Biological control method and system

Abstract

The present invention relates to a biological control method and system, a computer program product and a computer-readable storage medium relating to a given area, preferably a given agricultural area, wherein the aim of the invention is to determine the number of biocontrol agents to be introduced into the given area, the frequency with which the biocontrol agents are to be introduced into the given area, and the period during which the biocontrol agents are to be introduced into the given area, by means of a model equation, the model equation applying a mathematical relationship to at least one item of biological modelling data and to at least one item of ecological modelling data, the implementation thereof comprising the use of at least one item of user data.

Description

[0001] BIOLOGICAL CONTROL METHOD AND SYSTEM

[0002] Scope of the invention

[0003] The invention relates to a method and a biological control system concerning a defined area, preferably a defined agricultural area.

[0004] State of the

[0005] In the current state of knowledge, it is known to use chemical pesticides to protect crops in an agricultural area. However, the use of these chemical pesticides is gradually being banned due to their harmful effects on the health of farmers, consumers of cultivated products, and the environment.

[0006] An alternative to the use of chemical pesticides is biological control, or more generally, biocontrol. Biological control involves using living organisms (predators, parasitoids, etc.), called biocontrol agents, to combat harmful organisms (arthropods, pathogens, nematodes, etc.) that ravage crops.

[0007] Nowadays, only consultants such as agricultural advisors or biocontrol companies inform farmers about the use of biocontrol agents based on information relating to the type of crop and climatic conditions, for example.

[0008] However, these solutions are unreliable, only consider a limited number of factors, and, in terms of biocontrol, suffer from a lack of precision inherent to the human factor. Furthermore, there is no follow-up on the information provided to farmers, particularly when the agricultural advisor is no longer assigned to the agricultural sector in question or when they retire, for example.

[0009] Therefore, in view of the above, it appears necessary to propose a solution to overcome the aforementioned drawbacks.

[0010] According to a first aspect, the invention relates to a computer-implemented method for determining a curative or preventive treatment associated with a biocontrol agent against a pest in a given area, said given area being likely to include at least one pest, said method comprising the following steps:

[0011] - provide at least one suitable user database to receive user data, said user data including: at least one user data item concerning an indication relating to the pest,

[0012] - provide at least one suitable modelling database to receive modelling data, said modelling data including: at least one biological modelling data concerning information relating to the biocontrol agent, at least one ecological modelling data concerning an environmental condition associated with the biocontrol agent,

[0013] - provide a suitable model equation to apply a mathematical relationship to said at least one biological modeling data point and said at least one ecological modeling data point;

[0014] - implement the model equation using said at least one user data to obtain final data relating to the number of biocontrol agents to be introduced into the determined area, the frequency of introduction of the biocontrol agents to be introduced into the determined area and the period of introduction of the biocontrol agents to be introduced into the determined area.

[0015] According to one embodiment, said at least one user data includes at least one indication from the list below: The species of past, present or future pest;

[0016] The past, present or future presence of the pest;

[0017] The past, present or future density of the pest;

[0018] The index of past, present or future infestation by m 2 ;

[0019] The infested area, past, present or future;

[0020] The type of agricultural land cover;

[0021] The type of soil of the infested plot in the past, present or future;

[0022] The geographical location of the area to be treated;

[0023] The type of treatment: preventive or curative;

[0024] The use of inputs past, present or future.

[0025] According to one embodiment, said at least one biological modelling data includes at least one indication relating to the biocontrol agent from the list below: number of pests attacked per day by biocontrol agents, number of pests attacked per day by a population of biocontrol agent, population growth rate, predation rate, parasitism rate, total fecundity, fecundity per day per female, functional response, survival rate, life cycle duration, base temperature, number of degree days required to complete a full development cycle.

[0026] According to one embodiment, said at least one biological modelling data including at least one indication relating to the pest from the list below: population growth rate, fecundity, life cycle duration, survival, pest tolerance index of the cultivated plant, base temperature, number of degree days required to complete a full development cycle, damage and treatment threshold, type of appearance, proportion of pest development stages accessible by the biocontrol agent.

[0027] According to one embodiment, said at least one ecological modelling data includes at least one indication from the list below: temperature (C), humidity (D), photoperiod (E), nitrogen (G), phosphorus (H) or potassium (I) levels in the soil, proportion of sand in the soil (J), proportion of clay in the soil (K), infestation rate (L), biocontrol agent density (M), cannibalism proportion (N), superparasitism (O), multiparasitism (P), developmental stage (Q), past (R), present or future application of a chemical or organic input (S).

[0028] According to one embodiment, said mathematical relationship is based on a multiple linear regression model.

[0029] According to one embodiment, the method further includes the following steps to determine a curative treatment: determining the number of biocontrol agents to be introduced according to the model equation, preferably by calculating the number of pests consumed per biocontrol agent based on biological modeling data of the biocontrol agents and the pests, and based on ecological modeling data of the area to be treated, multiplied by the number of biocontrol agents and by the area to be treated;determine the period of introduction of biocontrol agents and the frequency of introduction of biocontrol agents using a population dynamics equation, preferably the Lotka-Volterra or Nicholson-Bailey equation, and taking into account the accessibility of pests in the area to be treated for biocontrol agents, allowing to define the date at which the quantity of pest is greater than the treatment threshold leading to the introduction of new biocontrol agents.;

[0030] According to one embodiment, the method further includes the following steps to determine a preventive treatment:

[0031] - determine the type of continuous or punctual appearance of the pests;

[0032] - determine the date of appearance or emergence of pests using a population growth equation, preferably the inverse continuous-time exponential growth equation for continuous appearance or a degree-day accumulation equation for punctual appearance;

[0033] - determine the number of biocontrol agents to be introduced on the date of appearance of the pests according to the model equation, preferably by calculating the number of pests consumed per biocontrol agent, multiplied by the number of biocontrol agents and by the area to be treated for continuous and punctual appearance;

[0034] - determine the quantity of biocontrol agents to be introduced on the date selected by the user to ensure the presence of the quantity of biocontrol agents defined in the previous step at the time of the emergence of the pests using, preferably, the inverse continuous time exponential growth equation.

[0035] According to a second aspect, the invention relates to a biological control system for determining a curative or preventive treatment associated with a biocontrol agent against a pest in a given area, said system comprising:

[0036] - a user database adapted to receive user data, said user data including: at least one user data item concerning an indication relating to the pest,

[0037] - a modelling database adapted to receive modelling data, said modelling data comprising: at least one biological modelling data concerning information relating to the biocontrol agent, at least one ecological modelling data concerning an environmental condition associated with the biocontrol agent,

[0038] - a processor to implement a model equation, said model equation applying a mathematical relationship to said at least one biological modeling data and to said at least one ecological modeling data, said implementation including the use of said at least one user data to obtain final data relating to the number of biocontrol agents to be introduced into the determined area, the frequency of introduction of the biocontrol agents to be introduced into the determined area and the period of introduction of the biocontrol agents to be introduced into the determined area.

[0039] According to a third aspect, the invention relates to a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to implement the above-mentioned process. According to a fourth aspect, the invention relates to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to implement the above-mentioned process.

[0040] Brief description of the drawings

[0041] The purpose, object, and characteristics of the invention will become clearer upon reading the following description, made with reference to the figures in which:

[0042] Figure 1 schematically shows a biocontrol system according to one embodiment of the invention;

[0043] Figure 2 shows an extract from a user database, according to one embodiment of the invention;

[0044] Figure 3A shows a first extract from a first modeling database, according to one embodiment of the invention;

[0045] Figure 3B shows a second extract from the first modeling database, according to one embodiment of the invention.

[0046] Figure 4A shows a first extract from a second modeling database, according to one embodiment of the invention;

[0047] Figure 4B shows a second extract from the second modeling database, according to one embodiment of the invention;

[0048] Figure 5 shows a diagram of the steps of the biocontrol method, according to one embodiment of the invention;

[0049] Figure 6, Figure 7 and Figure 8 show the results obtained in the step of determining the frequency and period of introduction of biocontrol agents, according to one embodiment of the invention;

[0050] Figure 9 shows the results obtained in the step of determining the date of appearance of the first pests on the crop, according to one embodiment of the invention;

[0051] Figure 10 shows the results obtained in the step of determining the number of biocontrol agents that must be present on the defined pest emergence date, according to one embodiment of the invention.

[0052] Detailed description of the invention The detailed description below is intended to set out the invention in a sufficiently clear and complete manner, in particular by means of examples, but shall in no case be regarded as limiting the scope of protection to the particular embodiments and examples presented below.

[0053] Figure 1 shows a biocontrol system according to the invention. In the present invention, the term "biocontrol" means biological control by means of a biocontrol agent or biocontrol organism such as a macro-organism or a micro-organism (e.g., predator, parasitoid, nematode, bacteria, fungi, etc.). The biocontrol system is intended for use in a specific area, preferably a specific agricultural area, whether it be an open, closed, or vertical greenhouse, or an open field.

[0054] The user, preferably a farmer, may wish to use biocontrol for curative purposes in the presence of a crop pest, or for preventive purposes at a time prior to the appearance of a crop pest (for example at the time of sowing or planting a crop) to prevent the subsequent presence of this pest, and this, thanks to a biocontrol agent in both cases.

[0055] As shown in Figure 1, the biocontrol system 100 includes a computer server 102. The computer server 102 can be a computer, for example.

[0056] Computer server 102 hosts a user database 104, a selection database 106 and two modeling databases 108, 110.

[0057] As shown in Figure 2, user database 104 contains user data provided by a user such as a farmer.

[0058] This user data includes biological user data concerning the pest and environmental user data concerning the environmental conditions of the agricultural area in question.

[0059] User data includes at least one indication from the list below: 1) The pest species;

[0060] 2) Pest density;

[0061] 3) The type of culture;

[0062] 4) The infestation rate

[0063] 5) The infested surface;

[0064] 6) The type of covering;

[0065] 7) The type of soil;

[0066] 8) The geographical location of the area to be treated;

[0067] 9) The type of treatment desired;

[0068] 10) agricultural practices.

[0069] More specifically, the user must specify:

[0070] 1.1) The species of pest, past, present or future;

[0071] 1.2) The past, present or future presence of the pest;

[0072] 2.1) The past, present or future density of the pest;

[0073] 3.1) Past, present or future culture in the agricultural area;

[0074] 4.1) the index of past, present or future infestation by m 2 ;

[0075] 5.1) The infested area past, present or future;

[0076] 6.1) The type of agricultural area cover such as bare land, an open greenhouse, a closed greenhouse or a vertical farm;

[0077] 7.1) The type of soil as defined by the clay-silt-sand ratio of the infested plot in the past, present or future;

[0078] 8.1) The geographical location of the area to be treated;

[0079] 9.1) The type of treatment: preventive or curative;

[0080] 10.1) The use of inputs such as fertilizers, plant protection products, etc. past, present or future.

[0081] Selection database 106 includes a list of pest species, each associated with a specific biocontrol agent. Modeling databases 108 and 110 contain modeling data already present when the system was commissioned. Modeling database 108 includes modeling data related to the biocontrol agent. Modeling database 110 includes modeling data related to the pest. As shown in Figures 3A and 4A, the modeling data includes ecological modeling data 112 concerning the ecological conditions related to a biocontrol agent and ecological modeling data 116 concerning the ecological conditions related to a given pest, for a plurality of biocontrol agents and pests.Furthermore, the modeling data reflect both extreme and intermediate ecological conditions within the defined area in order to identify all possible ecological situations. As shown in Figures 3B and 4B, the modeling data also includes biological modeling data 114 concerning the biocontrol agent and biological modeling data 118 concerning the pest under consideration.

[0082] Thus, modelling databases 108, 110 include biological modelling data and ecological modelling data.

[0083] These biological and ecological modeling data are derived from experimental data and / or bibliographic data from the literature in the field of crop pests.

[0084] It is possible to use artificial intelligence-based tools to extract biological and ecological modeling data from bibliographic data.

[0085] Furthermore, modelling databases 108, 110 are dynamic databases insofar as biological and ecological modelling data can also be derived from previous results generated by the present process.

[0086] Modelling databases 108, 110 are also updated using bibliographic data and / or biological and ecological modelling data from experimental data and / or previous results generated by this process.

[0087] The biological modelling data 114 of type 1 in the modelling database 108 includes all data relating to the biology of biocontrol agents. They will be referred to as “A” hereafter and may include, but are not limited to, the following: number of pests attacked per day per biocontrol agent, number of pests attacked per day by a population of biocontrol agent, population growth rate, predation rate, parasitism rate, total fecundity, fecundity per day per female, functional response, survival rate, life cycle duration, base temperature, number of degree-days required to complete a full development cycle.

[0088] The biological modelling data 118 of type 2 in the modelling database 110 includes all data relating to pest biology. They will be referred to as “B” hereafter and may include, but are not limited to, the following: population growth rate, fecundity, life cycle duration, survival, pest tolerance index of the cultivated plant, base temperature, number of degree days required to complete a full development cycle, damage and treatment threshold, type of occurrence (continuous or punctual), proportion of pest development stages accessible to the biocontrol agent.

[0089] Ecological modeling data encompass all factors that can influence biological modeling data. These factors may include, but are not limited to, the following and are designated from C to S: temperature (C), humidity (D), photoperiod (E), nitrogen (G), phosphorus (H), or potassium (I) levels in the soil, proportion of sand in the soil (J), proportion of clay in the soil (K), infestation rate (L), density of biocontrol agents (M), proportion of cannibalism (N), superparasitism (O), multiparasitism (P), developmental stage (Q), and past (R), present, or future application of a chemical or organic input (S). This list is not exhaustive and will be supplemented by the discovery of new influencing factors from experimental data and / or bibliographic data from the literature on crop pests.Figures 3A and 3B show an extract from modelling database 108 with some of the modelling data used in the example described below.

[0090] Figures 4A and 4B show an extract from modelling database 110 with some of the modelling data used in the example described below.

[0091] Figure 1 shows three electronic devices 120, 122, 124 used by three different users, as an example. It is clear that the number of users and corresponding electronic devices can be unlimited. In this description, the term electronic device refers to any electronic device with a human-machine interface, such as a screen and keyboard, that allows connection to a computer server. Thus, the electronic device 120, 122, 124 could refer, for example, to a desktop computer, a laptop computer, a mobile phone, or a tablet computer.

[0092] Each electronic device 120, 122, 124 is connected to the computer server 102 via the internet network 126. A user can therefore enter user data into the user database 104 using an electronic device 120, 122, 124 via the internet network 126.

[0093] Server 102 also includes a processor 128. Processor 128 processes an algorithm in the form of model equations designed to apply a mathematical relationship to the biological and ecological modeling data in the modeling databases 108 and 110. These model equations are implemented using the user's biological and ecological data from the user database in order to simulate the effect of ecological factors on: the population dynamics of living organisms, i.e., biocontrol agents and pests; the ability of biocontrol agents to consume, parasitize, or infect pests; and the duration, survival, and growth of populations of living organisms, i.e., biocontrol agents and pests. After processing the model equations, system 100 obtains final data relating to the number, frequency, and timing of biocontrol agent introductions.

[0094] The 128 processor is also suitable for performing calculations of mathematical formulas used in curative and preventive treatment.

[0095] Detailed formulas in the curative treatment: a) the quantity of biocontrol agents to be introduced

[0096] The first formula is used to define the quantity of biocontrol agents to be introduced during cultivation when the pest is already present and relates to the following equation:

[0097] [Math 1]

[0098] N ag = A(M) * M * Surface to be treated

[0099] With N ag , the number of biocontrol agents to be introduced, M the density of biocontrol agent and A(M) the result of a model equation which determines the number of pests consumed / parasitized / infected per day as a function of the ecological factors of the area to be treated and the density of biocontrol agents.

[0100] As observed, the number of biocontrol agents to be introduced across the treatment area depends on the biocontrol agent density, that is, the number of biocontrol agents per unit area. Indeed, competition within farms, primarily intraspecific in the context of biocontrol, plays a major role in pest regulation. This competition is well-documented in the field. However, it is difficult to incorporate into biocontrol programs without mathematical modeling.

[0101] For example, a single female Coccinellidae Harmonia axyridis (Non-patent literature - Reference 1) can consume 100 aphids per day when 500 aphids are available. However, a female will consume only 50 aphids per day when introduced with four other females of the same species.

[0102] In the context of a biocontrol program, the introduction of 5 ladybugs will not lead to the consumption of 500 aphids per day but to the consumption of 250. In order to take this parameter into account, a loop allowing the formula Math 1 to be iterated as many times as necessary is put in place and will stop iterating when the number of pests consumed by the population of biocontrol agent is equal to the infestation rate.

[0103] The mathematical relationship of the model equation A(M) is a statistical relationship of the multiple linear regression type. The mathematical tool used to obtain the model equation could, for example, be the R software (registered trademark).

[0104] The resulting model equation explains the type 1 biological modeling data, named A, as a function of the ecological modeling data, named C to S, and is written in the following form:

[0105] [Math 2]

[0106] A = Intercept + (C^S*C^S+C'^S'*C^S A2 +C”^S”*C^S A3 +C'”^S'”*C^S A4 ) with c^s, c'^s' and c”->s”, the impact coefficients of the parameters C^S on A, and the intercept, the coordinate of the point of intersection of the curve and the ordinate axis.

[0107] The impact or regression coefficient indicates the extent to which the value of a dependent variable (A) varies with the change in the value of the explanatory variable (C to S).

[0108] These coefficients are calculated using the least squares method.

[0109] Any function other than multiple linear regression that can explain the dependent variables A in terms of the independent variables C to S may also be used. b) The frequency and period of introduction of biocontrol agents

[0110] The second set of formulas is used to determine the frequency and timing of biocontrol agent introductions. This set of formulas allows for the determination of the population dynamics of crop pests and biocontrol agents over time, based on type 1 and 2 biological and ecological modeling data, user data, and the results of the previous step (step a). These formulas might, for example, be the Lotka-Volterra equation when the biocontrol agents are predators, or the Nicholson-Bailey equation when the biocontrol agents are parasites and / or parasitoids. These formulas are well-established.

[0111] These equations are as follows:

[0112] Lotka-Volterra equations:

[0113] [Math 3] dx / dt(t) = x(t) (a - c*p*y(t))

[0114] [Math 4] dy / dt(t) = y(t) (g*x(t)* cy) where t is time; x(t) is the number of pests as a function of time; y(t) is the number of biocontrol agents (here predators) as a function of time; dx / dt(t) and dy / dt(t) represent the variability of the populations of pests and biocontrol agents, respectively, over time; a is the intrinsic reproduction rate of pests in the absence of biocontrol agents); (3 is the mortality rate of pests due to the biocontrol agents encountered; Q is the reproduction rate of biocontrol agents as a function of the pests consumed and y is the intrinsic mortality rate of biocontrol agents in the absence of pests; c is the proportion of the pest population accessible to the biocontrol agent and is not described in the state of the art.This parameter is important to consider in a biocontrol program because not all developmental stages of crop pests are always available to biocontrol agents. Introducing biocontrol agents when pests are inaccessible would lead to the failure of the biocontrol program.

[0115] If c is less than 0.7, the frequency of introduction of biocontrol agents will be adjusted using the so-called growing degree day (GD) equation, which is known in the state of the art. This equation is as follows: [Math 5]

[0116] DJ = (Tmax+Tmin) / 2 - Tbase

[0117] Tmax is the maximum temperature observed daily

[0118] Tmin is the minimum temperature observed daily

[0119] Tbase is the temperature from which growth of the organism is possible, biological growth of the organism being zero below the base temperature.

[0120] Tbase and x are contained in type 2 biological modeling data.

[0121] Tmax and Tmin are estimated from the geographical location indicated in the user database and meteorological data extracted from freely accessible internet databases and known in the state of the art.

[0122] Each living organism requires the accumulation of a certain number of degree-days (x), as defined in type 2 biological modeling data, to reach each stage of its development. In this context, this equation allows us to determine the minimum interval between two introductions to ensure the presence of the pest stage susceptible to the biocontrol agent.

[0123] Équations de Nicholson-Bailey :

[0124] [Math 6] x(t + 1) = x(t) * g(x(t)) * [1 - (s + p) * f(E(t))]

[0125] [Math 7] y(t + 1) = x(t) * (1 - f( £ (t)))

[0126] Avec,

[0127] [Math 8]

[0128] E(t) = (a * p * y(t)) / (P + a * x(t)),

[0129] [Math 9]

[0130] G(x(t)) = exp(ri * (1 - (x(t) / K))),

[0131] [Math 10] f( E (t)) = (i + ( £(t) / k)r - k where x(t) is the number of pests at time t; y(t) is the number of biocontrol agents (here parasites or parasitoids) at time t; x(t + l) and y(t + l) represent the number of pests and biocontrol agents, respectively, at t + 1. The function g(x(t)) corresponds to the fraction of surviving pests, taking into account density-dependent survival among the pests, and depends on the intrinsic growth rate of the pests, denoted ri, the carrying capacity of the pests, K, and the number of pests at time t, denoted x(t). The function f(ei,t) is the proportion of the pest population that does not encounter biocontrol agents and depends on k as the aggregate parasitism risk, a as the biocontrol agent search rate, p as the maximum fecundity of the biocontrol agents, and y(t) as the number of biocontrol agents at time t.s is the susceptibility of pests to biocontrol agents (i.e., the proportion of parasitized pests that allow the production of offspring). The parameter p denotes the proportion of parasitized pests killed by the biocontrol agent without producing offspring.

[0132] In the case of curative treatment, the first application should take place as soon as possible. The frequency and timing of subsequent applications are then defined by the equations described above. When the number of pests reaches the treatment threshold—that is, the threshold at which treatment must be carried out to avoid reaching the damage threshold—the application of a quantity of biocontrol agents defined as described in step a) will be recommended.

[0133] If the number of pests after a development cycle is below the pest's damage threshold for the crop, no further introduction is recommended.

[0134] Detailed formulas for preventative treatment:

[0135] The objective of preventative treatment is to prevent the establishment of pests or the damage they cause if they are already present.

[0136] In the first step, type 2 biological modeling data are used to determine the type of pest emergence (continuous or episodic). A continuous emergence corresponds to a population growth over time. An episodic emergence suggests a massive arrival or the emergence of pests at a specific time.

[0137] For the next steps, pest data is derived from the past, provided that preventative treatment is applied before pest emergence / arrival. Past pest density and the date of past pest appearance are obtained from user data and / or bibliographic data from the literature on crop pests. a) Case of continuous occurrence

[0138] The first formula allows us to estimate the date of appearance of the targeted pests and relates to the inverse continuous-time exponential growth equation:

[0139] [Math 11]

[0140] Nt — 1 = Nt / exp(rr * t)

[0141] With Nt-1 corresponding to the number of pests at time -1, Nt the number of pests recorded at a certain time of year in the past and r r The pest growth rate is estimated using the following model equation:

[0142] [Math 12] r r = Intercept + (c^s*C^S+c'^s'*C^S A 2+c”^s”*C^S A 3+c'”^s'”*C^S A 4) with c^s, c'^s' and c”->s”, the impact coefficients of the parameters C^S on r r and the intercept, the coordinate of the point of intersection of the curve and the y-axis.

[0143] The impact or regression coefficient indicates the extent to which the value of the dependent variable r r varies with the variation of the value of the explanatory variable, i.e. from C to S.

[0144] These coefficients are calculated using the least squares method. Any function other than multiple linear regression that explains the dependent variables r r depending on the independent variables C to S can also be used.

[0145] Nt-1 is calculated retrospectively day by day until it equals 1. The number of days identified determines the assumed date of appearance and therefore the date of introduction of the biocontrol agents.

[0146] The second formula Math 1 described above allows the number of biocontrol agents to be introduced according to the conditions of the area to be treated on the date selected by the farmer and which must be prior to that indicated by the first formula Math 11. If no date is selected, the date identified by the first formula Math 11 will be used to make the recommendation of the number of biocontrol agents to be introduced.

[0147] The third formula below allows us to estimate the quantity of biocontrol agents to be introduced at the desired time, either before the emergence or the appearance of the pests determined by the first formula (Math 11), to ensure that the number of biocontrol agents defined by the second formula is reached at the time of the emergence or appearance of the pests determined by the first formula (Math 11). This formula relates to the inverse continuous-time exponential growth equation: [Math 13]

[0148] Nt — 1 = Nt / exp(rab * t)

[0149] With Nt-1 corresponding to the number of biocontrol agents at time -1, Nt the density of biocontrol agents calculated with the second formula, and r a b. The growth rate of biocontrol agents is estimated using the following model equation:

[0150] [Math 14] r ab = Intercept + (c^s*C^S+c'^s'*C^S A 2+c”^s”*C^S A3+c'”^s'”*C^S A 4) with c^s, c'^s' and c”->s”, the impact coefficients of the parameters C^S on r a b, and the intercept, the coordinate of the point of intersection of the curve and the y-axis. The impact or regression coefficient indicates the extent to which the value of the dependent variable r a b varies with the variation of the value of the explanatory variable, i.e. from C to S.

[0151] These coefficients are calculated using the least squares method.

[0152] Any function other than multiple linear regression that explains the dependent variables r a b. depending on the independent variables C to S can also be used.

[0153] Nt-1 is calculated retrospectively, day by day, up to the user-selected introduction date, i.e., the sowing date, planting date, or any date prior to the emergence or appearance date identified by the first Math 11 formula. The quantity Nt-1 of biocontrol agents at the defined date will be recommended. If no date is selected by the user, the default date will be the emergence or appearance date identified by the first Math 11 formula. b) Case of a one-time appearance

[0154] The first formula allows us to estimate the date of appearance of the targeted pests using the Math 5 equation known as growth degree days (DD) described previously.

[0155] The second formula, Math 1, described previously, determines the number of biocontrol agents that must be present at the time of pest emergence / appearance. It depends on the pest density recorded in the past.

[0156] The third formula, Math 13, is identical to the third formula developed for continuous pest emergence and allows for estimating the quantity of biocontrol agents to be introduced at the desired time, i.e., a period prior to the emergence or appearance of pests determined by the first formula. This ensures that the number of biocontrol agents defined by the second formula is reached at the time of pest emergence or appearance determined by the first formula. This formula relates to the inverse continuous-time exponential growth equation. The minimum data required from the user for the algorithm to provide a result is the pest species. Temperature and humidity parameters will be automatically populated based on the geographical location of the delivery address if no information is provided by the farmer.The data concerning the infestation in the area to be treated will be taken by default from the biological modeling database 2 and will be considered as maximum. If no area to be treated is specified, the recommendations will be made by Mr. 2 Obviously, the reliability rate of the results is higher when the amount of data provided by the user is greater.

[0157] The invention relates to a decision support tool based on mathematical formulas, including a mathematical model equation, to define the quantity, frequency and period of introduction of a biocontrol agent to be used on a crop / pest pair, based on user input data and modeling data.

[0158] The process according to the present invention is described in Figure 5 and comprises the following steps.

[0159] In step 200, the farmer provides the biocontrol system 100 with user data within the user database 104 shown in Figure 2. The user data includes at least the pest species.

[0160] Example 1: As of May 1, 2023, the user declares that their pepper crop is impacted by the Myzus persicae aphid over an area of ​​3000m² 2 The delivery address is in Nantes. The infestation level is high, with 200 aphids / m². 2 .

[0161] In a step 202, the processor 118 uses the selection database 106 to search for and select the name of the biocontrol agent corresponding to the pest species entered as biological user data.

[0162] Example 1: The biocontrol agent selected to control the pepper aphid (Myzus persicae) is the ladybug (Adalia bipunctata). In step 204, the farmer enters user data and indicates at least one of the following: whether they want a curative or preventive treatment (curative treatment being selected by default), for the agricultural area in question, the delivery address and the area to be treated, and recommendations per m 2 being performed by default.

[0163] Example 1: The user wants a curative treatment.

[0164] Case of curative treatment:

[0165] Therefore, in step 206, we determine the number of biocontrol agents to be introduced according to the conditions in the area to be treated.

[0166] For example: the formula used is

[0167] [Math 15]

[0168] N ag= A(M) * M * Surface to be treated with the model equation A(M) for the pair Adalia bipunctata / Myzus persicae: [Math 16]

[0169] The user data is passed to the described model equation, allowing for the following recommendation: the introduction of 5 adult A. bipunctata per m 2 that's 15,000 ladybugs for 3000m 2 is recommended in view of the conditions in the area to be treated.

[0170] In step 208, the period of introduction and frequency of introduction of biocontrol agents are determined as detailed above.

[0171] Example 1:

[0172] The initial introduction of biocontrol agents must, in the case of curative treatment, be carried out as soon as possible. The frequency and timing of subsequent introductions are estimated using Lotka-Volterra or Nicholson-Bailey models, which allow for the modeling of pest and biocontrol agent population dynamics over time.

[0173] User data is passed to the described model equation.

[0174] Example 1: Figures 6, 7 and 8 show the results obtained after using the Lotka-Volterra equations and considering the absence of reintroduction of biocontrol agents without the addition of alternative food as shown in Figure 6, the reintroduction of biocontrol agents when the treatment threshold is reached as shown in Figure 7 and the absence of reintroduction of biocontrol agent with the addition of alternative food allowing to maintain 1 ladybug / m2 as shown in Figure 8.

[0175] Two recommendations are made:

[0176] 1) reintroduce 3 Adalia bipunctata by m 2 18 days after the first introduction, then repeat the operation every 12 days;

[0177] 2) Introduce an alternative food source 3 days after the introduction of Adalia bipunctata to ensure the presence of 1 ladybug per m 2 .

[0178] Example 2: The user wants preventative treatment for an area of ​​3000m² 2 to combat Myzus persicae on peppers by January 1, 2024. The delivery address is still in Nantes. The previous year, the user reported the presence of 200 aphids / m² 2 May 1, 2023.

[0179] Case of preventive treatment:

[0180] A first step is to determine whether the appearance of the pests is continuous or sporadic.

[0181] Example 2:

[0182] Option 1: Biological database 2 informs us that the appearance of aphids is continuous. c) Case of continuous appearance

[0183] In step 210, the formula allows us to estimate the date of appearance of the targeted pests and relates to the inverse continuous-time exponential growth equation: [Math 17] Nt-1=Nt / exp(r*t )

[0184] In example 2, Nt = 200, and rr is defined by the following model equation: [Math 18] r = -0.104118 + 0.017794 * Average daily temperature

[0185] User data is passed to the described model equation.

[0186] Figure 9 shows the result of the formula and indicates that the first Myzus persicae appeared on February 24, 2023.

[0187] In step 212, the same formula as that used in step 206 is used and allows us to determine the number of biocontrol agents that must be present on the date defined in step 210.

[0188] Example 2: The results indicate that 1006 Adalia bi punctata should be present on February 24, 2024, for 3000m 2 surface.

[0189] In step 214, the formula allows us to estimate the quantity of biocontrol agents to be introduced at the desired time, i.e., the period prior to the emergence or appearance of pests determined in step 210, to ensure that the number of biocontrol agents defined in step 212 is reached at the time of emergence or appearance of pests. This formula relates to the inverse continuous-time exponential growth equation: [Math 19]

[0190] Nt-1=Nt / exp(r a b*t )

[0191] In example 2, Nt = 5 and r a best defined by the following model equation: [Math 20] r = -0.049968 + 0.008177 * Average daily temperature

[0192] User data is passed to the described model equation.

[0193] No information on the preferred introduction date was provided by the user, therefore the recommendation is to introduce 1006 Adalia bipunctata on February 24, 2024, at 3000m 2 surface area to be treated. Example 2:

[0194] Option 2: Biological database 2 informs us that the aphid infestation is sporadic. d) Case of a sporadic infestation

[0195] In step 216, the formula allows us to estimate the date of appearance of the targeted pests using the degree-days of growth (DD) equation.

[0196] Example 2:

[0197] According to biological modelling data B, Myzus persicae needs 200 degree-days to complete its cycle.

[0198] The accumulation of degree days depending on the conditions in the introduction area indicates that aphids will emerge from March 15, 2024.

[0199] In step 218, the same formula used in step 206 is used to determine the number of biocontrol agents that should be present at the time of emergence or appearance of pests.

[0200] Example 2:

[0201] According to the formula presented in step 206, 5 ladybugs / m 2 that's 15,000 ladybugs for 3,000m 2 must be present from March 15, 2024 to control the aphid population before it damages the crop.

[0202] In step 220, the same formula as that used in step 214 is used to estimate the quantity of biocontrol agents to be introduced at the desired period, i.e. the period prior to the emergence or appearance of pests determined in step 216, to ensure that the number of biocontrol agents defined in step 218 is reached at the time of the emergence or appearance of pests.

[0203] In example 2, the result indicates that 1.1 ladybugs / m² should be introduced. 2 i.e. 3347 ladybugs in the 3000m2 area to be treated on February 28, 2024 as shown in figure 10.

[0204] The present invention makes it possible to take into account environmental conditions to respond to the attack of crop pests by introducing biocontrol agents such as macro-organisms or micro-organisms in an appropriate manner.

[0205] The present invention thus makes it possible to take into account the impact of environmental factors on the ecology of biocontrol agents in order to provide a decision tailored to the farmer's specific situation. This decision includes recommendations regarding the species of biocontrol agent to be introduced, the dose, and the frequency or period of introduction. The advantage of the present invention lies in improving the effectiveness of existing biocontrol programs.

[0206] The embodiments described above are given as examples only.

[0207] Non-patent literature

[0208] Reference 1: Wu et al. 2018 - Functional responses and intraspecific competition in the ladybird Harmonia axyridis (Coleoptera: Coccinellidae) provided with Melanaphis sacchari (Homoptera: Aphididae) as prey. Eur. J. Entomol. 115:232-241.

Claims

Demands 1. A computer-implemented method for determining a curative or preventive treatment associated with a biocontrol agent against a pest in a given area, said given area being likely to contain at least one pest, said method comprising the following steps: - provide at least one user database (104) suitable for receiving user data, said user data including: at least one user data item concerning an indication relating to the pest, - provide at least one modelling database (108, 110) suitable for receiving modelling data, said modelling data including at least one biological modelling data item (114, 118) concerning information relating to the biocontrol agent, at least one ecological modelling data item (112, 116) concerning an environmental condition associated with the biocontrol agent, - provide a suitable model equation to apply a mathematical relationship to said at least one biological modelling data (114, 118) and said at least one ecological modelling data (112, 116); - implement the model equation using said at least one user data to obtain final data relating to the number of biocontrol agents to be introduced into the determined area, the frequency of introduction of the biocontrol agents to be introduced into the determined area and the period of introduction of the biocontrol agents to be introduced into the determined area.

2. A method according to claim 1, said at least one user data item comprising at least one indication from the list below: The species of pest, past, present or future; The past, present or future presence of the pest; The past, present or future density of the pest; The index of past, present or future infestation by m 2 ; The infested area, past, present or future; The type of agricultural land cover; The type of soil of the infested plot in the past, present or future; The geographical location of the area to be treated; The type of treatment: preventive or curative; The use of inputs past, present or future.

3. A method according to any one of the preceding claims, said at least one biological modelling data (114) comprising at least one indication relating to the biocontrol agent from the list below: number of pests attacked per day by biocontrol agents, number of pests attacked per day by a population of biocontrol agent, population growth rate, predation rate, parasitism rate, total fecundity, fecundity per day per female, functional response, survival rate, life cycle duration, base temperature, number of degree days required to complete a full development cycle.

4. A method according to any one of the preceding claims, said at least one biological modelling data (118) comprising at least one indication relating to the pest from the list below: population growth rate, fecundity, life cycle duration, survival, pest tolerance index of the cultivated plant, base temperature, number of degree days required to complete a full development cycle, damage and treatment threshold, type of appearance, proportion of pest development stages accessible to the biocontrol agent.

5. A method according to any one of the preceding claims, said at least one ecological modeling data point (112, 116) comprising at least one indication from the list below: temperature (C), humidity (D), photoperiod (E), nitrogen (G), phosphorus (H) or potassium (I) content in the soil, proportion of sand in the soil (J), proportion of clay in the soil (K), infestation rate (L), biocontrol agent density (M), proportion of cannibalism (N), superparasitism (O), multiparasitism (P), stage of development (Q), past (R), present or future application of a chemical or organic input (S).

6. A method according to any one of the preceding claims, said mathematical relationship being based on a multiple linear regression model.

7. A method according to any one of the preceding claims, further comprising the following steps for determining a curative treatment: determining the number of biocontrol agents to be introduced according to the model equation, preferably by calculating the number of pests consumed per biocontrol agent based on biological modeling data of the biocontrol agents and the pests, and based on ecological modeling data of the area to be treated, multiplied by the number of biocontrol agents and by the area to be treated;determine the period of introduction of biocontrol agents and the frequency of introduction of biocontrol agents using a population dynamics equation, preferably the Lotka-Volterra or Nicholson-Bailey equation, and taking into account the accessibility of pests in the area to be treated for biocontrol agents, allowing to define the date at which the quantity of pest is greater than the treatment threshold leading to the introduction of new biocontrol agents.; 8. A method according to any one of the preceding claims, further comprising the following steps for determining a preventive treatment: - determine the type of continuous or punctual appearance of the pests; - determine the date of appearance or emergence of pests using a population growth equation, preferably the inverse continuous-time exponential growth equation for continuous appearance or a degree-day accumulation equation for punctual appearance; - determine the number of biocontrol agents to be introduced on the date of appearance of the pests according to the model equation, preferably by calculating the number of pests consumed per biocontrol agent, multiplied by the number of biocontrol agents and by the area to be treated for continuous and punctual appearance; - determine the quantity of organisms to be introduced on the date selected by the user to ensure the presence of the quantity of biocontrol agents defined in the previous step at the time of the emergence of the pests using, preferably, the inverse continuous time exponential growth equation.

9. A biological control system for determining a curative or preventive treatment associated with a biocontrol agent against a pest in a given area, said system comprising: - a user database (104) adapted to receive user data, said user data comprising: at least one user data item concerning an indication relating to the pest, - a modelling database (108, 110) adapted to receive modelling data, said modelling data comprising: at least one biological modelling data item (114, 118) concerning information relating to the biocontrol agent, at least one ecological modelling data item (112, 116) concerning an environmental condition associated with the biocontrol agent, - a processor to implement a model equation, said model equation applying a mathematical relationship to said at least one biological modeling data (114, 118) and to said at least one ecological modeling data (112, 116), said implementation including the use of said at least one user data to obtain final data relating to the number of biocontrol agents to be introduced into the determined area, the frequency of introduction of the biocontrol agents to be introduced into the determined area and the period of introduction of the biocontrol agents to be introduced into the determined area.

10. Product computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 8.

11. Computer-readable recording medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 8.

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