WO2019130147A1

WO2019130147A1 - Method for diagnosing and monitoring a target element of a farm

Info

Publication number: WO2019130147A1
Application number: PCT/IB2018/060045
Authority: WO
Inventors: Laurent Gineste; Guillaume Blanc; Julien Coudon
Original assignee: Exotic Systems
Priority date: 2017-12-27
Filing date: 2018-12-13
Publication date: 2019-07-04
Also published as: FR3075565A1; FR3075565B1

Abstract

A system (1) for monitoring and alerting a target natural element (20) of a farm, comprising a platform (2) for diagnosing the target element (20) comprising a plurality of diagnostic sensors (3) and a module (4) for recording and analysing parameters, a pointer module (6) designed to receive an input indicating that a target state of the target element (20) is ongoing or about to occur, a platform (10) for monitoring said target element comprising a plurality of monitoring sensors (13) and a monitoring module (11) designed to monitor the data with respect to the physical conditions associated with the evolution of said target element (20) and/or of its environment (21).

Description

Method for diagnosing and monitoring a target element of a farm

TECHNICAL FIELD OF THE INVENTION

The present invention relates to a monitoring system and alert for a natural target element of a farm, comprising a platform for diagnosing the target element and a monitoring platform.

STATE OF THE PRIOR ART

Despite the ever increasing level of complexity of agricultural machinery, the operation of a farm or agricultural business remains a complex and demanding task. The operator is particularly challenged by a large number of parameters and events to monitor and control so that he can make the right decisions in relation to the daily tasks to be performed. Monitoring requires it to move continuously from plot to plot to know the precise situation at each key point of the operation. Farms are getting larger, and often gathering several parcels often distant, the farmer spends much of his time to make trips in order to learn. These information phases, although essential for effective management and organization, are as such unproductive and time-consuming.

In addition, the operator must regularly make decisions that often have serious consequences, both on the quality of the resulting products and on the management of time and tasks at the farm level. The farmer or rancher must be able to prioritize his decisions. However, very few effective tools exist today for this purpose.

[0004] For example, diagnostic methods are known that make it easier for the farmer to detect the state of health of plants or certain infections that may affect crops.

For example, the document FR2663192 discloses a method of early diagnosis of infection of cereal seedlings by a fungus, in particular Pseudocercosporella herpotrichoids, and a device specifically designed for the implementation of this method. The method involves forcing the seedlings of cereals in a controlled atmosphere under conditions that ensure simultaneous growth of the fungus and the parasitized plant. The described method applies to the field of protection of plants and more particularly to cereal farming. This method seems efficient but is entirely manual. On the other hand, it requires prior knowledge of the phenomena sought.

Document FR2970621 describes a method which consists in analyzing branches in a vineyard sample by determining the concentration of macro-elements and trace elements. An equilibrium ratio is determined between the concentration of the macro elements and the values of the trace elements. The data obtained for the extracted extracts of conductive tissues and for branches at reference values for a given type of vine are compared. A state of the corrections to be provided to maintain a nutritional state of the vines of the vineyard is achieved. This method seems effective, but is limited to establishing a diagnosis.

Also known soil moisture detection systems, to help the farmer who must decide when and how to water his crops.

[0008] US2017038749 discloses a precision agriculture irrigation modeling framework using a combination of meteorological data, crop data and other agricultural inputs to create customized agronomic models for diagnosing and predicting the state of the crop. moisture of a field, as well as the need for irrigation. Specific combinations of various agricultural inputs can be applied, as well as meteorological information to identify or adjust the water characteristics of crops and soils, to model optimal irrigation activities and provide advice, recommendations and guidelines for an application targeted artificial rainfall. This device is particularly focused on diagnostic phases and recommendations, only in the field of agricultural irrigation.

Also known devices or methods for simplifying the use of certain agricultural machinery. For example, the document FR2764401 describes a method for automating recurring tasks to be implemented with an agricultural machine such as a tractor. The method is based on the memorization of the actions activated by the operator, and the subsequent replay of the control signals that activate these operations. Storage can be done in advance by the vehicle manufacturer. The shares are classified as primary and secondary shares. The relative magnitudes of the actions are stored with the state of the vehicle associated with the actions. Non-representative actions can be filtered. Secondary actions can be synchronized. Stored actions can be displayed and edited, and actions can be interrupted during execution and repeated. A computer is used to memorize actions and to control operation and display. This process is in direct relation with the machine to which it is attached, makes it possible to record and repeat trivial actions, but without making it possible to establish a diagnosis or analyze the tasks performed.

In all these examples, no document discloses a solution for establishing a precise and exhaustive diagnosis of an element based on the values of the physical parameters to characterize this element during its evolution and more particularly to when this system goes to a particular state. Furthermore, no system makes it possible to simply and reliably monitor certain elements of a farm in order to detect the occurrence of one or more particular states.

WO2016189524 discloses a livestock monitoring system designed to record a series of physical parameters of the animals to be monitored using sensors placed on the cattle, send these data to a monitoring system programmed to detect the conditions of the animals. alerts corresponding to pre-established configurations requiring intervention with the animals concerned. In order to be able to detect an alert condition, not only do physical data from the sensors on the cattle have to be received, but also the conditions corresponding to alert situations must first be known. The monitoring system can thus compare the received parameters with the situations requiring alerts. For any new situation or new alert, the user must himself know the parameters corresponding to a critical state, and provide a new monitoring system adapted to these new parameters. This system is intended to monitor situations for which the user knows the parameters to monitor.

US2016150744 describes a system designed to apply pesticides on a crop. Specifically designed detectors make it possible to monitor the previously identified parameters corresponding to a situation in which the application of pesticides is to be triggered. As before, the system requires prior knowledge of the parameters to be monitored. When the thresholds are reached, the system triggers a pesticide application mode.

To overcome these disadvantages, the invention provides different technical means. SUMMARY OF THE INVENTION

A first objective of the invention is to enable a precise and exhaustive diagnosis to be made comprising all the parametric values relating to an element to be monitored during its evolution and more particularly at the moment when this element passes to a specific parameter. particular state, this particular state being useful for carrying out a task of the farm.

Another object of the invention is to provide a method for monitoring the state of an element to identify and / or anticipate the occurrence of this particular state using parameters easy to measure and monitor .

Another object of the invention is to provide a method for providing a farmer automatically and in the long term, status data of one or more elements of his farm.

MONITORING AND ALERT SYSTEM

To do this, the invention provides a system for monitoring and alerting a target element for growing or rearing a farm, comprising:

i) a diagnostic platform of the target element comprising a plurality of diagnostic sensors and a centralized module for recording and analyzing parameters designed to accumulate for a limited time corresponding to a diagnostic phase of said target element of state data of the target element according to its evolution;

ii) a pointer module adapted to receive a pointer input ("flag") indicating that a target state to be monitored of the target element is being or about to occur, and a data identification module corresponding to said target state of the target element;

iii) a platform for monitoring said target element, distinct from the diagnostic platform, and comprising a reduced number of detectors with respect to the diagnostic platform and a monitoring module designed to monitor for a duration corresponding to a monitoring phase of said element targets the state data of the target element and / or its environment, the monitoring platform also comprising an alert module designed to generate an alert condition when the parametric data obtained by the monitoring platform are in correspondence with the data of said target state identified by the pointer module.

Such a system allows, during a diagnostic phase, to acquire a wide range of physical state data of a system. The system provides a platform for complete diagnostic, powerful and allowing a very large number of data captures, to allow to ensure that the exact values corresponding to the phenomenon to be monitored are captured and taken into account. The diagnostic platform is designed with criteria of comprehensiveness and precision predominant on the criteria of energy saving, or of autonomy or durability, which are them priority at the level of the platform of surveillance.

These data acquired by the diagnostic platform make it possible to acquire a knowledge of the physical element of the target element, and more particularly a detailed and exhaustive knowledge of the physical state data as a function of the evolution of the device. target element.

The pointer module makes it possible to establish a precise and reliable link between the occurrence of the phenomenon sought or the event to be monitored and the physical values of the state of the target element at this precise moment, and possibly a little before and / or after.

The data in relation to the pointer can be used to determine a surveillance approach different from that conventionally used (generally manual and approximate) to perform monitoring of the target element. The user is thus able to set up reliable, accurate and durable automatic monitoring, using a simple monitoring platform configured for long-term operation in hostile environments, under variable conditions and often difficult. The monitoring phase is thus based on a reduced number of parameters, corresponding to the most significant parameters of the target state sought.

Advantageously, the number of detectors of the monitoring platform is at least 50% less and more preferably at least 70% less than the number of sensors of the diagnostic platform.

This large difference between the number of sensors for the diagnosis and the number of detectors for monitoring makes it possible, on the one hand, to make the diagnostic phase more reliable by taking into account a large number of parameters, without seeking to eliminate at this stage, not knowing which ones will be the most significant, and on the other hand to simplify and greatly reduce the implementation of monitoring, with a simple, inexpensive installation, suitable for monitoring over time.

In an advantageous embodiment, the parameters used for the diagnosis and / or monitoring are selected from: pressure, temperature, humidity, wind speed, brightness, UV index, mass, force, torque, current, voltage, magnetic flux , a bit rate, an image (with image recognition), a sound level, a color, a reflectivity, a concentration of a gas or other element, an impedance (especially on the ground).

According to an advantageous embodiment, the pointer module is adapted to communicate remotely with the latter.

This embodiment makes it possible to set up a pointer module (or "flag") at any location, preferably to facilitate the occurrence of the target event, while the diagnostic platform is advantageously placed at the same time. shelter, in a place facilitating the work of an operator.

Advantageously, the diagnostic platform and the pointer module each comprise a clock adapted for the timestamp of the data, and a clock synchronization module is provided (either at the level of the diagnostic platform and / or at the level of the pointer module).

This implementation makes it possible to ensure that the timestamp data of the pointer module and the sensors of the diagnostic platform are perfectly synchronized, in order to ensure that the correct data are used during the analysis. step of the score.

In a version comprising an artificial intelligence module, the diagnostic platform includes a self-learning module (for example based on artificial intelligence), designed to automatically identify the data of the corresponding specific physical parameters. passing said target element to the target mode.

METHOD OF DIAGNOSIS AND MONITORING

The invention also provides a method for diagnosing and monitoring a target element for growing or rearing a farm using a monitoring and warning system as previously described, comprising the following steps:

PHASE 1

i) setting up a diagnostic platform, said platform comprising a plurality of diagnostic sensors cooperating with said target element or its environment;

ii) acquisition and recording of system diagnostic data using the a plurality of sensors and a centralized recording module;

PHASE 2

iii) in case of occurrence of a target state of said target element, input of an identification data (flag) of said target state of said target element;

iv) identifying parameters and threshold values corresponding to the transition to said target state;

PHASE 3

v) setting up a monitoring platform, distinct from the diagnostic platform, and operating on the basis of the parameters and threshold values corresponding to the transition to said target state;

vi) setting up the parameterized monitoring platform, said platform comprising a reduced number of surveillance sensors with respect to the diagnostic platform, said monitoring detectors cooperating with said target element or its environment;

vii) monitoring of the target element and / or its environment by the monitoring platform;

viii) in case of correspondence between the parametric data obtained by the monitoring and the threshold values of passage towards said target state, issuing an alert.

The three-phase process allows first of all a complete and exhaustive acquisition of a vast amount of data, able to identify the parameters of a specific state of the target element of a farm. It then makes it possible to correlate, thanks to a pointer (or "flag"), a specific state sought or monitored with the values corresponding to this state for each of the parameters. Finally, it allows to set up a surveillance system, simple, reliable, and effective, strictly based on the specific state parameters identified.

The method makes it possible, once the specific state parameters have been identified, to monitor the target element and to anticipate a situation in which the specific state is highly likely to occur again. The farmer has the technology to be alerted to anticipate specific states for which urgent action is required. The monitoring phase is based on a reduced number of parameters, corresponding to the most significant parameters of the target state sought.

These parametric data are certain and accurate which allows for reliable monitoring. In an alternative embodiment, the step of identifying data parameters corresponding to said target state allows the identification of pre-alert data.

The pre-alert allows to warn the operator sufficiently early to allow intervention prior to the occurrence of the specific state.

In an advantageous embodiment, the diagnostic and monitoring method further comprises a step of synchronizing the clocks.

This step makes it possible to ensure that the timestamps of the data of the diagnostic platform and the data of the pointer are perfectly synchronized, thus enabling the pointing step to give rise to the identification of the state data. closer to the target state.

According to another advantageous embodiment, the diagnostic method furthermore provides at least one post-alert monitoring step. For example, post-alert surveillance includes monitoring the application of curative treatment. Such a step makes it possible to ensure that the treatment provided for in the event of an alert is applied appropriately. In the case of a spreading of curative product for example, detectors previously parameterized during the parameterization phase make it possible to monitor the quantity of product which reaches the zone to be treated, to indicate whether the level of product is sufficient or not, or even applied in excess.

According to yet another advantageous embodiment, the post-alert monitoring comprises a monitoring of a target state corresponding to healthy cultures. Thus, when the situation is returned to a normal state, that is to say with healthy crops, the operator is informed and can for example put an end to curative treatments.

DESCRIPTION OF THE FIGURES

All the details of embodiment are given in the description which follows, supplemented by FIGS. 1 to 4, presented solely for purposes of non-limiting examples, and in which:

FIG. 1 is a schematic representation of an example of a system for monitoring and alerting one or more target elements of a farm;

FIG. 2 is a functional flowchart with the main steps of an exemplary monitoring and alerting method for a target element of a farm; FIG. 3a is an example of a diagnostic and monitoring table for an example of a target element corresponding to a measurement of the moisture level of cultured grains;

FIG. 3b is a variant of the table of FIG. 3a in which only the relevant data have been kept in order to simplify or lighten the monitoring phase;

FIG. 4 is an example of a diagnostic and monitoring table for an example of a target element corresponding to the lift of the ground.

DETAILED DESCRIPTION OF THE INVENTION

DEFINITIONS

By "target element" or "target natural element" means an element in relation to a crop or a farm on a farm. This may be for example a cereal, a fruit, a vegetable, livestock or used for the production of milk or eggs. It can also be a part of one of these elements, such as a grain of a grain, a leaf of a tree, etc. The target natural element also includes environmental elements in which the object of exploitation evolves, such as soil, water, air, light, wind, etc. These examples are given for illustrative and not limiting. These elements can be essential factors in a farm operation. In some cases, they represent the very object of exploitation (for example the production of cereal grains), or in other cases harmful elements such as pests or diseases that one wishes to detect at most. early. These elements are usually monitored by the operators, usually manually. The farm elements are monitored to determine, for example, a level of maturity for harvesting or harvesting. Environment elements are monitored to adjust farm parameters, such as watering, adding fertilizer, etc., or to monitor the quality of a farm vector, such as for example the hardness of the soil.

By "sensor" is meant a device for transforming a physical quantity into a size that can be associated with a unit of measurement.

By "detector" is meant a device for monitoring that a physical quantity approaches or reaches a threshold corresponding to a determined value of a parameter to monitor.

DIAGNOSTIC SYSTEM AND MONITORING

Figure 1 is a schematic representation of an exemplary system 1 for monitoring and alerting a target element 20 of a farm. In this example, the surveillance and warning system 1 comprises a diagnostic platform 2 comprising a plurality of sensors 3 connected to a recording and analysis module 4. Finally, a database 5 of the parameters of use of the element target keeps the data received during the diagnostic phase.

A pointer module 6 allows a user (or a monitoring device for example) to enter a pointer (or "flag") when a target event occurs. Several pointers 6 may possibly be entered if the user (or other monitoring module) is able to determine the instants preceding the occurrence of the target event. Alternatively, the pointer 6 is provided outside the diagnostic platform, remote from it. This module 6 makes it possible to precisely identify the values provided by the various sensors 3 when the target state occurs and / or is in progress. Each parameter is pointed and / or marked to recognize when the target state occurs. Optionally or alternatively, several types of pointers (or "flags") are provided, for example a type for a target event, a type for normal operation, a type for an unforeseen event, etc.

A data identification module 7 corresponding to the target state (that pointed by the pointer module), is also provided, in relation to a database 8 of the parameters of the target state. Alternatively, the databases 5 and 8 may be arranged in a single base.

As an option, the diagnostic platform 2 may comprise a self-learning module 9, provided either to assist an operator during a diagnostic phase, for example by proposing ranges of evolution where a target event could potentially occur, or to enable a diagnosis to be made automatically.

The diagnostic platform 2 is designed so that a user can easily set up a wide range of sensors, while promoting records of a very large number of data. Since, before the diagnostic phase, the operator does not know which parameters are the most relevant, nor the values of these parameters, the architecture of the diagnostic platform makes it possible to use different types of sensors and to record many values for all of these sensors.

Once the diagnostic phase is completed, the operator will know the most relevant parameters and may therefore recommend a specific architecture, more targeted, for the monitoring phase. The monitoring platform, which often has fewer types different detectors, is thus less expensive. This platform is architected with the aim of promoting low energy consumption, less frequent transmission of data, and the implementation of optimized technological elements for a reliable and long-lasting monitoring installation.

Thus, as illustrated in the example of FIG. 1, a monitoring platform 10 comprises a plurality of detectors 13 connected to a monitoring module 11, in relation with the data 12 of the parameters of the state. target. The number of detectors 13 of the monitoring platform is at least 50% less and more preferably at least 70% less than the number of sensors 3 of the diagnostic platform. An alert module 14 generates a signal or alert when the actual data observed converge or are in correspondence with the data of the target state.

The alert is intended to warn the farmer, and / or another person or entity, of the next or current occurrence of the target state according to the settings previously made.

DIAGNOSTIC PRODUCT AND MONITORING

FIG. 2 illustrates, using a functional flowchart, an example of a monitoring and warning method advantageously adapted for implementation with a diagnostic and monitoring system as previously described in connection with FIG. Figure 1. In the illustrated example, the process is subdivided into three phases.

First, in step 100, a setting up of a diagnostic platform 2 is performed, with installation of different sensors, for example humidity, temperature, wind speed, etc. . The selected and installed sensors are related to the target element 20 and / or its environment, depending on the parameters to be measured. In this way, the data collected is relevant to the target element. However, as previously indicated, since the most relevant parameters are unknown or uncertain before carrying out the diagnosis, the operator advantageously envisages a greater number of sensors, to avoid neglecting a possible parameter that could be relevant for perform effective monitoring.

In step 101, an acquisition of the data of the sensors is carried out. The captured and recorded data correspond to the evolution of the target element and its environment over time. The state data is preferably stored before the desired target state during this state and, if necessary, after this state. The second phase of the method corresponds to the marking of a target state. The first step (step 103) is the referencing of the target state, when it occurs, using the pointer module 5. This step corresponds to the entry of an identification data (or "flag") the desired target state. At the precise moment of the occurrence of this state and / or when it is in progress, each of the data of the various sensors are recorded and marked in order to distinguish the data pointed from the other data recorded before the occurrence of the target state and after that -this. This marking makes it possible to obtain (in step 104) all of the precise values of the data relating to the occurrence of the target state. The operator, or a selection module, can then identify the significant parameters and the threshold values corresponding to the target state sought. It is advantageous to select the parameter or parameters that make it possible to discriminate the target state from all the available parameters and the target values of these parameters indicating the target state.

The third phase of the method is that of the monitoring of the target element 20 by means of the monitoring platform 10.

In step 105, a monitoring platform 10 is parameterized, based on the parameters and threshold values previously identified in step 104. In step 106, the parameterized monitoring platform 10 is set up in relation to the element to be monitored and / or its environment, according to the parameters identified in step 104.

The monitoring can then be implemented at step 107. The monitoring platform 10 makes it possible to monitor the evolution of the parameters measured by the detectors 13 put in place to determine when the target state risk of occurring or actually occurring.

If there is convergence between the data recorded during the monitoring and the threshold values, an alert to prevent the operator is issued in step 108. This alert can be issued sooner or later depending on the setting performed upstream. In certain cases where this may prove useful, and as far as possible, the threshold values used allow the issuance of an alert sufficiently in advance to enable the operator to anticipate the occurrence as best as possible. the desired target state.

SYNCHRONIZATION OF CLOCKS

The method comprises an optional step indicated in step 102. This step is useful and implemented for example in the case where one or more pointer modules 6 are independent of the diagnostic platform 2 and are likely to have one or more clocks with values different from those of the diagnostic platform. A clock synchronization step is then performed by means of the synchronization module of the clocks 15. One of the clocks is identified as a reference, for example the clock of the platform or an external clock whose value is reliable and accurate. The other clock or clocks are then synchronized with the reference clock.

SELF-LEARNING MODULE

Alternatively, to determine when the target element is likely to switch to a target state, the diagnostic platform may include a self-learning module 9. This module is designed to automatically identify the data parameters specific physical elements when the target element approaches a target state. This module can be based on artificial intelligence. It preferably includes a link to a database of target elements and target states facilitating the analysis of the convergence towards one of these states. For example, a self-learning module can compare ranges of data evolution obtained for an item being diagnosed in order to compare these evolutionary ranges to similar ranges likely to be in the database. Possible strong similarities between longer and longer ranges can serve as indicators to automatically determine target state data. The module can also operate semi-automatically, generating proposals for a user who may or may not accept them, for example based on his own observations.

EXAMPLES OF APPLICATIONS

EXAMPLE 1

FIG. 3a is a first example of a referencing table of data acquired in the world of cereal agriculture, for the purpose of establishing a diagnosis making it possible to simply and reliably identify the moisture content of grain corresponding to an optimum instant for cutting the plants and harvesting the grain. In a conventional manner, the humidity level is checked manually by taking a plurality of grain samples in order to take measurements in the laboratory or in an industrial environment, with measuring equipment suitable for carrying out measurements. internal to the grain. This approach is very time-consuming and requires the operator to make numerous trips to various areas of the farm where crops are about to be harvested. The implementation of automated monitoring, based on reliable parameters, defined during a diagnostic phase, not only allows a better productivity, but also a more reliable and precise detection of the best moment to harvest. The farmer can also organize the harvest of several parcels optimally according to the data he automatically obtains for each plot.

The first part of the table illustrates an example of data corresponding to a diagnosis of the moisture level of a grain. Not knowing, before the diagnosis, which is the significant parameter or parameters, the user sets up a complete and exhaustive diagnostic platform, to take into account a large number of potentially significant parameters. , either alone or with matching. In this diagnostic example, sensors measure air temperature and humidity, wind speed, brightness, and precipitation.

An example of data pointing is presented on the table presented as an example of principle. The analysis of the values of the parameters by comparing, for example, the values pointed to those in the vicinity of these, makes it possible to determine the most significant parameters, corresponding for example to those for which the deviations of values are the most important.

The second part of the table illustrates an example of parameters used for setting up an automatic monitoring. In this example, all parameters are retained, and the relevant values are used to target the monitoring to be performed.

FIG. 3b is a variant of the table of FIG. 3a in which the step of identification of the significant parameters made it possible to retain a small number of parameters, thus making it possible to simplify and lighten the monitoring phase, which can be implemented with a reduced number of detectors and parameters to monitor. In this example, the parameter corresponding to the brightness has been judged insignificant and not retained in the monitoring phase.

EXAMPLE 2

The table of Figure 4 illustrates another example of application to establish a diagnosis of significant parameters to determine simply, reliably and accurately the level of lift of the soil. This diagnosis is important because a minimum level of lift is required to allow the various equipment to move on the soil of the farm without damaging the crops in place and / or risk of sinking into the ground. Conventionally, this parameter is determined by an operator who must move to walk on the ground to feel the perceived level of hardness. He can also use a penetrometer to take precise measurements, but this last approach is particularly time-consuming and therefore very little used in practice. In this application, the establishment of an automatic monitoring mode with reliable parameters, without the operator having to intervene, allows significant productivity gains, and avoids incidents in cases where an operator would come to travel on a floor without first checking the level of hardness and / or lift.

The first part of the table illustrates an example of data corresponding to a diagnosis of the lift of the ground. Not knowing, before the diagnosis, which is the significant parameter or parameters, the user sets up a complete and exhaustive diagnostic platform, to take into account a large number of potentially significant parameters. , either alone or with matching. In this diagnostic example, sensors measure soil moisture and temperature at several depths, air temperature and humidity, infrared radiation, and wind speed.

In the example illustrated in FIG. 4, six parameters are recorded: the humidity of the soil and the subsoil, the temperature of the soil and the subsoil, the temperature of the air at a distance x from the soil, air humidity at a distance x from the ground, infrared radiation and wind speed.

Several sensors are set up to record data related to humidity and the temperature of the soil and subsoil. The sensors are placed at a distance of 5cm below the ground level, then at 10cm below the ground level, then 5cm in 5cm until reaching 30cm below ground level. The line marked with a flag corresponds to the values pointed out during the pointing phase, at the moment when a target state is observed or detected.

The second part of the table illustrates an example of parameters used for setting up an automatic monitoring. In this example, the measurements made it possible, for example, to identify a level of soil depth conferring more relevant results. Other levels of measurements are therefore not used for the monitoring phase, in order to make it as simple as possible to set up and manage.

As illustrated, the monitoring phase is based on a reduced number of parameters corresponding to the most significant parameters of the target state sought. In this example, the value of a single sensor, located some distance from the ground, is retained for the soil moisture data and the soil temperature data. In this example, the raised sensor is the one placed at 10cm below the ground level.

EXAMPLE 3

The table of FIG. 5 illustrates another example of application aimed at establishing a diagnosis of the significant parameters for determining in a simple, reliable and precise manner the appearance of a fungus such as yellow rust or rust. brown on cereal crops such as wheat. This diagnosis, made at the earliest possible stage of the appearance of the fungus, allows rapid targeted treatment to prevent all crops are invaded by a massive attack.

[0074] Conventionally, the risk related to fungi is treated in two ways. Most often, fungicide treatments are applied to most crops. This approach, uneconomical and expensive, involves a great consumption of products, and monopolizes the operator for long periods to spread. When this approach is not sufficient or has not been achieved and fungi have appeared on crops, the operator then applies a curative treatment, also expensive, unecological, and time-consuming.

The first part of the table illustrates an example of data corresponding to the earliest possible diagnosis of the appearance of the first mushrooms. Not knowing, before the diagnosis, which is the significant parameter or parameters, the user sets up a complete and exhaustive diagnostic platform, to take into account a large number of potentially significant parameters. , either alone or with matching. In this diagnostic example, as shown in FIG. 5, sensors measure soil moisture and temperature, air temperature and humidity, infrared radiation, and brightness. the color of a given area and / or image and imaging monitoring, reflectivity, impedance and ground-level current.

The line marked with a flag corresponds to the values pointed during the pointing phase, at the moment when a target state is observed or detected, in this case when the first signs of fungi or first mushrooms appear.

The second part of the table illustrates an example of parameters used for setting up an automatic monitoring. As illustrated, the monitoring phase is based on a reduced number of parameters, corresponding to the most significant parameters of the target state sought.

Alternatively, the alert phase is completed by a monitoring phase of the treatment applied. For example, the surveillance detectors also include detectors capable of measuring the implementation of the post-detection treatment, in this case a spreading treatment of fungicide and / or other product making it possible to return to a normal situation, with healthy cultures. . For example, capacitive sensors are provided to detect whether the fungicide is applied sufficiently, insufficiently or excessively.

Alternatively, the monitoring sensors may include sensors capable of monitoring the effectiveness of the applied treatment and / or return to a normal situation, with healthy cultures.

In this example, but also in general, the sensors and / or detectors preferably include geolocation data to allow the operator to identify the field or parcels concerned. As a result, both detectors and sensors on each plot, or each representative parcel, are predicted for both diagnostics and monitoring, because the results, particularly the results of the monitoring phases, often vary from one parcel to another.

In another variant, the input data, in particular for the diagnostic phases, include data from sources external to the farm, such as, for example, weather data from a third party provider, and / or data. various models, such as meteorological, rainfall, epidemiological and other model data.

Reference numbers used in the figures

Claims

1. System (1) for monitoring and alerting a target crop or livestock component (20) of a farm, comprising:

i) a diagnostic platform (2) of the target element (20) comprising a plurality of diagnostic sensors (3) and a centralized module (4) for recording and analyzing the parameters designed to accumulate during a period of time. limited duration corresponding to a diagnostic phase of said target element (20) of the state data of the target element as a function of its evolution;

ii) a pointer module (6) adapted to receive a pointer input ("flag") indicating that a target state to be monitored of the target element (20) is being or about to occur, and a module ( 7) identifying data corresponding to said target state of the target element (20);

iii) a monitoring platform (10) of said target element, distinct from the diagnostic platform (2), and comprising a reduced number of detectors (13) with respect to the diagnostic platform (2) and a monitoring module (1 1) adapted to monitor for a period corresponding to a monitoring phase of said target element (20) the state data of the target element (20) and / or its environment (21), the flat monitoring system (10) further comprising an alert module (14) for generating an alert condition when the parametric data obtained by the monitoring platform (10) is in correspondence with the data of said target state identified by the pointer module (6).

Monitoring and warning system according to claim 1, wherein the number of detectors (13) of the monitoring platform (10) is at least 50% less and more preferably at least 70% less than the number of of sensors (3) of the diagnostic platform (2).

3. Monitoring and warning system according to one of claims 1 or 2, wherein the parameters used for diagnosis and / or monitoring are selected from: pressure, temperature, humidity, wind speed , brightness, UV index, mass, force, torque, current, voltage, magnetic flux, flow, image, sound level, color, reflectivity, concentration of a gas or other element, an impedance.

4. Monitoring and alerting system according to any one of the preceding claims, wherein the pointer module (6) is adapted to communicate remotely with the diagnostic platform.

The monitoring and alerting system according to claim 4, wherein the diagnostic platform (2) and the pointer module (6) each comprise a clock adapted for time stamping the data, and wherein a module of synchronization (15) of the clocks is provided.

A monitoring and alerting system according to any one of the preceding claims, wherein the diagnostic platform (2) comprises a self-learning module (9) designed to automatically identify the corresponding physical specific parameter data. passing said target element to the target mode.

7. A method for diagnosing and monitoring a target element (20) for growing or rearing a farm using a monitoring and warning system (1) according to one of the claims. 1-6, comprising the steps of:

PHASE 1

i) setting up (100) a diagnostic platform (2), said platform (2) comprising a plurality of diagnostic sensors (3) cooperating with said target element (20) or its environment (21);

ii) acquiring and recording (101) system diagnostic data using the plurality of sensors (3) and a centralized (4) recording module;

PHASE 2

iii) in case of occurrence of a target state of said target element (20), input (103) of an identification data (flag) of said target state of said target element (20);

iv) identifying (104) parameters and threshold values corresponding to the transition to said target state;

PHASE 3

v) parameterizing (105) a monitoring platform (10), separate from the diagnostic platform (2), and operating on the basis of the parameters and threshold values corresponding to the transition to said target state;

vi) setting up (106) the parameterized monitoring platform (10), said platform comprising a reduced number of detectors (13) for monitoring with respect to the diagnostic platform (2), said detectors (13) of monitoring cooperating with said target element (20) or its environment (21);

vii) monitoring (107) of said target element (20) and / or its environment by the monitoring platform (10);

viii) in case of correspondence between the parametric data obtained by the monitoring and the threshold values of passage towards said target state, emission (108) of an alert.

The method of diagnosis and monitoring according to claim 7, wherein the parametric data used for diagnosis and / or monitoring are selected from: pressure, temperature, humidity, wind speed, brightness, UV index, mass, force, torque, current, voltage, magnetic flux, flow, image and sound level.

9. Diagnostic and monitoring method according to one of claims 7 or 8, wherein the step of identifying the data of the parameters corresponding to said target state allows the identification of pre-alert data.

10. A method of diagnosis and monitoring according to any one of claims 7 to

9, further comprising a step of synchronizing the clocks.

11. Diagnostic and monitoring method according to any one of claims 7 to

10, further comprising at least one post-alert monitoring step.

The diagnostic and monitoring method of claim 11, wherein the post-alert monitoring comprises monitoring the application of a curative treatment.

The diagnostic and monitoring method of claim 11, wherein the post-alert monitoring comprises monitoring a target state corresponding to healthy cultures.