SYSTEM AND METHOD FOR CONTROLLING AN IRRIGATION SYSTEM ON A FARM

Abstract

The invention relates to a method and a control system (3) for an irrigation installation (1) on a farm (13), comprising a control computer (33) configured to interact with measuring devices (5) and actuators (9) of said irrigation installation (1) through a refined and aligned conversational AI, referred to as current conversational AI (35), to control the irrigation of said farm (13) based on data transmitted by the measuring devices (5) and taking into account information relating to the farm and the irrigation installation. Figure for the abstract: Fig. 1

Description

Title of the invention: SYSTEM AND METHOD FOR CONTROLLING AN IRRIGATION INSTALLATION OF AN AGRICULTURAL FARM Technical field

[0001] The present invention relates to the field of irrigation of an agricultural holding and more particularly, automatic irrigation taking into account the properties of the soils and the specificities of the crops. STATE OF THE PRIOR ART

[0002] Irrigation of a farm depends on many parameters, not all of which can be measured. One of the most important parameters is the measurement of soil moisture, which varies according to depth.

[0003] Currently, methods are known which make it possible to trigger the irrigation of an agricultural operation based on soil hydrometry thresholds.

[0004] One of these methods is tensiometry, which measures a depression corresponding to the suction force that the root must exert to extract the available water. This method is relevant for knowing the quantity of water available for the plant but requires several probes per reading level and regular maintenance operations to obtain reliable information.

[0005] The most widespread technique for measuring soil moisture is capacitive measurement. This is most often carried out using a frequency measurement of an LC oscillator immersed in the soil. It should be noted that capacitance depends on permittivity and is therefore not a direct measurement of moisture. Permittivity can be related to moisture through empirical formulas that depend on the medium and the operating frequency.

[0006] However, the capacitive measurement technique suffers mainly from the difficulty of calibration linked to the multitude of influencing parameters. These influencing parameters include the diversity of soil textures; the variety of liquid / air / solid interfaces depending on the media; the conductivity of the interstitial liquid (dissolved ions); the temperature; the compaction and homogeneity of the medium; cracks; the presence and activity of organic matter (for example, rhizosphere, mycelium, algae, earthworms). The estimation of these factors is limited by the knowledge of certain non-measurable parameters, such as for example the saturated hydraulic conductivity of the soil and the hydraulic roughness of the plot surface. Calibration also depends on the measurement frequency and the texture of the soils: a device operating with a high frequency will be less sensitive to the conductivity but will have a larger analysis volume. more reduced.

[0007] Thus, the calibration of capacitive sensors is complex, tedious and specific to each type of soil. It is possible to reduce the influence of certain factors depending on the frequency but different relaxation phenomena cover the entire frequency spectrum.

[0008] Furthermore, for a given humidity level, the water available to plants is not the same depending on the type of soil. Most commercially available capacitive sensors operate at frequencies of the order of 10 MHz to 100 MHz. These high frequencies are sensitive to the water absorbed by the grains that the plants cannot extract and are less suitable for measuring the capillary water available for irrigation.

[0009] Furthermore, the irrigation conditions are characterized by the dose and duration of irrigation depending on the available flow rate as well as other parameters relating, for example, to the specificity of the soil and its storage capacity which have impacts on the growth of the plants depending on their adaptation to the environment.

[0010] This multitude of influencing parameters only allows for an approximate view of the water profile in comparison with the generic recommendations themselves.

[0011] Other irrigation techniques use empirical models based on meteorological data to control irrigation installations. These models are essentially based on evapotranspiration which depends on temperature, atmospheric pressure, brightness, wind, relative humidity, water quality, solar radiation and the type of crop.

[0012] These empirical models remain very limited and can only give a static and very partial representation of the irrigation conditions and the state of the crop.

[0013] Thus, the object of the present invention is to remedy the aforementioned drawbacks by proposing a method and a system for automatically controlling an irrigation installation that is simple to implement, taking into account the specificity of the soil and the type of crop while being compatible with all measurement techniques and whatever the measurement frequencies used. Statement of the invention

[0014] The subject of the invention is a system for controlling an irrigation installation of an agricultural holding, comprising a control computer configured to interact with measuring devices and actuators of said irrigation installation through a refined and aligned conversational AI, called current conversational AI, to control the irrigation of said holding according to the data transmitted by the measuring devices and taking into account the information relating to the agricultural holding and the irrigation installation.

[0015] This allows for automatic and intelligent control by optimizing results (water consumption, volume and quality of the harvest) according to the specificities of the farm. In addition, this AI-based system works regardless of the measurement frequency of the sensors, allowing the use of low-power, low-cost sensors that do not require complex calibration. The AI ​​learns by itself to correct and compensate for sensor errors.

[0016] Advantageously, the control computer is configured to determine a current water profile as a function of a change in the root depth of plants.

[0017] This allows the amount of watering to be adjusted according to the maturity of the crop, which changes according to the depth of the roots.

[0018] Advantageously, the control computer is configured to carry out preliminary learning on an initial conversational AI, said control computer being configured to:

[0019] -refine the initial conversational AI by providing it with scientific documents relating to agricultural irrigation producing a refined conversational AI having theoretical knowledge on irrigation, and

[0020] -align the refined conversational AI by providing it with examples of its actual operation producing said current conversational AI having real knowledge about irrigation.

[0021] Thus, current conversational AI is conversational AI refined and aligned for farm irrigation.

[0022] Advantageously, the control computer comprises:

[0023] -a conditioning module configured to condition the current conversational AI by providing it with said information relating to the agricultural operation and the irrigation installation, and

[0024] - an interface module configured to:

[0025] -receive at each sampling instant clocked by a clock, measurement data from the measurement devices,

[0026] -transforming said measurement data received from the measurement devices into textual data, and / or into tokens, and / or into lexical embedding,

[0027] -returning said text data and / or tokens, and / or lexical embedding to the current conversational AI which is configured to analyze and make a decision to start, continue or stop the irrigation, and

[0028] -control at least one actuator of the irrigation installation according to the decision taken by the current conversational AI.

[0029] Thus, current conversational AI is evolving towards a model specifically designed for the irrigation of the agricultural operation in question.

[0030] Advantageously, said information relating to the agricultural operation and to The irrigation system includes the soil type, crop, and farm area, as well as the types of measuring devices, types of actuators, and the watering flow rate of the irrigation system.

[0031] Advantageously, said measurement data comprises humidity measurements on several soil levels, air humidity measurements, temperature, brightness, wind, root depth measurements, an estimate of the probability of precipitation, and a time stamp.

[0032] Advantageously, the control system comprises a fine-tuning database. The interface module is configured to time-stamp and record in the fine-tuning database the history of its interactions with the measuring devices and actuators.

[0033] Thus, the entire discussion between FIA and the sensors and actuators is logged. The sensors give the measurements, the AI ​​gives the reasoning and decisions (e.g., hour by hour).

[0034] Advantageously, the control computer comprises an annotation module configured to annotate the history of the interactions, thus forming an annotated log in the fine-tuning database.

[0035] Annotation allows for the detection of poor decision-making and changing the content of the recording in order to make a better decision than the one that has already been made.

[0036] Advantageously, the control computer is configured to receive observations on the state of the plants and the soil of the agricultural operation and in that the annotation module is configured to annotate the history recorded in the fine-tuning database according to said state of the plants as a function of their maturity, and of the soil.

[0037] Thus, the state of plants and soil is a relevant indicator for AI which eliminates the need to know the precise humidity level of the soil.

[0038] Advantageously, the control computer is configured to evaluate a posteriori the effectiveness of the irrigation control by annotating a set of quality indicators including the yield and the quality of a harvest at the end of the harvest.

[0039] This makes it possible to optimize irrigation while meeting user satisfaction criteria.

[0040] Advantageously, the control computer is configured to carry out a calibration of the measurements from the measuring devices by continuous learning using observations on the state of the plants and the soil of the agricultural operation.

[0041] This eliminates the need for complex and costly calibration of capacitive sensors where the capacitive measurement is not a direct measurement of humidity.

[0042] Advantageously, the control computer is configured to continuously refine the current conversational AI by reinjecting the current annotated log into a learning process of a LoRA-like LLM language model training a common conversational AI model with LoRA adapter.

[0043] Thus, thanks to this technique, the current AI with LoRA adapter continues to learn from its logged and annotated experiences. Initially, the AI ​​was trained theoretically and on some practical examples and now, thanks to LoRA, the AI ​​has a deep and practical knowledge on the specific field of agricultural exploitation.

[0044] The invention also relates to an irrigation installation for an agricultural operation comprising the control system according to the preceding characteristics, further comprising:

[0045] -a set of measuring devices configured to take measurements on the plants, soil, and air of the agricultural operation, and to transmit the measurements to the control system, and

[0046] -a set of actuators configured to activate or stop irrigation according to the command received from the control system.

[0047] Advantageously, the measuring device comprises a tube made of insulating material equipped with capacitive humidity measuring sensors, a power supply module, an acquisition and processing module and a communication module.

[0048] Advantageously, the irrigation installation comprises an observation system comprising cameras configured to transmit to the control system images and / or observations on the state of the plants and the soil of the agricultural operation.

[0049] The invention also relates to a method for controlling an irrigation installation of an agricultural holding, comprising a step of interaction with measuring devices and actuators of said irrigation installation through a refined and aligned conversational AI, called current conversational AI, to control the irrigation of said holding according to the data transmitted by the measuring devices and taking into account the information relating to the agricultural holding and the irrigation installation.

[0050] Advantageously, the method comprises a first learning phase comprising the following steps:

[0051] -refine the initial conversational AI by providing it with scientific documents relating to agricultural irrigation producing a refined conversational AI having theoretical knowledge on irrigation, and

[0052] -align the refined conversational AI by providing it with examples of its actual operation producing said current conversational AI having real knowledge about irrigation.

[0053] Advantageously, the method comprises a learning phase comprising the following steps:

[0054] -conditioning the current conversational AI by providing it with said information relating to the agricultural operation and the irrigation installation,

[0055] -receive at each sampling instant clocked by a clock, measurement data from the measurement devices,

[0056] -transformation of said measurement data received from the measurement devices into textual data, and / or into tokens, and / or into lexical embedding,

[0057] -returns said text data, and / or tokens, and / or lexical embedding, to the current conversational AI which is configured to analyze and make a decision to start, continue or stop the irrigation, and

[0058] -control of at least one actuator of the irrigation installation according to the decision taken by the current conversational AI.

[0059] Other advantages and characteristics of the invention will appear in the detailed non-limiting description below. Brief description of the drawings

[0060] Embodiments of the invention will now be described, by way of non-limiting examples, with reference to the accompanying drawings, in which:

[0061] [Fig.l] very schematically illustrates an irrigation installation for an agricultural operation, according to one embodiment of the invention;

[0062] [Fig. 2] very schematically illustrates a measuring device intended to be used in agricultural operations, according to one embodiment of the invention; and

[0063] [Fig.3A], [Fig.3B], [Fig.3C], [Fig.3D] and [Fig.3E] illustrate very schematically in relation to [Fig.1], the different phases of interaction between the piloting computer and the conversational artificial intelligence of the piloting system, according to a preferred embodiment of the invention. DETAILED DESCRIPTION OF THE INVENTION

[0064] The principle of the invention is to use conversational AI to automatically control the irrigation of a farm by continuously taking into account the properties of the soil, the specificity of the crops and observations on the plants.

[0065] [Fig.l] very schematically illustrates an irrigation installation for an agricultural holding, according to one embodiment of the invention.

[0066] The irrigation installation 1 comprises a control system 3, a set of measuring devices 5 for measuring the humidity of the soil 7, a set of actuators 9 for activating the start or stop of the irrigation and possibly, an observation system 11.

[0067] The measuring devices 5 are intended to be partially embedded in the soil 7 of the agricultural holding in order to measure the humidity of the soil as a function of the depth. The measuring devices 5 are also configured to take measurements on the plants 12, the soil 7, and the air of the farm 13 and to transmit all the measurements to the control system 3.

[0068] The observation system 11 comprises cameras 14 and / or observation sensors for taking images and / or observations on the state of the plants 12 and the soil 7 of the agricultural holding 13. The observation system 11 may also comprise an AI adapted to identify water stress of the plants or signs of saturation on the images taken by the cameras 14. For example, water stress can be identified by the wilting of the plants and signs of water saturation can be identified by puddles of water in the fields.

[0069] The cameras 14 and / or observation sensors can be installed in different locations of the agricultural operation 13. Alternatively, drones comprising cameras and / or observation sensors can also be used to monitor the progress of the agricultural operation.

[0070] The observation system 11 based on cameras 14 and an adapted AI is thus adapted to transmit to the control system 3 images and / or observations on the state of the agricultural operation 13.

[0071] It will be noted that a first source of observation is advantageously the farmer who can inform the control system 3 about the state of his operation. In this case, the observation system 11 can complement the direct observations made by the farmer.

[0072] [Fig.2] very schematically illustrates a measuring device intended to be used in agricultural operations, according to one embodiment of the invention.

[0073] According to this embodiment, the measuring device 5 comprises a tube 15 made of insulating material equipped with humidity measuring sensors 17, a power supply module 19, an acquisition and processing module 21 and a communication module 23.

[0074] The insulating material of the tube 15 can be PVC, PET, polycarbonate, glass, ceramic, etc. The length of the tube 15 depends on the depth of the root network of the plant. The number of sensors 17 depends on the length of the tube 15 and therefore also on the depth of the roots of the plant.

[0075] Each humidity measurement sensor 17 is advantageously a capacitive sensor comprising a resonant electrical circuit whose resonant frequency is representative of a permittivity measurement which is transformed via empirical formulas into a measurement of the humidity of the soil.

[0076] The resonant electrical circuit is an inductive-capacitive circuit comprising an active reactance associated with a corresponding passive coupling element. The active reactance is for example a pair of metal armatures and the coupling element Passive is an inductive element. The geometry and configuration of the metal armatures can be optimized according to the measurement range and the desired accuracy. An example of a capacitive sensor is described in patent application FR3115111.

[0077] Advantageously, the tube 15 made of insulating material is equipped with different types of capacitive sensors 17 comprising resonant electrical circuits at different frequencies, suitable for different types of crops and different sensitivities.

[0078] Optionally, the measuring device 5 may comprise or be associated with other sensors or probes comprising a temperature sensor 25, a conductivity measuring sensor 27, a weather station 29, a time clock 31, etc.

[0079] The temperature and conductivity sensors are advantageously distributed throughout the probe at each humidity measurement level with the aim of merging these measurements because they are interdependent.

[0080] Advantageously, the power supply module 19 comprises an autonomous means of energy recovery such as for example a photovoltaic panel or a micro turbine. Alternatively, it may comprise simple electric batteries.

[0081] The acquisition and processing module 21 is for example a low-power microcontroller with a standby mode. The microcontroller 21 is configured to extract the measurements and data from the various sensors 17, 25, 27, 29, 31 in order to determine measurements of soil moisture as a function of depth, measurements of moisture and temperature gradients as a function of depth, weather data, etc.

[0082] The communication module 23 is configured to communicate the results from the acquisition and processing module 21 with or without wires to the control system 3.

[0083] In accordance with the invention, the control system 3 comprises a control computer 33 configured to interact with the measuring devices 5 and actuators 9 through a refined and aligned conversational artificial intelligence “AI”, called current conversational AI 35. The control computer 33 is configured to automatically control the irrigation of the agricultural operation 13 according to the data transmitted by the measuring devices 5 and taking into account the information relating to the agricultural operation 13 and to the irrigation installation 1.

[0084] The control of irrigation by the control computer 33 involves the determination of the optimal current water profile as a function of the evolution of the root depth of the plants. Indeed, the root depth evolves with its maturity, implying an evolution of the optimal water profile. It will be noted that the data relating to the evolution of the root depth of the plants are specific to each crop; they can be found in the literature or in practice by sampling.

[0085] The control system 3 further comprises databases 37 comprising various data relating to general information on agricultural irrigation, in training on the specific farm 13, real examples of operation of the irrigation installation 1, fine-tuning data relating to the history of interactions of the current conversational AI 35 with the measuring devices 5 and actuators 9, etc.

[0086] Thanks to the continuous learning of the current conversational AI 35 using in particular the information and observations relating to the agricultural operation 13 and the irrigation installation 1, the control computer 33 is configured to carry out a calibration of the measurements from the measuring devices 5. It is thus possible to set an operating frequency that is most suited to the specificities of the crop. This dispenses with a complex and costly calibration of the capacitive sensors 17 where the capacitive measurement is linked to a multitude of influence parameters that cannot be measured directly.

[0087] In addition, conversational AI-based processing works regardless of the measurement frequency. For example, measurement acquisition can be performed at low frequencies, thereby increasing sensitivity to plant-available water while increasing the volume of measurements by the sensor.

[0088] Furthermore, thanks to the continuous learning of the current conversational AI, the measuring device can be equipped with the first 17a and second 17b types of capacitive sensors. The first capacitive sensors 17a comprise high-frequency resonant electrical circuits, for example, greater than approximately 30 MHz, in order to reduce the impact of the conductivity and texture of the soils of large crops. The second capacitive sensors 17b comprise low-frequency resonant electrical circuits, for example between 0.1 MHz and 10 MHz, in order to be more sensitive to the conductivity and texture of the soils, which are factors in the availability of nutrients and water in the soils. Thus, whatever the measurement frequency, the current conversational AI 35 makes it possible to calibrate the measurements from the measuring devices 5 so that they are indicative of the soil moisture levels.

[0089] It will be noted that the control system 3 can be arranged locally in the agricultural holding. Alternatively, a farmer managing the agricultural holding can connect to a remote control system at a service provider or on a cloud.

[0090] Figs. 3A-3E illustrate very schematically in relation to [Fig.l], the different phases of interactions between the piloting computer 33 and the conversational artificial intelligence 35 of the piloting system 3, according to a preferred embodiment of the invention.

[0091] [Fig.3A] is a first phase during which the control computer 33 is configured to carry out preliminary learning on an initial conversational AI 35a. Advantageously, the initial conversational AI 35a is a conversational AI- pre-trained tional.

[0092] In step PI 1, the control computer 33 is configured to refine the initial conversational AI 35a by providing it with scientific documents relating to agricultural irrigation from a first database 37a, thus producing a refined conversational AI 35b. The fine-tuning allows the conversational AI model to acquire preliminary theoretical knowledge on irrigation.

[0093] In step P12, the control computer 33 is configured to align the refined conversational AI 35b by providing it from a second database 37b with examples of its actual operation comprising predetermined real interactions with the measuring devices 5 and actuators 9 of the irrigation installation 1. This alignment produces a refined and aligned conversational AI, called current conversational FIA 35. The alignment makes it possible to force the AI ​​model to behave according to concrete examples of operation.

[0094] This first phase thus allows FIA conversational current 35 to begin irrigation control by applying the initial knowledge. Then, during the following phases FIA conversational current 35 will gradually learn the consequences of its own tasks.

[0095] [Fig.3B] is a second phase during which the control computer 33 is configured to evolve the current conversational FIA 35 towards a model specifically made for the irrigation of the agricultural operation 13 in question.

[0096] Indeed, the control computer 33 comprises a conditioning module 41 and an interface module 43 configured to carry out the steps P21-P25. The interface module 43 is for example an application intended to communicate with the current conversational AI 35 and with the measuring devices 5 and actuators 9.

[0097] In step P21, the conditioning module 41 is configured to condition the current conversational AI 35 by providing it with the information 44 relating to the agricultural operation and the irrigation installation. The information relating to the agricultural operation 13 includes the type of soil (clayey, sandy, etc.), the crop (corn, sunflower, etc.), the maturity of the crop, and the surface area of ​​the agricultural operation. The information relating to the irrigation installation 1 includes the types of sensors integrated into the measuring devices 5, the types of actuators 9, and the watering flow rate of the irrigation installation according to, for example, the maturity of the crop, etc. Thus, the conditioning is a set of data allowing the current conversational AI 35 to have information on the current task.

[0098] Furthermore, during steps P22-P25, the interface module 43 allows interaction through current conversational FIA 35 with the measuring devices 5 and actuators 9. It also allows the time-stamping and recording of these interactions.

[0099] More particularly, in step P22, the interface module 43 is configured to receive, at each sampling instant clocked by a clock 45, measurement data from the measurement devices 5. This measurement data comprises humidity measurements on several soil levels, air humidity measurements, temperature measurements, brightness measurements, wind measurements, root depth measurements, an estimate of the probability of precipitation (for example within 24 hours), and a timestamp (i.e. the time of day and the period of the year).

[0100] In step P23, the interface module 43 is configured to transform the measurement data received from the measurement devices 5 into textual data and / or into tokens and / or into word embedding.

[0101] Thus, according to a first particular embodiment, the measurement data are transformed into textual data comprising character strings representative of the measurement data and providing information on the current state of the measurement devices 5.

[0102] According to a first variant, the measurement data are transformed into tokens comprising symbols corresponding to identifiers directly interpretable by current conversational AI.

[0103] According to a second variant, the measurement data are transformed into a lexical embedding according to a data vectorization in a latent vector space of the model. This abstract representation technique makes it possible to reduce the dimension of the representation of words, thus facilitating learning. Lexical embeddings from several measurement devices can be hybridized by training an adaptation layer. This makes it possible to provide the current conversational AI model with a 'sense' allowing it to directly 'understand' what it 'sees', what it 'hears', and what it 'feels' (temperature, humidity, etc.).

[0104] In step P24, the interface module 43 is configured to return the textual data, and / or tokens and / or lexical embedding to the current conversational AI 35. The latter is configured to analyze and make a decision to start, continue or stop the irrigation.

[0105] In step P25, the interface module 43 is configured to control at least one actuator 9 of the irrigation installation according to the decision taken by the current conversational AI in step P24.

[0106] It should be noted that safeguards can be implemented for this second phase in order to reduce the risk of bad decisions.

[0107] [Fig.3C] is a third phase intended to prepare a fine-tuning database.

[0108] Indeed, the control computer 33 also includes an annotation module 47 in addition to the conditioning modules 41 and interface 43. During this third phase, the control computer 33 uses the interface modules 43 and annotation 47 to prepare the fine-tuning database.

[0109] In step P31, the interface module 43 is configured to time-stamp and log or record the history 46 of its interactions with the measuring devices 5 and actuators 9 through the current conversational AI 35 in a database 37c. The history 46 includes the reasoning and decisions taken by the current conversational AI 35, for example, hour by hour.

[0110] In step P32, the annotation module 47 is configured to annotate the history 46 of the interactions recorded in the database 37c, thus forming a refining database 37d, also called an 'annotated log'. More particularly, the state of the plants and / or the soil of the agricultural holding can be annotated by a farmer 53 or operator of this holding. The farmer 53 can possibly re-annotate what was previously annotated.

[0111] Furthermore, the annotation module 47 is configured to use information 51 on the state of the plants and / or the soil from the images and / or observations of the observation system 11 to annotate the history 46 of the interactions recorded in the database 37c.

[0112] The annotation aims to detect bad decision-making and change the content of the recording or simply indicate that the result of a decision-making was not good in order to allow the current conversational AI 35 to progress by making better decisions than those made previously.

[0113] Advantageously, the annotation also makes it possible to adjust the amount of watering according to the maturity of the crop.

[0114] Current conversational AI 35 can thus learn by itself by looking at the differences between the objectives and reality. After a few experiments, current conversational AI 35 discovers the right decision-making. This can then be permanently added to the refinement of current conversational AI.

[0115] Over time, the current conversational AI sees the effects of its decisions on the plants on the farm. For example, if the current conversational AI sees that the plants are experiencing water stress, it can conclude that its last reasoning was not good. Thus, the current conversational AI can learn the consequences of its mistakes, which allows it to learn and improve its future reasoning and decisions.

[0116] Thus, the state of the plants and the soil is used here as an indicator of water stress or over-irrigation either through observation by the farmer 53, or by means of the cameras 14. This makes it possible to dispense with knowing precisely the rate soil moisture knowing that this last parameter is not a relevant indicator on its own, because for a type of soil with a given hydrometry profile, the capacity of plants to absorb moisture is not the same.

[0117] All these observations and annotations on the state of the plants and the soil allow 1TA current conversational 35 to further improve the calibration of the measurements from the measuring devices 5.

[0118] Advantageously, the control computer 33 is configured to evaluate a posteriori the effectiveness of the irrigation control by annotating a set of quality indicators. The set of quality indicators includes the yield (for example, water consumption, weight of the harvest, duration of cultivation, diseases, etc.) and the quality (for example, taste quality) of a harvest at the end of the harvest.

[0119] In this case, the control system is configured to take these annotations into account by adjusting the initial parameters during a next cultivation to meet user satisfaction criteria.

[0120] It should be noted that the user can assign a weight to each quality indicator depending on the importance he gives to each of these criteria. For example, growth speed may be less important than quality depending on the farmer's priorities. As the harvest progresses, the farmer may, for example, seek to reduce his water consumption while improving the volume of the harvest and its quality. In this case, strong weights are assigned to these three criteria to identify which water profiles are most favorable at each phase of crop development.

[0121] The water profile can be modified by the duration and frequency of watering, knowing that if we water for a long time and less often, the humidity will be greater in depth than if we water little and often.

[0122] Adapting the water profile to the plant's needs throughout its growth allows water consumption to be optimized while ensuring the quantity and quality of the harvest. Thus, feedback from past harvests allows us to converge towards the optimal water profile for each growth phase.

[0123] [Fig.3D] is a fourth phase intended to use the Large Language Model (LLM) language model technique to perform additional continuous refinement.

[0124] In step P41, the control computer 33 is configured to continuously refine the current conversational AI 35 by reinjecting the current annotated log 37d into a learning process of an LLM language model of the LoRA (Low Rank Adaptation) type. It will be noted that LoRA is a rapid, low-resource refinement technique that makes it possible to block the entire network of the model except for a few areas. That is to say, there are layers that will be left free, allowing the current conversational AI not to forget what it has learned previously.

[0125] This step P41 is a loop during which the log annotated in the database 37d which contains only the good decisions or corrected decisions is reinjected by the control computer 33 into a LoRA type learning process to refine the current conversational AI 35. This fine-tuning of the current conversational AI 35 consists of taking the “previous version of the LoRA model” 61 and performing learning on the cumulative data “LoRA fine tuning” 63 to produce a new “model of the current conversational AI” comprising a LoRA component, called “current conversational AI with LoRA adapter” 135. This same loop is repeated until the convergence of the current conversational AI. It will be noted that at each iteration, certain layers of the current conversational AI with LoRA adapter 135 will be modified to take into account the new information or experiences.The loRA adapter can be advantageously merged with the current conversational AI model.

[0126] Continuous fine-tuning thus allows for lighter learning by using the annotated log from the beginning to update the AI ​​model. Using the LoRA technique, this type of fine-tuning allows for updating only a few LoRA portions or layers of the current conversational AI model with LoRA adapter 135 and is therefore much less demanding in terms of required computing power.

[0127] With this technique, the current conversational AI with LoRA 135 adapter continues to learn from its experiences and should converge towards an AI model that is very specialized in the farm in question.

[0128] The frequency of continuous fine-tuning may be a function of bad decisions. As long as the current conversational AI with LoRA 135 adapter makes the right decisions, there is no need for further fine-tuning.

[0129] For example, this fourth phase can be carried out on a supplier's server. Alternatively, it can also be carried out locally on the farm.

[0130] [Fig.3E] is a fifth phase intended to use common conversational AI with LoRA adapter to better manage irrigation of farms.

[0131] This fifth phase is identical to the second phase of [Fig.3B], except that it is the current conversational AI with LoRA 135 adapter which is used for the farmer's operation.

[0132] Thus, in step P51, the conditioning module 41 is configured to condition the current conversational AI with LoRA adapter 135 by providing it with information relating to the agricultural operation and the irrigation installation.

[0133] In step P52, the interface module 43 is configured to receive, at each sampling instant clocked by the clock 45, measurement data from the measurement devices 5.

[0134] In step P53, the interface module 43 is configured to transform the measurement data received from the measurement devices 5 into textual data and / or into tokens and / or into lexical embedding.

[0135] In step P54, the interface module 43 is configured to return the textual data, and / or the tokens and / or the lexical embeddings to current conversational FIA with LoRA adapter 135. The latter is configured to analyze and make a decision to start, continue or stop the irrigation.

[0136] In step P55, the interface module 43 is configured to control at least one actuator 9 of the irrigation installation according to the decision taken by the current conversational AI with LoRA adapter 135 in step P54.

[0137] Thus, we continue to accumulate recordings that could eventually be used to feed similar AIs for other farms. This allows the AI ​​to learn to adapt to climate change and better manage the farm.

[0138] Advantageously, the control method or system can be applied to several consecutive crops to recover the language models of successive learning in order to evaluate the effectiveness of the irrigation control a posteriori.

[0139] This process or control system can also be applied to several crops in parallel to recover the language models of the different learnings. This makes it possible to create a generic conversational AI that can easily adapt to a new agricultural operation.

[0140] The control system according to the invention is very simple to implement while being compatible with all types of crops, soils, measurement methods or irrigation. It takes into account the knowledge of the grower, the specificity of the soil and the type of crop. It makes it possible to do without complex calibration of the capacitive sensors by taking into account the indicators from the plants and the soil. In addition, the control system is compatible with low-consumption probes and sensors.

[0141] Of course, various modifications can be made by those skilled in the art to the invention which has just been described, solely by way of non-limiting examples.

Claims

Claims

1. System for controlling an irrigation installation of an agricultural holding, characterized in that it comprises a control computer (33) configured to interact with measuring devices (5) and actuators (9) of said irrigation installation (1) through a refined and aligned conversational AI, called current conversational AI (35), to control the irrigation of said agricultural holding (13) according to the data transmitted by the measuring devices (5) and taking into account the information relating to the agricultural holding and the irrigation installation.

2. Control system according to claim 1, characterized in that the control computer (33) is configured to determine a current water profile as a function of a change in the root depth of plants.

3. Control system according to claim 1 or 2, characterized in that the control computer (33) is configured to carry out preliminary learning on an initial conversational AI, said control computer being configured to: - refine the initial conversational AI by providing it with scientific documents relating to agricultural irrigation producing a refined conversational AI having theoretical knowledge on irrigation, and - align the refined conversational AI by providing it with examples of its real operation producing said current conversational AI (35) having real knowledge on irrigation.

4. Control system according to any one of claims 1 to 3, characterized in that the control computer (33) comprises: - a conditioning module (41) configured to condition the current conversational AI (35) by providing it with said information relating to the agricultural operation (13) and to the irrigation installation, and - an interface module (43) configured to: - receive at each sampling instant clocked by a clock (45), measurement data from the measurement devices (5), - transform said measurement data received from the measurement devices (5) into textual data, and / or into tokens, and / or into lexical embedding, - return said textual data, and / or said tokens, and / or said lexical embedding to the current conversational AI (35) which is configured to analyze and make a decision to start, continue or stop irrigation, and -control at least one actuator (9) of the irrigation installation according to the decision made by the current conversational AI.

5. Control system according to claim 4, characterized in that said information relating to the agricultural operation (13) and to the irrigation installation includes the type of soil, the crop, and the surface area of ​​the agricultural operation, as well as the types of measuring devices, the types of actuators, and the watering flow rate of the irrigation installation.

6. Control system according to claim 4, characterized in that said measurement data comprises humidity measurements on several soil levels, air humidity measurements, measurements of temperature, brightness, wind, root depth, an estimate of the probability of precipitation, and a time stamp.

7. Control system according to any one of claims 4 to 6, characterized in that it comprises a fine-tuning database, and in that the interface module (35) is configured to time-stamp and record in the fine-tuning database the history of its interactions with the measuring devices and actuators.

8. Control system according to claim 7, characterized in that it comprises an annotation module (47) configured to annotate the history of the interactions, thus forming an annotated log in the fine-tuning database.

9. Control system according to claim 8, characterized in that the control computer (33) is configured to receive observations on the state of the plants and the soil of the agricultural operation and in that the annotation module (47) is configured to annotate the history recorded in the fine-tuning database according to said state of the plants as a function of their maturity, and of the soil.

10. Control system according to claim 8 or 9, characterized in that the control computer (33) is configured to evaluate a posteriori the effectiveness of the irrigation control by annotating a set of quality indicators including the yield and the quality of a harvest at the end of the latter.

11. Control system according to claim 9 or 10, characterized in that the control computer (33) is configured to carry out a calibration of measurements from measuring devices (5) by continuous learning using observations on the condition of plants and soil on the farm.

12. Control system according to any one of claims 8 to 11, characterized in that the control computer (33) is configured to continuously refine the current conversational AI by reinjecting the current annotated log into a learning process of a LoRA type LLM language model forming a current conversational AI model with LoRA adapter (135).

13. Irrigation installation for an agricultural operation comprising the control system according to any one of the preceding claims, characterized in that it further comprises: - a set of measuring devices (5) configured to take measurements on the plants, the soil, and the air of the agricultural operation (13), and to transmit the measurements to the control system (3), and - a set of actuators (9) configured to activate or stop the irrigation according to the command received from the control system.

14. Irrigation installation according to claim 13, characterized in that a measuring device (5) comprises a tube (15) made of insulating material equipped with capacitive sensors (17a, 17b) for measuring humidity, a power supply module (19), an acquisition and processing module (21) and a communication module (23).

15. Irrigation installation according to claim 13 or 14, characterized in that it comprises an observation system (11) comprising cameras (14) configured to transmit to the control system (3) images and / or observations on the state of the plants and the soil of the agricultural holding.

16. Method for controlling an irrigation installation of an agricultural holding, characterized in that it comprises a step of interaction with measuring devices (5) and actuators (9) of said irrigation installation (1) through a refined and aligned conversational AI, called current conversational AI (35), to control the irrigation of said holding according to the data transmitted by the measuring devices and taking into account the information relating to the agricultural holding and the irrigation installation.

17. Piloting method according to claim 16, characterized in that it comprises a first learning phase comprising the following steps: -refine an initial conversational AI by providing it with scientific documents relating to agricultural irrigation producing a refined conversational AI with theoretical knowledge on irrigation, and -align the fine-tuned conversational AI by providing it with examples of its actual operation producing said common conversational AI with real knowledge about irrigation.

18. Control method according to claim 16, characterized in that it comprises a learning phase comprising the following steps: - conditioning the current conversational AI by providing it with said information relating to the agricultural operation and the irrigation installation, - receiving at each sampling instant clocked by a clock, measurement data from the measurement devices, - transforming said measurement data received from the measurement devices into textual data, and / or into tokens, and / or into lexical embedding, - returning said textual data, and / or tokens, and / or lexical embedding, to the current conversational AI which is configured to analyze and make a decision to start, continue or stop irrigation, and -control of at least one actuator of the irrigation installation according to the decision taken by the current conversational AI.

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