade, or a pair of intensity of one shade to, for example, paving layer.
- the representation in the image of the previous task is then carried out with the corresponding intensity.
- each plot identifier is associated with an intensity or an intensity couple.
- the outline of an agricultural parcel determined according to location data can therefore be integrated into the image.
- This outline can be integrated into any layer provided that it is distinguishable from the information already encoded on it.
- it can be integrated into a layer according to an intensity other than the one(s) already used.
- the contours can be in black. Such contours are visible in the constructed images shown in Figures 8 and 9.
- each value of the data or information to be encoded is associated with an intensity (or a pair of intensity),
- a pattern generated from a geometric primitive is associated with these data and parameterized according to the values taken by them.
- the encoding layer is then generated according to the determined associations and, if applicable, the determined parameterization.
- the activity-specific data gathered on the same layer can be different but linked, or correlated, between them: for example, the paving of a layer can be determined according to the last task carried out in the agricultural plot and the pattern generated from a geometric primitive can be parameterized according to the time elapsed since this last task.
- the two pieces of information encoded on the layer are different (last task and time elapsed since this one) but linked together.
- Example data refers to data from data sources 29 external to the farm. The objective being to encode the maximum of information resulting from the data collected on the image so that this one is the most representative of these, certain layers of the image can be built from these external data.
- a category of outdoor data particularly relevant to certain embodiments of the invention includes weather data relating to the location of the farm.
- the meteorological data may in particular include the existence or even the intensity of rain during a given period.
- Data from other farms may be relevant to determine the agricultural task performed during a time period given by the user. For example, during the harvest season, it is very likely that operators in the same geographical area harvest at very similar times. Similarly, sowing periods for farmers, or foaling for horse breeders generally have specific periods and durations in the year. Thus, allowing the classifier to take such information into account is useful.
- the external data that can be encoded on a layer can in particular be:
- FIG. 10 illustrates an example of classifier training results.
- the “Task” task column indicates the known task whose temporal zoning data is submitted to the classifier so that it associates them with an agricultural task.
- the “AI Task” column indicates the result of classification by the classifier. To classify location data, the classifier creates an image shown in the “Image” column.
- the constructed images include an outline of the agricultural plot, for example "Large field", and a GPS track from the location data collected.
- the task associated with the location data is “Herbicide treatment” (“IA task” column) while the known task is “Rolling” according to the “Task” column.
- This association by the classifier is an example of erroneous results that allow training the classifier.
- each image corresponds to a day
- the classifier has for each image a sequence of tasks performed during the day.
- the training of the classifier can be updated as soon as new tasks are assigned to images (for example at each classification, or even at each manually confirmed classification) and/or the classifier can be trained at specific times on the database of new data (specific to the farm or not) from which it is possible to construct new images associated with known agricultural tasks.
- the known tasks used to train the classifier can come from other exploitations. In this way, it is possible to use verified results from other farms, ie images associated with known tasks performed in other farms. This can be useful for the first training of the classifier of a given exploitation. But it can also make it possible to take into consideration the characteristics common to similar farms (geographically or by sector of activity, for example). [184] It is possible to test the classifier by submitting to it images for which the task to be assigned is known as explained above. We then see if the results are satisfactory. Similarly, the test images may come from other operations.
- the interpretation step is implemented according to a predetermined duration of data collection, or even a minimum amount of data collected or simply at any time, it is possible to represent all the data on a single image and to train the classifier to recognize the successive tasks performed during the data collection period.
- the image can contain several plots on the layer representing the plots and for each plot represented, the layers of zones and tasks preceding represent the information of previous zones and tasks for each plot by varying the intensities or pairs of zone or task intensities from one plot to another.
- the interpretation step be carried out for a batch of location data corresponding to a given parcel or that each change of parcel detected according to the location data leads to the creation of a new image.
- the constructed images of Figures 8 and 9 illustrate this embodiment.
- the batch of data collected to be interpreted agricultural parcel is sequenced by agricultural parcel.
- a sequencing by plots of the batch of data collected to be interpreted is carried out.
- an image is constructed for each batch of data corresponding to an agricultural plot.
- zones 1, 2 and 3 can help with this sequencing since the particular zones 3 correspond to locations outside agricultural plots.
- an image corresponds to an agricultural plot on the farm and the classifier aims to assign the image to the agricultural task most likely carried out in the plot.
- each image corresponds to a specific parcel
- the previous examples are simplified: on each image, one layer corresponds to the previous task carried out in the parcel, one layer corresponds to the type of zone to which the parcel belongs , a layer corresponds to the identifier of the parcel.
- the pattern for example, a circle, represents the time elapsed since the last task carried out (parameterized according to the diameter of the circle) and finally, we integrate the GPS track of the trajectory (included in the plot therefore) and the contour of the parcel.
- Figure 13 illustrates the data manipulated by the classifier. Based on the location data collected, the computerized interpretation module determines the farm, the plot, the time spent in the plot.
- the user is determined by the identifier of the autonomous portable device.
- the neural network classifier is able to classify each image: for each input image of the classifier, it assigns this image the most probable agricultural task.
- each line corresponds to a version of the classifier (“model” column).
- the “Success percentage” column indicates the performance of the classifier based on the classification results of the model collected in a results database (“Export name” column).
- the classifier makes it possible to recognize the task carried out in each plot and for how long over a predetermined period of time.
- the time period is the year 2021. Nevertheless, it could be a day or a week so that it is possible to know its schedule thanks to the classifier.
- the identified tasks are entered by the classifier.
- the classifier determines a most likely performed task that it proposes to the user.
- the user can access an editing interface, allowing him to check or even edit the data entered. This can be useful especially if the user needs to report an unexpected activity, such as a phone call, a breakdown, or other.
- the editing interface can be available from menu 19 of the "smartphone" computer program interface (see Figure 3), via a web interface accessible from a microcomputer, and allow the data to be modified at "smartphone" level before sending to the server, or at the server level 7.
- the server sends to the "smartphone" information relating to the agricultural task determined by the interpretation module, and the "smartphone” presents, on a graphic interface, the determined agricultural task, and offers the user the possibility of confirming or not the determined agricultural task.
- the information entered by the user is sent back to the server, which can take it into account for the definitive recording of an agricultural task 35 in the user's calendar.
- the user may be asked to enter manually in a list the agricultural activity actually carried out.
- This manual validation step is preferably carried out regularly, for example daily, so that the agricultural tasks actually carried out are taken into account the next day by the interpretation module.
- the areal extent of menu 19 is small relative to the areal extent of power button 11. As a result, power button 11 is the main interface of this screen.
- the tasks classified but not confirmed can for example be of less weight than the tasks classified and confirmed manually and the tasks entered manually.
- the operator can access the processed data from a supervision interface 20. It will be noted that the operator can be the user but that, in certain cases, all users do not have access to the supervision interface 20. Access to the supervision interface 20 can be managed by authentication systems. The operator can thus access the information recorded for one or more users associated with its operation, including itself, if necessary.
- Access is for example made from a personal microcomputer 21 or other electronic device connected to the server 7 via a page accessible via the Internet network 16.
- the operator can for example access, for a given user, all the lines processed for the user.
- the information can also be presented in a non-chronological manner, for example accumulated over a configurable period, over one or more users and/or one or more zones.
- the operator can thus determine in particular the overall time spent in a particular area, the overall time spent outside the area, the overall time spent at headquarters, the overall time spent on break and/or the overall time spent traveling, over a period configurable, and/or the evolution of these quantities over time, as shown for example in Figure 5.
- the method whatever the embodiment, is not necessarily implemented by a “smartphone” of the user.
- a box dedicated electronics presenting the necessary functionalities.
- the box may comprise a simplified man-machine interface comprising a mechanical on/off button.
- Part of the methods described above can be implemented, whatever the embodiment, by computer programs executed on one or more processors.
- Several objects equipped with processors can work in a network, the steps of the method can be implemented by one or the other, or a plurality of processors communicating with each other.
- a given agricultural task may require the simultaneous presence of several users.
- the computerized interpretation module 28 determines the agricultural task from the time location data received from several portable systems.
- some agricultural tasks can be modeled in man units. hour rather than in hourly units.
- the interpretation module is able to recognize it, whether it is implemented by a user for about 18 hours (spread over several working days), or according to more complex organizations (two users for about 9 hours , or even one user for 9 hours then two users for 4.5 hours, or other scenarios).
- the interpretation module 28 determines the agricultural task by taking into account the type of estimated movement of the user, if the agricultural activity models are parameterized with the type of displacement.
- an agricultural machine of a farm is equipped with a device having similarities with the portable system of the users.
- This device stores an identifier of the agricultural machine, comprises a geolocation module making it possible to determine the location of the machine, and communication means enabling it, at regular intervals, to send information concerning its temporal location to the server.
- Agricultural tasks can be modeled to take into account the use of a type of agricultural machinery. For example, the agricultural task "Labour" is modeled to take into account the use of a tractor.
- the farm operator can enter the identifiers of the various agricultural machines on the farm equipped with time tracking devices, as well as the type of these agricultural machines. For example, if the operator has several different tractors, each assigned to a remote identifier, each can be entered in the "tractor" category.
- the computerized interpretation module 28 determines the agricultural task from time location data received jointly from a portable system 4 worn by a user and time location data from an agricultural machine.
- the simultaneous presence of an agricultural user and an agricultural machine on a plot are taken into account by the computerized interpretation module 28 to recognize the agricultural task.
- Some data processors compare a time spent in a plot on a date determined according to the location data collected with a pre-filled timetable and then deduce from this comparison the agricultural task carried out according to the location data collected .
- One specificity of the data processing according to the invention is therefore not to compare the location data, or data calculated from it, with model data.
- the processing of location data according to the invention uses only the temporal links between the agricultural tasks of one or more agricultural activities according to the models of agricultural activities defined to deduce from the location data the agricultural task performed.
- Communication module 26 computerized pre-processing module 27 computerized interpretation module 28 data sources 29 external classifiers 30, 31 selector module 32 computerized learning module 33 history 34 agricultural task 35
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EP22702977.4A EP4285614A1 (fr) | 2021-02-01 | 2022-02-01 | Système et procédé informatisé d'interprétation de données de localisation d'au moins un travailleur agricole, et programme d'ordinateur |
CA3206924A CA3206924A1 (fr) | 2021-02-01 | 2022-02-01 | Systeme et procede informatise d'interpretation de donnees de localisation d'au moins un travailleur agricole, et programme d'ordinateur |
AU2022212646A AU2022212646A1 (en) | 2021-02-01 | 2022-02-01 | Computerised system and method for interpreting location data of at least one agricultural worker, and computer program |
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US20160283887A1 (en) * | 2015-03-26 | 2016-09-29 | Tata Consultancy Services Limited | System and method for agricultural activity monitoring and training |
EP3239897A1 (fr) * | 2016-04-29 | 2017-11-01 | Fujitsu Limited | Procédé et appareil pour déterminer la similitude entre des ensembles de données à plusieurs variables |
EP3451231A1 (fr) * | 2017-08-31 | 2019-03-06 | Fujitsu Limited | Imagification de données à plusieurs variables |
US20200045873A1 (en) * | 2017-04-18 | 2020-02-13 | CropZilla Software, Inc. | Machine Control System Providing Actionable Management Information and Insight Using Agricultural Telematics |
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US20160283887A1 (en) * | 2015-03-26 | 2016-09-29 | Tata Consultancy Services Limited | System and method for agricultural activity monitoring and training |
EP3239897A1 (fr) * | 2016-04-29 | 2017-11-01 | Fujitsu Limited | Procédé et appareil pour déterminer la similitude entre des ensembles de données à plusieurs variables |
US20200045873A1 (en) * | 2017-04-18 | 2020-02-13 | CropZilla Software, Inc. | Machine Control System Providing Actionable Management Information and Insight Using Agricultural Telematics |
EP3451231A1 (fr) * | 2017-08-31 | 2019-03-06 | Fujitsu Limited | Imagification de données à plusieurs variables |
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JAFARI ALI ET AL: "SensorNet: A Scalable and Low-Power Deep Convolutional Neural Network for Multimodal Data Classification", IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I: REGULAR PAPERS, IEEE, US, vol. 66, no. 1, 1 January 2019 (2019-01-01), pages 274 - 287, XP011699683, ISSN: 1549-8328, [retrieved on 20181206], DOI: 10.1109/TCSI.2018.2848647 * |
JIANG WENCHAO WJM84@MST EDU ET AL: "Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks", MULTIMEDIA, ACM, 2 PENN PLAZA, SUITE 701 NEW YORK NY 10121-0701 USA, 13 October 2015 (2015-10-13), pages 1307 - 1310, XP058509796, ISBN: 978-1-4503-3459-4, DOI: 10.1145/2733373.2806333 * |
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