WO2022027151A1 - System for tracking and monitoring pollinators, and methods and devices of same - Google Patents

Abstract

The invention relates to a predictive system for tracking and monitoring pollinating agents, such as bees, and other insects, and to related devices and methods, which allow a prediction to be made on the basis of data obtained in the field, calculating a pollinator index (pollination index) of beehives to improve crop production, which is valuable information for farmers and beekeepers, respectively, wherein the development of the system is focused on understanding how the data obtained from the activities and characteristics of the pollinators (for example, the bees) allow progress within the complex process of pollination to be monitored and understood in order to generate a positive impact on the production of different pollinated crops.

Description

POLLINATOR MONITORING AND MONITORING SYSTEM; METHODS AND DEVICES OF THE SAME

field of invention

The disclosure relates generally to the field of ecology and more specifically to new and useful systems and methods for tracking and/or monitoring bees and other pollinators.

State of the art

Beekeeping is a great challenge: the owner of the hives must constantly monitor the bees, which in itself represents a great wear and tear for the beekeeper. In addition, this also implies stress for the bee, entering a revision paradox. The beekeeper must review several parameters within the hive, among which are foci to take into account the condition of the queen, the population of the hive and diseases. Even if this is only a qualitative measure, the problems become apparent when directly observed and it depends on the knowledge of the beekeeper the possibility of survival for the hive.

The dependency between the queen, her population and the amount of food is high. In general, a queen will lay more eggs if there is more food and this will result in an increase in population; however, it will stop laying eggs if it notices that its population is declining. Population is always a factor in the hive equation, no matter what parameter you want to measure.

So how can we help the bees not die? One way to address the problem is to monitor and support him in the periods where he needs it, allowing him to carry out his activities, but in the safest possible environments.

1. Efforts have recently been made to carry out experimental approaches that allow monitoring of some specific colonies

or even try to make an estimate that allows us to reconstruct the flight of the bees and their trajectories

even using the help of AI in the process.

For example, US10064395B2 describes a system configured to monitor and track the health and productivity of a hive. Information associated with the environmental conditions around the hive is monitored along with the conditions within the hive. This information is processed through a control system and compared with the expected reference values. Communication data is transmitted by the control system to a remote device to notify the beekeeper of the current and past health and productivity of the hive. The beekeeper can remotely operate one or more functions to affect the hive. In addition, an electronic monitoring device is used to track the number of bees leaving and entering the hive. A double gate design is used to determine each bee's direction of travel in order to determine a more accurate reflection of hive health.

On the other hand, in CN210181653U, the utility model discloses a cloud computing-based Artificial Intelligence (AI) beekeeping system, comprising a data acquisition terminal installed in each hive and used to acquire the temperature, humidity, gas, audio, video, presence of bees, weight and position, in the hive; the information acquisition terminal is arranged in the bee field and is used to acquire meteorological data and honey source data in the bee field; the data transmission device is connected with the data acquisition terminal and the information acquisition terminal and is used to upload acquired data; and the cloud server is connected with the data transmission device and is used to receive and store the collected data, analyze the bees, hives and bee field according to the collected data and historical data, and send the result of the analysis to the portable terminal of the beekeeper. The utility model reveals a real-time monitoring dynamic change of the hive and the bee, knows the status of the bee, and the information of the hive adjustment improves the breed and honey-gathering ability of the bee to improve honey production.

Both cases mentioned above focus on direct information from hives and their environment, mainly associated with the development of beekeeping. However, at the moment there would not be a complex monitoring system that allows adequately estimating, on the one hand, the behavior of different hives, obtaining comprehensive information from hives and combining said information with that from the environment in real time, and that, on the other hand, allows make a prediction associated with the behavior of these pollinating agents for the benefit of the crops and the owners of the fields, in addition to improving the knowledge of the hives to the beekeepers themselves, to finally achieve an improvement in production through a predictive model based on the monitoring of pollinating agents.

Description of achievements

The following description of embodiments (eg, including variations of embodiments, exemplary embodiments, specific examples of embodiments, other suitable variants, etc.) is not intended to be limited to these embodiments, but rather to enable any skilled person in the technique to make and use.

Embodiments (eg, of methods and/or systems, etc.) may include measuring the population of bees leaving, which has a relationship to the total number of bees in the hive; By knowing how many bees go out and how much food they bring, we can estimate the health and quality of the hive.

Pursuing at least this mission, embodiments may include monitoring technologies that are responsive to each other and provide one or more of at least the following features:

• Measurement of local environmental variables, be it an apiary or a crop field: o Ambient temperature, degrees Celsius (T °); o Environmental humidity, as concentration of water in the air (H%); o Radiation, including: solar radiation (W/m2), visible light (Lz) as the number of lumens, and any other suitable radiation source, received at a particular point during a particular period of time; or Wind (V

as the speed and direction of the windings that occurred at a particular point during a particular period; o CO2 concentration (C%), as concentration of carbon dioxide dissolved in the air; o Sea level; or Geolocation.

• Measurement of Environmental Variables of a hive in particular: o Ambient temperature in degrees Celsius (T °); o Environmental humidity, as concentration of water in the air (H%); o Apiquera Light (Lz) as the number of lumens received at a particular point during a particular period of time; o Concentration of CO2 (C%), as concentration of carbon dioxide dissolved in the air inside the hive; or noise; and o hive weight and any other related physical measurements.

• Measurement of flow from hive to hive: o Passage of bees leaving (A —>); o Entrance passage for bees (A <-); o Entering bees with pollen (AP <-); o Passage of bees with pollen coming out (AP -^); o Identification and learning of patterns of each hive; o Identification of diseases within the flow, presence of varroa, deformed wings; o Identification of normal and abnormal behaviors; o Identification of looting, insect attack.

• Measurement of the flow of bees in the field: o Bees passing a specific point; o Multipoint coordination; o Estimation of insect abundance; o Identification of flying insects; o Correlation of yield with pollination levels; or noise. o Identification of classes, family or/and species of pollinators

• Association of data collected to a specific user o We learn the effect between the different variables and bee hives (apiary); o We give specific advice for your apiary, based on global information for local use; o The particularities of each family and place generate better learning for our software, increasing the range of advice and procedures that can be used; o Use it in the flowering and pollination process, to calculate and predict yield; o Land evaluation using global public databases, for the analysis of crops in the field, calculating a useful vegetation index to develop the pollination strategy. • Smart analysis to generate recommendations and alerts based on collected data

• Historical record: o Past illnesses; o Propensities of the hive; o Risky dates.

• Meeting platform to offer or request services related to beekeeping: o Rental of beehives for online fruit pollination; o Search for land to pollinate; o Search for honey collection sites (products produced from the hive).

• Quality, safety and legality: o Ensuring beekeeper/producer business development in a simple way; o Establish agreements and contracts that facilitate payments; o Bee-friendly contracts.

• Easy to install systems: o Automation for independent operation connected by internal network; o Adaptable, simple and independent.

• Strength and durability: o We build with resistant materials and are aware of the environmental adversities that it must suffer

• Easy to use o Linking to the application and monitoring starts from the beginning; o Association with artificial intelligence that allows predictions and advice to users (for example, high pollination zone, etc.), including any other benefits of current devices and technology that are useful to users.

However, any of the foregoing suitable features may be provided, implemented and/or otherwise used in portions of embodiments of the methods and/or systems.

Consistent with the development of our technology, embodiments may include one or more than three different devices to measure variables (eg those described in this document).

Embodiments of an intelligent control gate (ICD) may include:

Embodiments of one or more ICD devices may include an adapter designed for the hive entrance, consisting of a gantry providing a single entrance or passageway to the hive, implemented with different sensors for bee flow detection and capture. of different related parameters, and that in addition the ICD allows opening and closing the inlet or adjusting it within the dimensions of the device. In examples, the technology is an adaptation to the existing hive that is placed on the board (where the bees come and go) whose purpose is to monitor (eg count) the number of bees entering and leaving the hive, and their condition (with or without pollen, state, size, etc.), such as recording the transit of bees in / out of the hive.

In examples, an ICD can be designed so that the information is collected in a more organized way (the rails allow the bees to enter and exit), where the porch or walkway is the key to the success of the monitoring system.

In specific examples, the ICD may include one or more of: a sensor array (for example, a camera, thermal imager), a microprocessor, a data transmission system, a power source, where the sensor array may include different types of sensors, including: optical, thermal, sound, mechanical, chemical, electrical, physical, and/or any other suitable sensor; wherein the microprocessor may include any handheld microprocessor (eg, raspberry pi or any other suitable handheld microprocessor); wherein the data transmission may allow data transmission to a remote central station and/or other suitable components within a certain range; and where the power source may include one or more of: battery, AC, DC, on-grid, off-grid, and any other suitable available power source, including but not limited to: solar power, wind power, thermal power , etc., and where the power source can last for at least one transmission year (and/or any suitable length of time).

In examples, the ICD has been tested in the field under different environmental conditions, as well as at different times of day. In specific examples, the ICD may implement and/or provide any of the following conclusions:

1. Measuring the environmental variables (T, H, Lz, V ->) allows us to associate visible patterns in the flow of bees, which is providing very relevant information.

2. Sending data in a certain time frequency window allows different patterns to be predicted and obtained (for example, for 3 minutes every 15 minutes, approximately 4 times in an hour).

3. The power system (eg solar, battery) can provide the power to measure and send data.

4. The information receiving antennas can connect up to at least 6 devices.

5. We have at least 15 min to 50 hours of strengthening our artificial intelligence and it has quite good results.

6. There are measurement times that do not deliver the information that we observe in the field, bee flows not only respond to environmental variables.

7. The assembly of this technology is very fast, like Plug-n-play (for example: within an hour or less).

8. The maximum and minimum temperatures that this machinery supports are: 10 - 50 degrees centigrade.

9. The number of entries can be changed (enlarge or reduce).

However, the ICD embodiments may be configured in any suitable manner.

By way of example, an ideal embodiment of the invention comprises an ICD, made up of the following components, but not limited to:

programmable microprocessor

Removable memory card (microSD 16GB or higher) • 5 Mpx or higher camera.

• Flexible for camera and microprocessor connection

• Temperature and humidity sensor

• Electronics container housing

• Gantry to channel the single entrance and exit to the hive, which may also include a device with an adjustable entrance.

Embodiments of an intelligent verification source (ICF) may include:

Embodiments of one or more ICF devices can be designed similar to a bee drinker in crop fields, such as being able to monitor the number of bees stopping to drink water in the middle of their transit to/from the hive. , within a certain time, period of time.

In specific examples, it may include one or more of: an array of sensors (for example, a camera, thermal imager), a computer module, a microprocessor, a data transmission system, a power source, where the array of sensors may include different types of sensors, including: optical, thermal, sound, mechanical, chemical, electrical, physical, and/or any other suitable sensor; wherein the computer module may include a Single Board Computer (SBC) (eg, a Raspberry Pi) or any other suitable computer module; wherein the microprocessor may include any portable microprocessor, a programmable microprocessor (eg Arduino microprocessor) and/or any other suitable portable microprocessor); where data transmission allows data to be transmitted to a remote central station within a certain range, for example a long transmission network, from the ICD or ICF, to the central transmission, to a cloud and then to a cell phone or computers ; and where the power source may be: battery, AC, DC, on-grid, off-grid, and/or any other suitable available power source, including but not limited to: solar power, wind power, thermal power, etc. ., and where the power source can last for at least one transmission year (and/or any suitable period of time).

The embodiments may work for crop field measurement. In specific examples, the ICF has been tested under different environmental conditions, as well as at different times of the day. In specific examples, the ICF may implement and/or provide any of the following findings:

1. Measuring the environmental variables (T, H, Lz, V ->) allows us to associate visible patterns in the flow of bees, which is providing very relevant information;

2. Sending data at different time frequencies allows different patterns to be predicted and obtained (for example, operating during the day in daylight, from sunrise to sunset, especially from 10 a.m. to 5 p.m.);

3. The power system (eg solar, battery) can provide the power to measure and send data;

4. Information receiving antennas can connect up to at least 2 devices;

5. Its maximum connection distance is about 120 meters.

6. There are measurement times that do not deliver the information that we observe in the field, bee flows not only respond to environmental variables.

7. Quick assembly 8. Characteristics that encourage bee attraction still need to be developed.

9. Bees drink from the water

10. The water can get hot and that is a long-term problem.

11. The system can be tailored to the flowers.

12. Get the captured noise from the flowers.

13. Get data from at least four points per plant

However, the ICF embodiments may be configured in any suitable manner.

By way of example, an ideal embodiment of the invention comprises an ICF, composed of the following components, but not limited:

• Programmable microprocessor

• Removable memory card (microSD 16GB or higher.

• 5 Mpx or higher camera.

• Flexible for camera and microprocessor connection

• Temperature and humidity sensor

• Microphone of at least -30db, which can also contain at least one input/output connector (for example, microllSB)

• Electronics container housing, which includes an appendix for the location of the microphone (for example, with a design that emulates a flower), and which may also contain a container to contain the liquid.

• Liquid container container.

Alternatively, the aforementioned configuration is equivalent to the ICB, except for the adaptable module related to the drinker or liquid container.

Embodiments of a Central Environmental Station (ASO) may include:

Embodiments of one or more ASC devices may, as another device including one allow reception of information provided from one or more ICDs and/or ICFs, and/or transmit the data to one or more other devices: cell phone, computer , server, other antenna and / or any other device suitable for capturing field data, processing information and / or displaying results that can be analyzed by experts and / or provide additional specific recommendations to the user.

In specific examples, ASCs are made of environmentally resistant, outdoor-ready material and can be made from CNC-machined milled plywood and/or laser-cut stainless steel (but can be additionally or alternatively constructed with any suitable material). An ASC may include one or more of: a sensor array (for example, a camera, thermal imager), a microprocessor, a data transmission system, a power source, where the sensor array may include different types of sensors , including: optical, thermal, sound, mechanical, chemical, electrical, physical sensor and/or any other suitable sensor; wherein the microprocessor may include any handheld microprocessor (eg, raspberry pi or any other suitable handheld microprocessor); where data transmission allows data to be received and/or transmitted to a remote central station and/or a suitable device within a certain range, e.g. a transmission network long, from ICD or ICF, to the central broadcast, to a cloud, and then to cell phones or computers; and where the power source may be: battery, AC, DC, on-grid, off-grid, and any other suitable available power source, including but not limited to: solar power, wind power, thermal power, etc. and that it lasts for at least one transmission year (and/or for any suitable period of time).

ASC implementations can respond to receiving data from an ICD and/or ICF while analyzing local environmental variables:

1. Measure the environmental variables (T, H, Lz, V ->), although it does not represent a great measurement challenge, its correlation with the information from the other two instruments is crucial.

2. Sending data under the time frequencies allows to predict and obtain different patterns, which frequencies depend on the species, for example, we can measure the activity of flower and insect activity every 15 minutes for 5 hours daily. Additionally or alternatively, we may measure activity once an hour, for at least seven hours, every day or upon request.

3. The battery associated with the solar panel power system can provide the power to measure and send data throughout the day.

4. You can connect up to at least 50 ICD or ICF units.

5. Its maximum connection distance is about 100 meters.

The connection system between the ASC (Environmental Station Central), the ICD and/or ICB/ICF devices allows the transmission of data from the field. The flow of information from the devices is exemplified in figure 4 where it is observed that the versatility of the connection, which allows extracting from any device as long as they have access to a network. The diagram in figure 4 shows some of the connection configurations that can be established for data extraction from any place where the system is installed.

The connection system allows for an indefinite connection time with the ability to automatically reconnect in case of outages. This system has the ability to direct the flow of information at convenience through appropriate configurations as the case may be.

By way of example, an ideal embodiment of the invention comprises an ASC, composed of the following components, but not limited to:

• A computer module, which also includes a programmable microprocessor to improve data processing and device performance.

• A first removable memory card, of at least 32 Gb or greater

• A second removable memory card, of at least 64 Gb or greater, to generate backups at least one temperature and humidity sensor

• at least one solar radiation sensor

• at least one sensor to measure wind speed

• at least one sensor to measure wind direction

• an electronics housing

Regarding the measurement time of the different devices arranged in the field, (ICD, ICF, ICB, ASC and others), although the minimum time suggested to obtain an adequate reading and thus reduce the generation of artifacts due to the time of obtaining data, is 10 seconds, we have observed that the appropriate time to establish a reliable measurement over time is ideally between 30 to 60 seconds, at regular intervals (for example, every hour) ideally throughout the entire working day. bee work. Likewise, a suitable regular observed interval is from 8:00 a.m. to 8:00 p.m., which can be reduced to at least between 10:00 a.m. and 5:00 p.m., which corresponds to the ideal minimum observation window.

Embodiments may additionally or alternatively include:

Hive monitoring can include:

In specific examples, for this, the ICD is placed at the entrance of the hives, which allows control of their entry and exit, and/or the amount of pollen they bring and their different problems. In specific examples, when a growth pattern is not normal, the beekeeper is warned, recommendations are given according to what is observed and according to the pattern that the ICD is observing, according to the parameters and learning of our trained monitoring system. , facilitating the work, and avoiding having to check all the hives manually.

Accomplishments may include indicating which hives should be checked and/or which treatment should be applied, in accordance with the recommendations provided. In specific examples, although the treatment is very particular for each hive, the measurement of the long-term population provides a much more detailed, assertive and increased possibility of understanding the observed patterns, thus avoiding the detriment of the hive and its population.

Monitoring outside the hive may include:

In specific examples, when measuring from the entrance of a hive, we could know how the population was and how the bees were working, but we could not know where they were working, a factor of great importance when it comes to transmitting diseases, pollinating crops or applying, insecticides.

In specific examples, the development of ICF stems from the need to understand how the bee was behaving in the field, which led to opening a monitoring market for fruit and seed producers, who can now know how pollination works in the different areas of its territory, cultivation.

Specific examples may include monitoring the health of bees in the field and hive, but it can also integrate the producer into the chain, increasing responsibility for these fragile individuals, where monitoring enables safety for both the producer and the beekeeper.

The beetle monitoring social network may include:

Building on the security that incarnations can provide, incarnations can include a social network that allows growers and beekeepers to come together on a platform, such as where the grower can ask beekeepers to pollinate their farms and the beekeepers in turn can decide which and when to pollinate them, increasing the number of interactions in the market and the availability of hives, all mediated by the platform. In specific examples, the platform may grant one or more of: • Data security for both users: producer and beekeepers.

• Possibility of "hiring" hives with one or more portions of realizations and known parameters.

• Contracts and obligations to both, with bee-friendly policies to protect the entire production chain.

• Sale of pollination stimulants and bee care products.

• Common platform to learn, share and comment on pollination and beekeeping techniques, availability of data to learn

• Collaborative work through network alerts, alerts and presence of apiaries

As where the only certain possibility to rescue the decline of bees is to consolidate the beekeeping chain with known actors who work under the fundamental premise of caring for these insects.

The scope of the invention can contemplate the development of a platform for managing pollination, articulating communication between producers and beekeepers, reducing the efforts of the pollination process and being able to generate benefits for the beekeeper, increasing the rental price of the hives that have been monitored and facilitating the integration of technology for the control and management of hives, optimizing time and resources. On the other hand, the risks for producers of deficient pollination processes that reduce crop yields are reduced, allowing for increased yields, increased income per hectare and traceability of pollination processes.

Big Data:

Since not all bees are found in hives, incarnations can work to develop a public good to monitor native bees and other insects in parks and cities, so they can be mapped and management plans can begin to be put in place.

If we monitor the flows in hives and fields in all commercial species that have pollination processes dependent on insects. The scope of the invention can allow the development of predictive models of the number of hives to be used before the pollination processes using the information of the specific historical environmental variables of each field to estimate the floral load of the crops, associated with the availability of projected pollinators from the measured flows, helping to quantify the correct pollinator load for each crop and area.

The analyzes and results of the analyses, monitoring and all the information obtained in the field, is stored in a dynamic database, which is securely integrated into the platform, allowing the predictive model to be fed as field analyzes are carried out. successively. The information and the accesses to the information are designed to be visible according to the corresponding type of user, for the corresponding period of time and, in addition, they allow the algorithm for the elaboration of the predictive model to be fed dynamically, and based on the analyzes of the crops analyzed in the field. Additional or alternative components:

Embodiments can include homemade waterers and various formulas to make for the bees, giving them sanctuaries to rest during their long day at work.

The ICBs correspond to listening devices also called "intelligent check branch" are complementary to the system, because the data obtained from the ICBs, then being able to integrate the listening data (ICB) to adjust the pollination rate and thus make the systems are complementary, but not dependent. Finally, the use of both technologies (ICF and ICB) allows enhancing the accuracy of recommendations, alerts, index calculations and effects on the response and control capacity of farmers, so as to increase crop yields.

Examples of the invention

As an embodiment of the invention, some of the variables analyzed can be described as follows:

Variable: Temperature.

The temperature is taken with an electric thermometer that allows us to estimate the number of degrees centigrade in a certain particular condition, be it an open apiary or a hive.

Temperature is measured in degrees Celsius and is represented as T°

Bees cannot control their body temperature, so they need to vibrate and move to maintain temperatures. Table one shows the different alert levels that are taken into account when working to measure the temperature of the apiary.

Table 1: Example of an ambient temperature alert illustration, with green being a non-alert state, yellow being an alert state, and red being an emergency state.

The activity level of a hive is directly related to the ambient temperature, a low temperature within the highest exposures to the sun can mean low activity.

If a hive is highly vulnerable to changes in environmental temperature, it is even more vulnerable to the inside temperature, which always tries to stay between 37 ° C. In this context, table 2 shows the vulnerability of the hive, in relation to the internal temperature.

Table 2: Example of a hive temperature alert illustration

Humidity variable:

Environmental humidity directly affects the level of work that bees can do, as well as reflecting the amount of water they may need or the possible generation of associated diseases (for example, fungi).

Table 3 shows how humidity affects different periods of the day, while Table 4 shows how humidity affects the interior of the hive.

Table 3: Example of ambient humidity measurements

Table 4: Example of Hive moisture measurements

Light variable:

Bees are dependent on light, their activity begins when the light exceeds a certain threshold of lux and they decrease their work when the value is below it, so on cloudy or particularly dark days it is normal for bees to have a flux less.

Bees are a complex organism, they respond to patterns and stimuli in hundreds of ways, as they interact with each other to make decisions, work as a team and act individually.

Flow of bees (foraging bees or honey collectors):

This number of foraging honey bees represents an estimated flux over time (honey bee foraging behavior over time). Where there are different outputs during the day having an afternoon pickup and a smaller one a little before noon, depending on the total population, the bees will respond as shown in Figure 5 (IN and OUT of the bees. Blue = output / orange = flow in).

As another example of the invention, the results of an earth model simulation for pollination evaluation using 100 technological hives in a 13-hectare field of Brassica napus ("Canola") are outlined.

EXAMPLE 1. Graphs and metrics resulting from a simulation of the model to estimate pollination.

Next, the results obtained from a simulation of The earth says model for the evaluation of pollination using 100 hives in a field of Brassica napus, cañóla, with a surface area of 13 hectares, are outlined.

The graphs presented in this document represent simulations of the form of analysis that was applied for the processing of data obtained from the fields. The first analysis presented in Figure 6 corresponds to the estimation of the average population of the apiaries called 1 (red color) and 2 (black color). Where the number of daily bees that have an average of 2 apiaries composed of 50 technological hives each is estimated. The analysis of environmental variables is also included in the pollination evaluation model. The variables considered are the maximum and minimum temperatures recorded during the pollination process, in addition to the solar radiation presented in Figure 7.

The number of bees working in the field was estimated based on the estimated average population in the apiaries, considering that the hives have a regular population of 15,000 to 20,000 individuals and that only 25% of them go out to work. Taking into account the use of 100 hives, it was estimated that the maximum range of foraging represented as a percentage of dynamic foraging, considering factors of flight dispersion and age of the hives, only reaches 58% (Figure 8), with respect to a maximum theoretical estimated ideal foraging with hives of 45,000 individuals where 50% of them go out to forage.

With information obtained from the bibliographic search, we built an estimated flowering model based on the environmental effect of the day, the duration of the flowers, the growth of the axial axes, the distribution of flowers in the axial axis, the opening of the apical axis, which are multiplied by the number of plants per square meter in the field, shown in Figure 9.

With the data from the dynamic flowering curve, together with the data on the percentage of foraging, a relationship is established (Figure 10), which allows estimating the efficiency of foraging or also called the extent of pollination. For this, the capacity of the plants to set, the number of flowers, the number of trips that the bees can make, the number of bees present in their time dynamics and climate changes must be considered. Constructing the data, 3 relevant variables are obtained: number of visits made to flowers, represented as a percentage of foraging abundance, estimated ideal number of visits and estimated number of flowers. Where the correlation between the first and second variables speak of the efficiency of foraging based on the development of the pollination process.

Once the relationship on the extent of pollination was obtained. If we establish this model in a linear comparison, where 100% represents a well-performed pollination and the plants open the maximum number of flowers per day and 10 visits of foraging bees are considered for the ideal flower set, we can obtain the relation of bees needed to pollinate the property according to the day of flowering shown in Figure 11. This graph exemplifies the need for foraging based on the flowers available per day to consider that pollination is being carried out efficiently. The foraging index represented as percentage of foraging achieved (blue line) must always be above the graph of the percentage of open flowers (black line) in this way it can be ensured that the maximum theoretical number of flowers calculated in one day is competently covered for a given flowering time (black line). The passage of the blue line over the black line represents that the abundance of bees in the field is greater than the number of flowers to pollinate in a certain time and the opposite effect, when the blue line is below the black line, indicating the lack of the abundance of bees in the field. Based on the above, figure 11 shows three zones indicated with letters, which represent; a) a bee population greater than the number of flowers in early pollination times; b) The number of flowers exceeds the estimated beekeeping force for those days; and c) During the completion of processes the beekeeping force manages to exceed the level of available flowers, a normal behavior for the end of flowering processes.

The examples mentioned above are not intended to limit the invention, but rather to show some of the ways in which the required parameters can be analyzed and/or described.

Additionally, the current invention comprises a system based on AI, in order to train and improve the current invention with respect to the monitoring system over time, by developing a database that includes: field data, hive data, environmental data, bee data and any suitable data. , in order to predict and provide information to the user, regarding guidelines and recommendations to improve the performance and status of the hive, including: bee flow, bee health, etc.

EXAMPLE 2. Example of the invention for calculating the pollination index

For the evaluation of the pollination process of a 10-hectare cherry field, the pollination index was calculated, obtained through historical field information provided by the client and the information obtained with the pollinator data capture system. The projected pollination index of the process is established in relation to the average yield of the last three seasons that was obtained per hectare and/or the yield estimate considered by the user. In this way, a sigmoidal curve is modeled that represents the behavior of the estimated production per day, graphed as a projected pollination index, in which ideal environmental conditions and standard development times are considered for the different stages of the pollination process.

The system models the curve with the input data, which is provided by table 5. This allows modeling the curve of the pollination index projected in a landed way to the field conditions, associating the projected yield for the field to an index of projected pollination, creating an ideal curve represented in dark red in figure 12. This is readjusted day by day based on critical variables, which are obtained with the pollinator monitoring system, allowing the pollination index achieved to be weighted.

The variables that determine the pollination index reached are the availability of pollinators, the climate function and the stage of development of the field; the relationships of these generate the blue bars in figure 12. The variables to obtain the daily pollination index achieved are subject to differential weighting depending on the stage of the process, and may influence not to reach, reach or exceed the pollination index. initially projected pollination for each particular day.

The pollination index can be viewed on any online or offline, web or mobile, digital or physical platform to provide a useful tool for informed and early decision-making for farmers who want to optimize their pollination process by increasing agricultural yields. Table 5: Required historical data of the field.

The projected pollination rate is modeled based on the number of hives used per hectare, assuming that they all behave in the same way and that they meet the standard for pollinating fruit trees that defines populations of 30,000 individuals.

Using table 6, the number of bees per hive to forage is calculated and then the number of bees to forage per hectare, assuming that all the hives are the same. This number of bees is associated with the average or projected yield for the process, adjusting the projected pollination rate.

Table 6: Standard number of bees foraging according to the population of the hive

In order to obtain the pollination index achieved, the state of development of the field must be known, which is related to the stage within the pollination process, which is defined by the number of days within the process. The pollination process is divided into 4 stages, where each stage is calculated per crop, based on reference data, customer information and environmental information of the crop and land, and where the duration of the stages is different for each crop, and is dependent on the climate (including variables such as temperature, humidity, precipitation, among others), which are exemplified in Figure 12 with the red vertical lines defining the terminus and beginning of each of them. These ranges of days are adjusted based on the species, variety and weather conditions, which can extend or shorten the stages depending on the condition. These conditions can be found for each plant species and variety. Tables 7 and 8 describe some examples of the number of days of development of species and varieties of interest, which were assembled based on what is described in the literature, own evaluation and information provided by the client, reference values of number of hives used for the processes which helps determine the ideal number of pollinators based on published data and the market. Depending on the stage of the pollination process, the effect of the weather function, which works on the flows of bees measured from the hive, has a greater or lesser impact depending on the stage, showing to cause more negative effects on performance when it is not used. reaches the pollination rates projected in stages 2 and 3.

Table 7:

Table 8:

The availability of pollinators for the process is adjusted daily by means of the data on bee flows in the hives, which allow knowing the population of the hives, relating the range of bee flows at a specific time and season of the year. exemplified in table 9. In the case of the cherry field, the table defined for the spring season given the flowering date is used.

Table 9: Standardized fluxes of incoming bees in 30 seconds from hives with known populations during the spring.

The fluxes of bees at the entrance of the hives are normalized by the weather function, which adjusts the value of the number of bees given by the quantification of the flux. This climate function is composed of the environmental temperature factor of the apiary and solar radiation. These factors are averaged based on table 10 and 11, which define the value by which the flow must be normalized, thus recalculating the value of the bee flow to obtain the real number of the hive population with which it is calculated. you will get the pollination rate achieved.

Table 10: Weighting of the influence of the radiation level on the flow of bees.

Table 11: Weighting of the influence of ambient temperature in the apiary on the flow of bees.

As a way of validating the calculated populations, the system consults the information on the internal temperature of the hive, which is also associated with a population value defined in table 12. This integrates the temperature in degrees Celsius related to the time and season of the year in a similar way as in table 9. In this case, the time of year corresponds to spring given the date of flowering of the crop. The use of this table focuses mainly on hives with populations of less than 30,000 individuals, since the thermoregulation capacity of vigorous hives does not present differences. The data in this table is adjusted based on the minimum and maximum ambient temperatures. The values in table 12 work between 5 - 29° Celsius. Table 12: Internal temperatures of hives with known populations in spring time during flight hours with minimum environmental temperatures of 5 ^o and maximum of 29 °.

By using the instruments and tables described above we can calculate the pollination rate achieved for the day, allowing easy comparison with the projected pollination rate. As shown in figure 12, when the pollination index reached does not exceed the project, the yield estimate defined in table 5 is negatively affected, adjusting 100% for the pollination index reached for the remaining days of the process. In this particular case, the environmental conditions for September 3 were unfavorable for the activity of the pollinators and the development of the field, so it was not possible to match the projected pollination rate. However, as it occurs in the last stage of development of the pollination process, a stage where the vast majority of the flowers have already been pollinated, the effect on the calculation of the final pollination index only decreases.

On the other hand, the example in figure 13 represents a situation of increased yield according to the calculated pollination index, since there was an increase in the flows of the hives together with a sustained ideal condition in the environment during stages 2 and 3 process, positively influencing the extent of pollination.

EXAMPLE 3. Example of the invention for the specific use of ICF and/or ICB to determine the relative abundance of pollinators in the field.

During the pollination process of a 2-hectare kiwi field, the ICF/ICB devices were used to determine the abundance of pollinators in certain critical points of the field to know the range, frequency and visit patterns of pollinators in the flowers of the crop. , which allowed associating population levels of the hives and performance of specific sectors.

The system works by detecting the buzz of the pollinators in a certain field site, which allows finding time windows where the pollinator activity is stronger, as can be seen in figure 14, the number of pollinators heard shows to have a greater activity in the afternoon (16 hours) compared to the morning (10 hours). This allows the producer to consider this information to reduce field work during the periods of maximum pollinator work in order to have as little influence as possible.

Another example of use for this information is to add solutions to encourage the work of bees during low activity hours to enhance the pollination process and improve yields per hectare. Another application of the pollinator data capture system allows the generation of hourly and daily heat maps of pollinator flows within the field (figure 15). In order to clearly visualize the sectors with the highest abundance of pollinators, which according to the colorimetric scale presented in table 13, the red colors indicate a lower average number of listenings and the blue colors indicate a greater number of listenings, which is associated with a greater abundance of pollinators, which is sustained during the days of the process, increases the probability of good pollination at said points, enhancing yields.

Table 7: Definition of listening ranges associated with colors

3-6 Yellow

10< Blue

The use of this technology makes it possible to understand the sectors that have a lower abundance of pollinators within the field, generating a useful tool for decision-making, indicating which sectors could be strengthened with the introduction of more pollinating force or through the use of substances that stimulate the visit in these sectors using ICF for the administration of said solutions.

EXAMPLE 4. Example of the invention for the specific use of ICD to determine the effect on the activity of bee fluxes after the application of insecticides

Within the pollination process of a 20-hectare canola field where 200 hives were monitored over a period of 30 days. The activity of the average inlet and outlet flows was analyzed for all the hives represented in Figure 15, where the pattern of activity of the hive flows throughout the process is shown. In figure 16 the input flows are indicated by the blue line and the output flows by the orange line, the entry of bees with pollen is indicated by the gray line.

The analysis of the flows includes environmental variables such as temperature, radiation and precipitation that influence and regulate the flows of the hives.

Through information obtained from field observations including information available in the literature. The application of a commercial insecticide described for the control of aphids during the pollination process was recorded.

The use of this type of substance in pollination processes is strongly contraindicated, due to the serious effects on pollinators in the field, however, the quantification of the effect on hives becomes complex to measure without an adequate instrument.

If we establish that the flow records for the day 12.28.20 represents the average value within the flows set to 100% and compare it with what happens after the application of the insecticide, we can determine that the activity of pollinators evaluated through the average flows decreases by 25.8%, which could be directly affecting the projected yield.

The system is capable of quantifying the effect of different solutions on the activity of pollinators, including insecticides, fungicides, herbicides, bactericides, fertilizers and all types of agricultural products for crops.

Other applications of the invention:

As a use of the invention, current technology can be used to establish a quantitative norm for the presence of pollinators, where the presence and abundance of insects can be considered, in this way to establish management and measure its efficiency in the different ecosystems. This implies an integration and categorization of different pollinators in different factors and days, where this metric can be included in the sustainability indices and also in the measurement of the carbon footprint. This can be compensated by promoting the use of native flora in the city and in the ecosystem corridors that may be generated.

As another use or application of the invention, it includes: (i) developing Tools to quantify pollination and thus generate a modernization in the strategies used to quantify the efficiency of pollination for both agricultural fields and native forests, which allows transforming the agriculture as we see, this and starting the integration of native forest responsibility; (ii) develop a system to monitor and understand changes in behaviors -physiological, behavioral- that insects have and how they relate to different flowers, thus understanding which are the ideal pollinators and beginning to establish specialized quantification scales for the pollinating efficiency of each species; and/or (iii) Promote the development of multi-flower patches and long-term flowers, hopefully of wild and/or native origin, and management of remaining local vegetation.

As another use and / or application of the invention, it includes: (i) Establish tools to manage, consolidate and promote the growth of the beekeeping chain; (ii) Generate technologies that are easy to implement, low cost and that generate direct and instant benefits to beekeepers, either by reducing work time, increasing monitoring capacity or avoiding losses; and / or (iii) Build tools that facilitate communication between beekeepers and producers who need pollination, allowing real-time monitoring and the generation of alerts associated with patterns found in the hives.

As another use and/or application of the invention, current technology can provide: i) Pollinator count,

i) Pollinator quantification tools, and iii) Practical, effective and dynamic tools for beekeeping development.

Another embodiment of the invention comprises a method for tracking and monitoring pollinators to improve pollination and crop production based on a predictive model, comprising at least three characteristic steps: a. Pre-pollination stage; b. Pollination Stage; and c. Post-Pollination Stage. where the "Pre-pollination" stage comprises the following steps: i. Information gathering: (can be both face-to-face and remote) that consists of the collection of data by the user/client, historical data of the land and the corresponding crops, specific data of the land such as geographical coordinates, characteristics, climatology, data collected from different devices in the field (hardware) and any other information relevant to the implementation of the system in the field, using a series of parameters including: initial parameters provided by the user, custom parameters specific to each field obtained from any of the devices and/or derived directly and indirectly from them through the platform, including parameters calculated from the data obtained, such as the pollination index calculated for each field, as well as any other relevant field information to feed the associated predictive model algorithm with the platform, and where the Information gathering also includes the following steps: Remote analysis (which includes the vegetation index and the calculation of the pollinator potential), Prospecting (which consists of a face-to-face visit to the field, which allows the search and location of connection points on the ground that will be part of the system, carry out the data transmission speed tests, carry out the geographical evaluation for the subsequent location of the fapiarios, complete the relevant data of the form and for the definition of performance), Meeting with the user/client, and Definition of performance. ii. Primary evaluation of hives, where said evaluation in the first instance includes the measurement and pre-selection of hives to be located and installed in the field, including: labeling or labeling of the state of the hives, thermal evaluation of hives in the apiary and estimation of the number of bees per apiary; and iii. Development of the pollination strategy, which includes the result of the analysis provided in the information gathering process, evaluation of the parameters of the predictive model incorporated in the platform, calculation of personalized and specific parameters for the field, including: potential pollinator, number of bees in the group of hives that will go to the field, number and location coordinates of total apiaries to be installed in the field, and any other relevant parameter required based on which the pollinator strategy to be used is generated; where the "Pollination" stage comprises the following steps: iv. Secondary evaluation of hives, which includes the installation, measurement and registry of hives in the field, where for this, the technologization of the field and hives is carried out, allowing the installation of the different devices (ICD, ICF, ICB, ASC) in the area previously defined in the field based on the criteria of the operator and user jointly, which will take samples of at least 60 seconds duration, successively by time intervals and during a daily period of time established by the operator, based on in the parameters that defined the pollinator strategy, together with the monitoring of hives parameters in comparison with previous measurements, including: temperature, number of bees, activity, flow of pollinators, and any other relevant parameter for the analysis; v. Activity monitoring, also includes: a daily comparison with expected performance, and recommendations in the process; where the daily comparison comprises the generation of an initial mobile/web report that contains a comparison with performance, based on the estimate of the number of bees according to temperature classification vs. the number of bees required for the predefined performance, and also comprises a activity monitoring that captures information regarding bee flow and pollen input, incorporated into a regularly updated web/mobile report; and where the recommendations during the process are based on the information provided and contained in the updated report, also including other parameters obtained such as: activity level, population level, performance comparison based on a graph of expected performance vs. effective performance depending on the total number of bees expected for the process, and where in addition, said recommendations are incorporated in the update of the report, indicating apiary activity and/or the need to increase the number of hives, if applicable; and I saw. Application of the Pollination Strategy; where the "Post pollination" stage comprises the following steps: vii. Validation of increased yields, further comprising: a quantification of the yield, an evaluation of the yield and an evaluation and quantification of the quality of the pollinated fruits; where the quantification of the performance includes the analysis of the user/client to determine the production obtained at the end of the process, information that is subsequently incorporated into the database and updated on the platform, based on which the effective yields are calculated and the user is notified; where the evaluation of the performance, includes the verification of the performance according to the data obtained in the validation, the analysis of the performance achieved in the process carried out and the delivery of a final report to the user, including: percentage of the actual performance achieved with respect to the projected performance, suggestions associated with the development of the process and suggested improvements for an eventual next pollination process based on the platform and the present technology; and where the evaluation and quantification of quality of pollinated fruits, includes a specific evaluation depending on each type of crop; and viii. Pollination Strategy Evaluation.

The method described above was used, for example, in the development of EXAMPLE 2; Other steps, stages and sub-stages of the process are shown in detail in FIGURE 17.

In a summarized embodiment of the process and/or the methodology related to the invention, there are the following steps:

1. Closing of sale

2. Enter and register on the platform

3. Complete pollination form

4. integration with platform service

5. Information gathering

6. Generation of a pollination strategy

7. Installation of hives in the field

8. Installation of devices in hives and field

9. Activity monitoring

10. Update display and alert platform

11. Uninstall devices

12. Quantification of performance by the client

13. Platform update for performance comparison expected versus achieved.

Other

Embodiments of the method and/or system may include every combination and permutation of the various components of the system and the various processes of the method, including any variants (eg, embodiments, variations, examples, specific examples, figures, etc.), where Parts of Embodiments The method and/or processes described herein may be performed asynchronously (eg, sequentially), at the same time (eg, in parallel), or in any other suitable order by and/or using a or more instances, elements, components of and/or other aspects of system 200 and/or other entities described in this document.

Any of the variations described in this document (for example, embodiments, variations, examples, specific examples, figures, etc.) and/or any part of the variations described in this document may be combined, added, excluded, used, made in series, perform additionally or alternatively, in parallel and/or otherwise applied.

Portions of embodiments of the method and/or system may be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium that stores computer-readable instructions. The instructions may be executed by computer-executable components that may be integrated with embodiments of system 200. The computer-readable medium may be stored on any suitable computer-readable medium, such as RAM, ROM, flash memory, EEPROM, optical devices (CD or DVD), hard drives, drives floppy disk or any suitable device, including data storage on an external storage system, such as a remote database. The computer-executable component may be a general or application-specific processor, but any suitable dedicated hardware/firmware combination device may alternatively or additionally execute the instructions.

As one skilled in the art will recognize from the foregoing detailed description and the figures and claims, modifications and changes to embodiments of the method, system and/or variants may be made without departing from the scope defined in the claims. The variants described herein are not intended to be restrictive. Certain features included in the drawings may be exaggerated in size and other features may be omitted for clarity and are not intended to be restrictive. Figures are not necessarily to scale. Absolute or relative dimensions or proportions may vary. The section titles in this document are used for organizational convenience and are not intended to be limiting. The description of any variant is not necessarily limited to any section of this specification.

Brief description of the Figures

FIGURE 1: Specific example of an Intelligent Check Door (ICD), including: a) a 3D model of the ICD and b) an example of how the ICD should be installed.

FIGURE 2: Represents a specific example of a 3D model of an "Intelligent Check Fountain" or ICF device, which includes: a) front view, and b) rear view of the ICF.

FIGURE 3: Represents the scheme of the device "Central Environmental Station" or "ASC" (for its acronym in English, Environmental Station Central), which includes: a) Model of the ASC on the outside, and b) Representation of the internal components of the ASC .

FIGURE 4. Example of connection scheme between devices for data extraction from the installation site. The arrows show the alternatives of the direction of the information flows. ASC corresponds to the environmental central station, ICB corresponds to the ICBranch (listening devices), ICF corresponds to ICFountain (bee counter device and dispenser of water, pheromones, etc.) and ICD corresponds to ICDoor device for capturing bee flows. in hives)

FIGURE 5: Example of a flow of bees (number) according to a time window.

FIGURE 6: Estimated average population in hives according to apiary. The population estimated for the hives by apiaries (Y axis) was monitored over time (X axis) from the installation of the apiaries. The estimated population represents the relationship between the number of bees that enter the hive and the pollen-foraging population by the adaptation time of the apiary. In the graph, the black dots correspond to apiary 1 and the red squares to apiary 2. FIGURE 7: Record of environmental variables, average temperature and radiation. A) Average temperature in degrees Celsius (Y axis) B) Average recorded solar radiation in megajoules per square meter (Mj/m ² ) (Y axis).

FIGURE 8: Relationship between percentage of dynamic foraging versus percentage of ideal abundance of foraging. The dynamic foraging abundance plotted represents the percentage of foragers estimated to pollinate the target field. The X axis represents the days of the process, while the Y axis shows the dynamic percentage of foraging. The green and red dashed lines indicate the maximum and minimum numbers, respectively, of estimated bees needed to pollinate the field.

FIGURE 9: Dynamic flowering curve. Estimation of the number of open flowers per day (Y axis) in the field during the flowering period (X axis). The total flowers were estimated under standard parameters on the number of daily flowers that a plant opens, the density of plants per square meter and the climatic factor.

FIGURE 10: Extent of pollination. The extent of pollination was estimated based on the number of open flowers per day, the number of visits required by the flower to be pollinated (right Y-axis) and the number of daily foraging bees represented as a percentage of foraging abundance ( left Y-axis). In green you can see the number of total flowers estimated in the field over time (X axis), while the striped area represents the estimated number of bees foraging, finally the yellow area indicates the estimated number of ideal visits that require the number of flowers estimated for each day.

FIGURE 11. Dynamic estimation of foraging. Relationship between the percentage of foraging achieved (left Y-axis) and the percentage of opening of daily flowers (right Y-axis) compared to the time of the flowering process (X-axis). The estimation of the percentage of foraging was made with the rate of foraging by applying the dispersion of the bees, which, when compared to the number of flowers, forms areas between the curves which represent the differences between the number of bees working and the number of available flowers, for the bees on certain days.

FIGURE 12. Modeling of the pollination index projected (dark red curve) and achieved (green bars) for the pollination process of a cherry field. The y-axis represents the value of the pollination index with respect to time on the x-axis. The red vertical lines delimit the change between the four stages of the process.

FIGURE 13. Example of calculating the pollination index under conditions of increased cherry yield.

FIGURE 14. Average hourly listening of pollinators within the kiwi field.

FIGURE 15. Heat maps associated with the abundance of pollinators heard by 6 devices at specific points within the kiwi field. Figure A represents the morning time at 10 am, B represents the time at noon 12 pm and C represents the afternoon time at 16 pm.

FIGURE 16. Flows of bees in hives per day during the canola pollination process. The blue line represents the inflow of bees per day, the orange line represents the outflow of bees per day, the gray line represents flow entry of pollen to the hives. The blue vertical line represents the application of insecticide against aphids in the field, which causes a decrease in the flows mentioned above.

FIGURE 17. Service flow chart and implementation of the pollinator tracking and monitoring system, including the stages from the preparation of the service to the end of the service, separated by each of the stages: A) Pre-pollination; B) Pollination, and C) Post-pollination.

Claims

CLAIMS A pollinator tracking and monitoring system to improve pollination and crop production based on a predictive model, CHARACTERIZED because it comprises: a. An input data form to feed the system; b. A set of devices for tracking and monitoring pollinators, to obtain field information in real time; c. A link platform for the different elements and devices of the system, including an interface for the user and remote operation of the devices in the field; and d. A data base; The system of claim 1, CHARACTERIZED because the input data form contains information that includes, but is not limited to: geographic location to implement the system, historical weather information of the geographic location, definition of the field polygon to implement the system, field coordinates, crop characteristics, yield per hectare of the crop in at least the last three seasons, data collected from different devices in the field and any other information relevant to the implementation of the system in the field, using a series of parameters including: initial parameters provided by the user, general parameters available in global and/or public databases, specific custom parameters for each field obtained from any of the devices and/or derived directly and indirectly from them through the platform, including calculated parameters From the data obtained, as vegetation indices and pollination index calculated for each field. The system of claim 1, CHARACTERIZED in that the set of devices comprises at least: an access device or intelligent control door (ICD) to capture the flow of bees for each hive, an intelligent control source (ICF) that allows monitoring the number of bees that stop to drink water in the middle of their transit to/from the hive within a certain period of time, and a central environmental station (ASC), which allows the reception of the information provided from one or more devices of the system, and/or transmit the data to another electronic device, including electronic devices external to the system, for the collection of field information, real-time monitoring, remote adjustments and elaboration of a predictive model that allows improving the application of pollinating agents in the field, in order to improve the production of crops in said field. The system of claim 3, CHARACTERIZED because the ICD is an adaptation to the existing hive, consisting of a gantry that is placed in the hive (where the bees come and go) whose purpose is to monitor (for example, count) the number of bees entering and leaving the hive, and their condition (with or without pollen, state, size, etc.), recording the transit of bees in/out of the hive; where the ICD can also be designed so that the information is taken in a more organized way (the rails allow entrance and exit of the bees), where the porch or walkway is the key to the success of the monitoring system. The system of claim 3, CHARACTERIZED because the ICF comprises a housing with a functional design, similar to a bee drinker in crop fields, facilitating the ability to monitor the number of bees that stop to drink water in the middle of its transit from/to the hive, during a determined period of time and based on the required parameters of initial analysis. The system of claim 3, CHARACTERIZED because the ASC allows the reception of the information provided from one or more field devices of the system (ICD, ICF), as well as in turn transmit the data to any device of the system or outside of it , including: cell phone, computer, server, other antenna and/or any other device suitable for capturing field data, processing information and/or displaying results that can be analyzed by experts and/or provide additional specific recommendations to the user. The system of claim 3, CHARACTERIZED in that the set of devices further comprises a listening device or ICB comprising a functional housing that includes an appendix for the location of the microphone, which allows by detecting the buzz of pollinators in a certain site of the field, determine the abundance of pollinators in certain critical points of the field to know the scope, frequency and visit patterns of pollinators in the flowers of the crop, also allowing to associate population levels of the hives and performance of specific sectors of the field. The system of claim 7, CHARACTERIZED in that the ICBq can also contain a container to contain the liquid, and replace the function of an ICF in the field. The system of claims 3 to 8, CHARACTERIZED in that the system devices are custom designed and where each system device comprises at least one of the following components: a set of sensors, a computer module, a microprocessor, a microprocessor program him, a data transmission system, and a power source; where the set of sensors can include different types of sensors, including: optical sensor, thermal sensor, sound sensor, mechanical sensor, chemical sensor, electrical sensor, physical sensor, any other suitable sensor and combinations of these, including a thermal imager; wherein the microprocessor may include any suitable portable microprocessor; wherein the computer module may include a single board computer (SBC); wherein the data transmission allows data to be received and/or transmitted between, to and from devices in the system to a remote central station and/or a suitable device within a certain range, including devices outside the system; and where the power source may be: battery, AC, DC, on grid, off grid, and any other suitable power source available, including but not limited to: solar power, power wind, thermal energy, etc. and that

30 allows transmission to be maintained for the necessary period of time in which follow-up and monitoring is required. The system of claim 1, CHARACTERIZED because the platform is the means of coordination of the different components and allows the global operation of the system, developed for the management of pollination, facilitating the integration of technology for the control and management of hives, optimizing time and resources, and also allowing to articulate communication between users and system operators. The system of claim 19, CHARACTERIZED because the platform allows the development of predictive models of the number of hives to be used before the pollination processes using the information of the specific historical environmental variables of each field to estimate the floral load of the crops, associated with the availability of pollinators projected from the measured flows, helping to quantify the correct load of pollinators for each crop and zone. The system of claim 1, CHARACTERIZED in that the database is dynamic and is integrated with the platform, allowing the algorithm developed to generate the predictive model to be fed as field analyzes are carried out successively. A method of tracking and monitoring pollinators to improve pollination and crop production based on a predictive model, CHARACTERIZED because it comprises at least three characteristic stages: a. Pre-pollination stage; b. Pollination Stage; and c. Post-Pollination Stage. The method of claim 13, CHARACTERIZED in that the "Prepollination" stage comprises the following steps: i. Gathering information; ii. Primary evaluation of hives; and iii. Development Pollination Strategy. The method of claim 14, CHARACTERIZED because the collection of information consists of the collection of data by the user/client, historical data of the land and the corresponding crops, specific data of the land such as geographical coordinates, characteristics, climatology, and any other relevant field information to feed the algorithm of the predictive model associated with the platform, and where the collection of information further comprises: ia). remote analysis, ib). Prospecting, ic). Meeting with the user/client, and id) Performance definition, where in addition said survey allows calculating the pollination index. The method of claim 15, CHARACTERIZED in that the remote analysis comprises the calculation of vegetation indices and pollination index. The method of claim 15, CHARACTERIZED because the prospecting consists of a face-to-face visit to the field, which allows the search and location on the ground of the connection points that will be part of the system, carry out the data transmission speed tests, carry out the geographical evaluation for the subsequent location of the apiaries, completing the relevant data of the form and for the definition of performance. The method of claim 14, CHARACTERIZED because the primary evaluation of hives comprises the measurement and preselection of hives to be located and installed in the field, including: labeling or labeling of the state of the hives, thermal evaluation of hives in apiary and estimation of the number of bees per apiary. The method of claim 14, CHARACTERIZED because the development of the pollination strategy includes the result of the analysis provided in the information gathering process, evaluation of parameters of the predictive model incorporated in the platform, calculation of personalized and specific parameters for the field, including: the pollination index, number of bees in the group of hives that will go to the field, number and location coordinates of total apiaries to be installed in the field, and any other relevant parameter required based on which the pollinator strategy to use. The method of claim 13, CHARACTERIZED in that the "Pollination" stage comprises the following steps: i. Secondary evaluation of hives; ii. activity monitoring; and iii. Application of the Pollination Strategy. The method of claim 20, CHARACTERIZED because the secondary evaluation of the hives, includes the installation, measurement and registry of hives in the field, where for this, the technologization of the field and hives is carried out, allowing the installation of the different devices (ICD, ICF, ICB, ASC) in the area previously defined in the field based on the criteria of the operator and user jointly, which will take samples of at least 10 seconds duration, iteratively and successively by time intervals and during a daily period of time established by the operator, based on the parameters that defined the pollinator strategy, together with the monitoring of hive parameters in comparison with previous measurements, including: temperature, number of bees, activity, flow of pollinators, and any other parameter relevant to the analysis. The method of claim 20, CHARACTERIZED in that the activity monitoring further comprises at least: a ii-a) daily comparison with expected performance, and ii-b) recommendations in the process; where the daily comparison comprises the generation of an initial mobile/web report that contains a comparison with performance, based on the estimate of the number of bees according to temperature classification vs. the number of bees required for the predefined performance, and also comprises a activity monitoring that captures information regarding bee flow and pollen input, incorporated into a regularly updated web/mobile report; and where the recommendations during the process are based on the information provided and contained in the updated report, also including other parameters obtained such as: activity level, population level, performance comparison based on a graph of expected performance vs. effective performance based on the total number of bees expected for the process, and where in addition, said recommendations are incorporated in the update of the report, indicating apiary activity and/or the need to increase the number of hives, if applicable. The method of claim 13, CHARACTERIZED in that the "Post pollination" stage comprises the following steps: i. Yield increase validation; and ii. Pollination Strategy Evaluation. The method of claim 23, CHARACTERIZED in that the performance increase validation further comprises at least: an ia) performance quantification, an ib) performance evaluation; and ic) evaluation and quantification of quality of pollinated fruits; where the quantification of the performance includes the analysis of the user/client to determine the production obtained at the end of the process, information that is subsequently incorporated into the database and updated on the platform, based on which the effective yields are calculated and the user is notified; where the evaluation of the pollination strategy includes the verification of the performance according to the data obtained in the validation, the analysis of the performance achieved in the process carried out and the delivery of a final report to the user, including: percentage of the actual performance achieved regarding the projected performance, suggestions associated with the development of the process and suggested improvements for an eventual next pollination process based on the platform and the present technology; and where the evaluation and quantification of the quality of the pollinated fruits comprises a specific evaluation depending on each type of crop. Use of the system, devices and methodologies of the preceding claims, CHARACTERIZED because the application of the invention allows: i) to provide an effective personalized count of pollinators, i) the generation of tools for the quantification of pollinators, and i¡¡) the generation of practical and dynamic tools for beekeeping development. Use of the system, devices and methodologies of the previous claims, CHARACTERIZED because the application of the invention also allows: (i) Establish tools to manage, consolidate and promote the growth of the beekeeping chain; (ii) Generate technologies that are easy to implement, low cost and that generate direct and instant benefits to beekeepers, either by reducing work time, increasing monitoring capacity or avoiding losses; (iii) Build tools that facilitate communication between beekeepers and producers who need pollination, allowing real-time monitoring and the generation of alerts associated with patterns

33 found in hives; and (iv) improve the production of a crop in the field, based on the algorithm developed for the platform's predictive model and fed with the database with historical information in combination with updated real-time information for the user. Use of the system, devices and methodologies of the preceding claims, CHARACTERIZED because the invention can be used to establish a quantitative norm for the presence of pollinators, where the presence and abundance of insects can be considered, in this way managing and measuring their efficiency in the different ecosystems, where the implementation of the standard implies an integration and categorization of different pollinators in different factors and days, where this metric can be included in the sustainability indices and also in the measurement of the carbon footprint, and where this It can be compensated by promoting the use of native flora in the city and in the ecosystem corridors that may be generated. Use of the system, devices and methodologies of the previous claims, CHARACTERIZED because the invention can be used to: (i) Develop tools to quantify pollination and thus generate a modernization in the strategies used to quantify the efficiency of pollination for both agricultural fields as well as for native forests, which allows transforming agriculture as we see it, and initiating the integration of native forest responsibility; (ii) develop a system to monitor and understand changes in behaviors -physiological, behavioral- that insects have and how they relate to different flowers, which allows identifying the ideal pollinators and establishing specialized quantification scales for efficiency pollinator of each species; and (iii) Promote the development of multi-flower patches and long-term flowers, of wild and/or native origin, and the management of remaining local vegetation.