WO-2026095786-A1 - PREVENTIVE, PREDICTIVE AND PRESCRIPTIVE ANALYSIS PROCESS FOR PRECISION AGRICULTURE

Abstract

The purpose of the invention is to optimise the analysis of plant, water and soil samples sent by producers to laboratories, using artificial intelligence to enhance agricultural management, using a wider dataset than in traditional agricultural management. The invention analyses physical, chemical and biological variables of the studies and compares them with the weather history and production objectives. Potential cultivation problems are identified, the most critical problems are prioritised, and action plans balancing all the variables involved in growing the plant are proposed, thereby reducing the use of fertilisers and agrochemicals. The data are captured every 15 minutes on the piece of land, enabling continuous adjustments in relation to the soil, irrigation and the plant. The invention generates customised plans for nutrition, fertility, irrigation, pests and diseases, providing specific recommendations to maximise the yield, quality, profitability and sustainability of agricultural production.

Inventors

• TREJO LUNA HERREJÓN, Salvador Miguel
• GONZÁLEZ JAIME, Juan Pablo
• MARTÍNEZ TREJO LUNA, José Raúl
• ELVIRA BARRAGÁN, Jesús Francisco

Assignees

• SISTEMAS AVANZADOS INTEGRALES S.C.

Dates

Publication Date

20260507

Application Date

20241107

Priority Date

20241030

Claims (18)

1. 1. A preventive, predictive and prescriptive analysis process for generating static preventive and dynamic regenerative productive capacity indices for precision agriculture, implemented on a web server platform (104) configured with an Agronomic Intelligence Engine (100), characterized in that it comprises: A first stage implemented in an Agronomic Intelligence Engine (100) to obtain the Static Productive Capacity index, through the steps of Obtain (10) the customer record by means of a mobile device (102) of the user (108) connected to the network (110); Upload (20) the pdf files to the web server platform (104) for receiving laboratory analysis results, of the physical, chemical and biological characteristics of the following elements of the property, soil, water and plant; Capture (30) the results of the analyses using an artificial intelligence text reader included in the Agronomic Intelligence Engine (100) that extracts the results to the database (116) in which they are grouped and stored; Process (40) by means of the Agronomic Intelligence Engine (100) the correlation of the information between the analyses loaded on the platform, adding to said analysis according to the geographical coordinate of the property, the climatic data of the area by means of a measuring station (106) placed on the property that has sensors of temperature, humidity, wind speed and solar radiation; Issue (50) a rating on the feasibility of developing an agricultural project, using the Agronomic Intelligence Engine (100) which has factors of weighting with a reliability index that depends on the number of factors entered; Report (60) deficiencies and/or constraints of soil, water, biology and/or climatic conditions using the Agronomic Intelligence Engine (100); Correlate (70) using the Agronomic Intelligence Engine (100) the information on the type of crop and the production objective; Issue (80) through the Agronomic Intelligence Engine (100) a document with automatic management recommendations for improvements, amendments or adjustments to the physical and chemical characteristics of soil and water elements in order to increase the project's rating; Establish (90) through the Agronomic Intelligence Engine (100) a general static agronomic management plan of nutrition needs, fertility, irrigation and pest and disease control stages according to the processed information, to achieve the goals according to the required production objective; A second stage implemented in the Agronomic Intelligence Engine (100) to obtain the Regenerative Dynamic Productive Capacity index, in addition to incorporating the results of the first stage; reports in real time the weather conditions and the availability in the soil of water and nutrients for the development and growth of the crop against its requirements; Determine using the Agronomic Intelligence Engine (100) based on the information obtained the appropriate management for the phenological stage for the production and quality of the harvest; Determine using the Agronomic Intelligence Engine (100) based on meteorological and soil information, such as temperature, humidity and heat units, the best management of pests and diseases; Develop, through the Agronomic Intelligence Engine (100), a dynamic system for feeding data captured permanently on the property through the measuring station (106) installed on the property, updating the indices and elements that the Static Preventive Productive Capacity index considers for the constant evaluation of the project; and, Issue and make amendments and/or corrections using the Agronomic Intelligence Engine of the soil, irrigation and/or plant.
2. 2. The predictive and preventive analysis process, in accordance with claim 1, further characterized in that: in the Establish step (90) of the first stage, in extreme cases, the recommendations may even include changing the crop; if so, the system generates recommendations for alternative crop changes, taking into account the conditions where the agricultural production unit is located, with an efficiency model that considers: agronomic, commercial and operational feasibility, estimated transition cost, and expected returns per crop.
3. 3. The predictive and prescriptive analysis process according to claim 1, further characterized in that: the Regenerative Static Productive Capacity index is determined by comparing the requirements of the different crops, varieties, hybrids and/or biotypes in general with the data corresponding to the place where it is located.
4. 4. The predictive and prescriptive analysis process according to claim 3, further characterized in that: the crop requirements are classified into 5 levels, very high, high, optimal, low and very low.
5. 5. The predictive and prescriptive analysis process according to claim 1, further characterized in that: a weighting factor is estimated for each of the indices and with it the weighted productive capacity index is determined.
6. 6. The predictive and prescriptive analysis process according to claim 1, further characterized in that: the user record includes the client's name, geographical location of the property, number of hectares of the property, type of crop or production objective.
7. 7. The predictive and prescriptive analysis process according to claim 6, further characterized in that: the geographical location of the property can also be obtained remotely by placing a PIN through an API to obtain the corresponding coordinates.
8. 8. The predictive and prescriptive analysis process according to claim 1, further characterized in that: the step of capturing the results of the analyses using an artificial intelligence text reader allows the identification of tokens in a pdf document or image to read the analyses of soil, water, plant, microbiology from any laboratory.
9. 9. The predictive and prescriptive analysis process according to claim 8, further characterized in that: through the extraction of this data, a data sheet file is constructed which serves as a guide to interpret the tokens extracted from the document and relate them to the elements of interest for analysis.
10. 10. The predictive and prescriptive analysis process according to claim 1, further characterized in that: the climate data can also be obtained by means of an external data server (112) from a global climate provider that provides the same values mentioned for the provided date-time, given the geographical coordinates of the property.
11. 11. The predictive and prescriptive analysis process according to claim 1, further characterized in that: The ratings are determined in accordance with the best agronomic practices that serve to establish rating ranges for each element measured in the different categories to be evaluated and considering the particular crop being analyzed.
12. 12. The predictive and prescriptive analysis process according to claim 1, further characterized in that: by constantly monitoring the climate and soil and correlating it with the physical, chemical and biological analyses of soil and water of the Regenerative Dynamic Productive Capacity index through the Agronomic Intelligence Engine, a soil nutrition and conditioning model is determined that is sufficient in milligrams per liter (mg/1) to achieve a balance between soil regeneration and plant needs.
13. 13. The predictive and prescriptive analysis process according to claim 1, further characterized in that: by constantly monitoring the climate and correlating it with plant analyses through the Agronomic Intelligence Engine (100), indices are determined that influence the plant for its phenological stage changes, thus being able to issue recommendations on the stress state in the plant and its possible solutions to take the plant in the best way through its phenological stages.
14. 14. The predictive and prescriptive analysis process according to claim 1, further characterized in that: through constant monitoring of the climate, soil and correlation with the physical analyses of the soil and water of the Regenerative Dynamic Productive Capacity index, through the Agronomic Intelligence Engine (100), the behavior of the laminar flow of water and its availability to the plant is determined, allowing adjustments to be made in the irrigation plans so that a balance is developed that allows the renewal of the soil and It also provides sufficient moisture for the plant without degrading it.
15. 15. The predictive and prescriptive analysis process according to claim 1, further characterized in that: by constantly monitoring the climate on site, correlating its variables and the irrigation algorithms, the appearance of pests and diseases in their different states and/or thresholds through the Agronomic Intelligence Engine (100), the best time to control their appearance is determined by changing from a reactive application to a preventive one.
16. 16. A system for the predictive and prescriptive analysis process for generating static preventive and dynamic regenerative productive capacity indices, characterized in that it comprises: A user's mobile device (102) (108) configured to provide property information to a web server platform (104) via a network (110); A measuring station (106) having one or more fixed remote sensors communicatively coupled via the network (110) to send data to the platform (104); An external data server (112) that includes historical meteorological data connected to the platform (104) via the network (110); A network (110) having a communication layer (114) configured to perform data input/output interface functions to the mobile device (102), the platform (104), and the measuring station (106); and, A web server platform (104) comprising a database (116) for storing information received by the mobile device (102), the measuring station (106) and the external data server (112) via the network (110) and an Agronomic Intelligence Engine (100) configured to carry out the process according to claims 1 to 14.
17. 17. The system according to claim 16, further characterized in that: the sensors of the measuring station (106) can be ambient and soil sensors that are communicatively coupled, transmitting every 15 minutes, either directly or indirectly through the network (110).
18. 18. The system according to claim 17, further characterized in that: the environmental sensors can be for temperature, CO2, humidity, wind speed, rainfall, and solar radiation, and the soil sensors can be for electrical conductivity, temperature, humidity, pH, and nutrients (phosphorus, nitrogen, potassium).

Description

PREVENTIVE, PREDICTIVE AND PRESCRIPTIVE ANALYSIS PROCESS FOR PRECISION AGRICULTURE TECHNICAL FIELD OF THE INVENTION The present invention relates to the technical field of precision agriculture, computer systems programmed with operations useful in agricultural management, computer systems that are programmed or configured to generate what the possible problems that the crop will have during the productive cycle and prioritize them to indicate what the action plan is to address them, and specifically to a predictive and prescriptive analysis process implementing the use of artificial intelligence to extract and analyze information of Atmospheric weather (historical, present and forecast), correlated with the physical, chemical and biological variables from laboratory studies related to soil, water, plant and microbiology to detect physiological and metabolic problems in the crop, as well as the real-time analysis of the interaction of representative variables of Plant, Water, Soil and Atmospheric Weather (historical, present and forecast) with the purpose of understanding, in a growing way, the functioning of nature in the interaction of these variables. Prescriptive analysis is used to generate personalized recommendations for agricultural inputs to achieve the performance, quality, profitability and sustainability objectives of the agricultural production unit. BACKGROUND OF THE INVENTION Precision agriculture has emerged as an advanced practice in the agricultural sector, based on the incorporation of technologies such as remote sensing, data analysis and geographic information systems (GIS) to improve decision making. This discipline uses detailed data on soil conditions, climate, humidity, topography, and other environmental factors to manage crops with greater precision. With the development of new predictive and prescriptive analysis techniques, farmers can now anticipate events and make optimal decisions about managing their crops and land, reducing costs, increasing productivity, and conserving natural resources. Predictive analytics in precision agriculture refers to the use of large volumes of data, often in real time, to predict future crop scenarios. Through machine learning algorithms and advanced statistical techniques, it is possible to accurately forecast critical factors such as crop yield, potential pests, soil nutrient and moisture levels, and even weather conditions. This predictive capability not only helps optimize crop planning but also allows for minimizing climate risks and improving the management of water and fertilizer resources. Prescriptive analytics, on the other hand, goes a step further, not only predicting outcomes but also recommending specific actions based on previous analyses. Prescriptive systems in precision agriculture analyze large volumes of historical and current data to suggest optimized interventions, such as the best time to sow, the optimal amount of fertilizers or pesticides to apply, and the most suitable irrigation techniques. This approach facilitates more informed decision-making, tailored to the specific conditions of each agricultural plot. Advances in sensor technologies, drones, and satellites have contributed enormously to the development of predictive analytics and Prescriptive technology in precision agriculture. These devices collect detailed, georeferenced information that enables high-resolution analysis, providing a holistic view of crop and soil health. Combined with the use of artificial intelligence (AI), farmers can efficiently manage large areas of farmland, optimizing resource use and improving the sustainability of the agricultural sector. Predictive and prescriptive analytics are key components of modern precision agriculture, enabling data-driven management and providing farmers with a competitive advantage. These tools not only maximize crop yields but also help address challenges related to climate change, resource scarcity, and global food demand. Currently, there are various technologies being applied to improve production processes in agriculture. All these technologies have a common objective: "To help the farmer improve food production in the crop he manages." Mexican patent application number MX/a/2023/000724, entitled "PREDICTION OF HORTICULTURAL YIELD FROM FIELD LOCATION USING MULTIBAND AERIAL IMAGES," describes its main objective as helping farmers predict crop yield through the processing of multiband aerial images that feed a machine learning system. This system compares current fruit growth from the aerial image with a historical database of pre-labeled images to predict the potential yield at harvest. Based on the information contained in the aerial images, and by analyzing the colorimetry that highlights the color range, the presence of certain elements can be inferred, such as... Nitrogen levels are measured, and based on this, the farmer can be advised whether or not to apply fertilizer. The technology described i