AI-based drone system for spraying pesticides on arable land

Abstract

An autonomous agricultural drone system for the precise spraying of pesticides, consisting of: an advanced image recognition camera integrated with an artificial intelligence (AI) module specifically trained on pest and plant health datasets, with the camera configured to detect specific pests, identify pest hotspots, and differentiate between healthy and stressed plants; a real-time decision processor that is operationally connected to the image recognition camera, the processor being configured to: Data received from the image recognition camera is interpreted to assess pest density and plant health. adjusts the pesticide output and spraying patterns of the drone based on real-time field conditions, determines the severity of pest infestation in order to avoid excessive use of pesticides and to apply pesticides precisely and only where they are needed; a multitude of intelligent spray nozzles equipped with sensors and actuators, each nozzle configured to dynamically adjust its spray rate based on pest density and plant species, as determined by the real-time decision processor, thereby minimizing chemical use and environmental impact; characterized by the fact that The drone system includes a multi-liquid injection nozzle system that allows the drone to carry and selectively apply multiple solutions, including pesticides and nutrients, in a single flight, with the system configured to switch between solutions in response to real-time data from the image recognition camera and decision processor.

Description

TECHNICAL FIELD OF THE INVENTION

[0001] This disclosure relates to an AI-powered drone system, more specifically, an autonomous AI-powered drone for precise pesticide application in agriculture, improving efficiency and environmental sustainability. BACKGROUND OF THE INVENTION

[0002] The background description contains information that may be useful for understanding the present invention. This is not an admission that the information provided herein is prior art or relevant to the invention claimed herein, or that any publication expressly or impliedly referenced is prior art.

[0003] Traditional methods of pesticide application in agriculture, such as manual or large-scale aerial spraying, often result in excessive chemical use, resulting in environmental pollution and increased costs. These approaches lack precision, frequently leading to pesticide drift, off-target exposure, and increased pest resistance.

[0004] Precision agriculture has emerged as a solution to these problems, focusing on targeted, data-driven approaches to improving plant health while reducing chemical impacts. However, current technologies, including conventional drones, do not offer fully autonomous systems with integrated AI for real-time pest detection and adaptive spraying capabilities.

[0005] Conventional approaches also require significant manual labor and time, especially on large farms where field coverage is often slow and labor-intensive. Furthermore, blanket spraying methods miss certain pest areas, unnecessarily treat healthy plants, and lead to further inefficiencies in pesticide use.

[0006] Existing agricultural drones generally rely on fixed-rate spraying, which applies pesticides evenly across entire fields. This one-size-fits-all approach fails to account for fluctuations in pest density, leading to wasteful chemical use and potential damage to the surrounding ecosystem.

[0007] The present invention presents an AI-powered autonomous drone system that combines image recognition, multispectral sensors, and real-time path planning to detect pest hotspots and enable precise, variable pesticide application. This system is intended to minimize chemical use, reduce costs, and support sustainable agricultural practices. The integration of AI-driven image recognition and sensor fusion into the invention enables the drone to adapt its spray patterns based on real-time data on crops and pests. This targeted approach not only conserves resources but also reduces environmental impact by applying pesticides precisely where they are needed. This increases crop yield while promoting environmentally friendly agricultural practices. OBJECTIVES OF THE INVENTION

[0008] The primary objective of the invention is to create an autonomous, AI-controlled drone system that significantly improves the precision of pesticide application in agriculture. Using real-time image recognition and sensor data, the system detects and targets specific areas of pest infestation, reducing unnecessary chemical use and improving pest control effectiveness. This targeted approach helps farmers address pest problems promptly and accurately, ultimately leading to healthier plants and higher yield quality.

[0009] Another important goal of the invention is to reduce the environmental impact of conventional pesticide spraying methods. Conventional methods often lead to overapplication, chemical drift, and contamination of non-target areas, which can harm beneficial organisms and ecosystems. The proposed system uses AI to dynamically adjust pesticide production based on pest density and crop health, significantly reducing chemical use and limiting the ecological footprint. This supports sustainable agricultural practices by aligning with environmentally friendly agricultural goals and minimizing runoff and soil degradation.

[0010] The invention also aims to improve operational efficiency in large-scale agriculture. By incorporating autonomous flight path planning and adaptive spraying techniques, the system reduces the need for manual labor and repetitive field coverage, saving time and reducing labor costs. The drone's ability to navigate autonomously and cover large areas with optimal precision makes it a highly efficient solution for modern agriculture, especially in expansive fields where pest control is often labor-intensive and time-consuming.

[0011] The invention provides a robust framework for data collection and analysis to support predictive pest management. Because the drone collects data on pest activity, plant health, and environmental conditions, it can inform future spraying strategies and pest control measures. This data-driven approach not only helps refine pesticide application techniques over time but also enables farmers to anticipate pest infestations and take proactive measures, thereby improving agricultural productivity and sustainability in the long term. SUMMARY OF THE INVENTION

[0012] This disclosure relates to an AI-assisted drone system, more specifically an autonomous AI-powered drone for the precise application of pesticides in agriculture, improving efficiency and environmental sustainability.

[0013] The present invention consists of several innovative components that enhance the functionality and precision of an AI-powered pesticide spraying drone. These parts work together to optimize pest management, minimize environmental impact, and improve operational efficiency.

[0014] The present invention consists of an advanced image recognition camera with integrated AI, designed to identify specific pests and crop health problems in real time. This module uses a high-resolution camera with machine learning algorithms trained on pest and crop data, allowing the drone to detect the presence of pests and indicators of crop stress. By accurately identifying pest-affected areas, this module ensures that pesticides are applied only where needed, thus conserving resources and reducing unwanted impacts.

[0015] A real-time decision processor in this module allows the drone to dynamically adjust its spray patterns based on the detected pest density and crop health. This intelligent spraying approach optimizes pesticide use by applying only the required amount to affected areas and minimizing chemical exposure to surrounding healthy plants. This AI-powered system ultimately improves crop protection while supporting sustainable agricultural practices.

[0016] The present invention consists of an adaptive spray nozzle system equipped with intelligent nozzles capable of spraying with variable spray rates. These nozzles adjust pesticide delivery depending on pest density and plant species, enabling precise, targeted pesticide application. With built-in sensors and actuators, the intelligent nozzles conserve pesticides by spraying only the affected areas, thus minimizing chemical waste and potential ecological damage.

[0017] The present invention consists of an innovative multi-fluid injection function that allows the drone to carry multiple solutions, such as different pesticides or nutrients, and switch between them during flight. This capability enables efficient multi-target applications, addressing different plant needs or pest species in a single pass. By combining adaptive spraying with multi-fluid injection, this system improves resource efficiency, reduces labor, and improves overall plant health.

[0018] The present invention consists of a compact environmental monitoring system, including a micro weather station and plant health sensors integrated into the drone to conduct real-time environmental assessments. The micro weather station measures local wind speed, temperature, and humidity, allowing the drone to adjust its spray patterns in response to real-time field conditions. This feature prevents pesticide drift and ensures more precise application, reducing the risk of chemical contamination in non-targeted areas.

[0019] The present invention consists of multispectral and hyperspectral sensors that monitor plant stress indicators such as chlorophyll content and moisture content. By analyzing these factors, the drone can adjust spray rates according to the plant's health and stress level, ensuring the correct amount of pesticide or nutrient is delivered. This environmentally adaptive system supports precision agriculture by minimizing waste and promoting sustainable crop management.

[0020] The present invention consists of an autonomous precision flight and path optimization module that uses AI to calculate efficient routes based on pest density, crop layout, and field conditions. This module leverages deep learning algorithms to generate optimized flight paths, ensuring complete field coverage with minimal overlap and minimal energy consumption. During operation, the system can dynamically adjust routes to avoid obstacles and adapt to real-time environmental changes.

[0021] The present invention consists of a precise altitude control system that utilizes LiDAR and ultrasonic sensors to maintain a constant low-altitude hover. This close-flight mode enables accurate pesticide application with minimal drift, making the drone highly effective for targeted pest control. Together, these flight optimization features enable precise navigation over large fields, thus reducing operating costs and labor requirements in large-scale agriculture.

[0022] The present invention consists of a self-calibrating pesticide mixing system that enables the mixing and dilution of pesticide solutions in flight. An in-flight mixing chamber within the system combines different pesticides or dilutes them as needed, allowing the drone to adjust concentrations in real time based on pest density or crop requirements. This feature ensures precise application rates and minimizes the risk of overdose or runoff.

[0023] The present invention consists of an automatic dilution mechanism that changes the pesticide concentration depending on the crop species, pest type, and environmental conditions. By dynamically calibrating the mixture, the drone can deliver optimal pesticide amounts to specific areas of the field, reducing chemical waste and ensuring effective pest control. This adaptive mixing capability supports both cost-effective and environmentally friendly pest control practices.

[0024] The present invention consists of a field data analysis and predictive AI module designed to collect and analyze data on pest activity, plant health, and environmental factors over time. This module stores field data and uses predictive analytics to anticipate pest outbreaks and optimize future pesticide applications, creating a data-driven approach to pest control. By tracking pest patterns and plant responses, the system enables more accurate and timely intervention.

[0025] The module is connected to the cloud, allowing historical data to be uploaded and processed for refined spraying strategies. Predictive algorithms analyze past and current data and help farmers implement proactive measures based on identified trends. This approach not only optimizes current pest control but also helps prevent future infestations, thus improving agricultural productivity and sustainability in the long term.

[0026] The present invention consists of an advanced flight and power management system that improves the drone's endurance and operating time. This system includes swappable battery packs and optional solar-assisted charging, allowing the drone to cover large fields in a single pass. The system also features an intelligent power allocation function that prioritizes power distribution to critical components based on real-time requirements, thus maximizing flight efficiency.

[0027] The present invention consists of energy optimization algorithms that adjust power output depending on factors such as altitude, speed, and payload. This energy management feature ensures that the drone can perform extended spraying missions without frequent recharging, making it a cost-effective and labor-saving solution for large-scale operations. This extended flight capability supports efficient pest control over expansive crop areas. BRIEF DESCRIPTION OF THE DRAWING

[0028] The accompanying drawings are provided to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate an exemplary block diagram of the present disclosure method and, together with the description, serve to explain the principles of the present disclosure. Fig. shows an example of the drone for precise spraying of pesticides. DETAILED DESCRIPTION

[0029] The following is a detailed description of the embodiments. The embodiments are detailed enough to convey the disclosure. However, the amount of detail provided is not intended to limit the expected variations of the embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives that fall within the spirit and scope of the present disclosure, as defined in the appendix.

[0030] The present invention consists of an advanced image recognition camera integrated with an AI module specifically trained on pest and plant health datasets. This high-resolution camera can identify specific pests and locate pest hotspots, enabling precise targeting of affected areas. By distinguishing between healthy and stressed plants, the system ensures that pesticides are applied only where necessary, reducing both chemical waste and environmental impact.

[0031] Advanced image recognition camera that can detect early signs of plant stress, such as discoloration or structural changes, which may indicate the presence of pests or nutrient deficiencies. This early detection enables proactive intervention and helps growers address problems before they spread. This better protects plants, minimizes potential yield losses, and improves plant health.

[0032] In addition, the camera processes images in real time as the drone moves across the field, enabling continuous assessment and response to pest activity. This dynamic capability allows the drone to adjust its spray patterns in flight and precisely address immediate problems. By focusing on specific problem areas, the invention conserves pesticide resources while achieving effective pest control.

[0033] The present invention consists of a real-time decision processor that uses machine learning algorithms to interpret data from the image recognition camera. This processor assesses pest density and plant health in real time and adjusts the drone's spray patterns and pesticide delivery accordingly. By making instant decisions based on field conditions, the processor ensures that pesticides are applied efficiently and precisely, only where they are most needed. Through data analysis, the decision processor determines the severity of the infestation and adjusts application rates to avoid overuse of pesticides. This selective approach not only conserves resources but also promotes environmentally friendly agricultural practices by reducing chemical load in the field.Real-time adjustments prevent unnecessary applications and make pest control both effective and sustainable.

[0034] The processor operates continuously, feeding information back to the drone's flight control and spraying system, enabling adaptive responses when the drone encounters new areas. This feedback loop allows the drone to prioritize high-risk areas, minimize spraying in low-risk zones, and avoid healthy areas entirely. By adapting applications to the actual pest population, the invention ensures maximum crop protection with minimal environmental impact.

[0035] The present invention consists of intelligent spray nozzles equipped with sensors and actuators that dynamically adjust spray rates based on pest density and plant species. These nozzles are designed to respond to real-time data from the drone's AI system, ensuring precise application where it's most needed. By automatically adjusting spray rates, the system saves pesticides, applies only the required amount to specific locations, and reduces overall chemical consumption.

[0036] Each nozzle can individually modulate its spray output, allowing for fine-tuned control of pesticide distribution under varying crop conditions. For example, areas with high pest density receive more concentrated applications, while healthy areas receive minimal or no pesticides. This targeted approach minimizes waste, improves crop health, and reduces the risk of chemical runoff into the surrounding soil and water.

[0037] In addition, the smart nozzles contribute to more efficient pest control by reducing the need for repeated applications. The precision of delivery allows for fewer, more effective treatments, ultimately saving time, resources, and labor. This smart, sensor-based system complies with environmentally friendly practices, supporting sustainable agriculture, and simultaneously optimizing pest control.

[0038] The present invention also features a multi-fluid injection nozzle, allowing the drone to transport and switch between different pesticide or nutrient solutions during a single flight. This innovative feature enables multi-target application, meaning the drone can target different pest species and crop needs without having to return for refills. The ability to transport multiple solutions simultaneously significantly increases the versatility and efficiency of the drone.

[0039] This nozzle system seamlessly switches between solutions based on real-time data from the field, applying the appropriate substance depending on crop requirements and pest species. For example, the system can switch from a broad-spectrum pesticide to a nutrient spray while moving across the field, delivering a tailored treatment for different crop zones. This adaptability not only improves pest control effectiveness but also optimizes nutrient delivery for improved crop health. By reducing the need for multiple flights or manual intervention, the multi-fluid injection system saves resources and time, providing a more streamlined approach to crop management. This capability also reduces operating costs and enables more comprehensive field coverage in a single application, making it ideal for large-scale farming.

[0040] The ability of the intelligent nozzles to adjust spray rates in real time according to pest density and crop type maximizes the effectiveness of pesticide application and ensures that chemicals are applied only where absolutely necessary. At the same time, the multi-fluid injection system enables different treatments in different areas in a single pass, significantly reducing downtime and operational costs.

[0041] The present invention consists of a micro weather station, a compact sensor array installed on the drone to measure local environmental factors such as wind speed, temperature, and humidity. This micro station allows the drone to adjust its spray patterns in real time, reducing pesticide drift and ensuring precise delivery directly to the target areas. By responding to these conditions, the system minimizes the likelihood of chemicals spreading into unintended areas, making application safer and more efficient.

[0042] The environmental data collected in real time by this station is crucial for adapting spray coverage to immediate field conditions. For example, in strong winds, the drone can stop or adjust its spray angle to avoid drift, while in calm conditions it can apply pesticides more freely. This adaptability prevents waste and maximizes the impact of each application, ensuring that pesticides are used effectively.

[0045] Furthermore, the micro weather station allows the drone to maintain an optimal altitude based on atmospheric data, improving spray accuracy. This on-the-fly adjustment ensures that the pesticide reaches the plants in the most effective way, improving the overall operational efficiency of the drone.This proactive environmental response promotes sustainable agriculture by reducing the risk of overuse of pesticides and minimizing environmental impact.

[0043] The present invention consists of real-time soil and plant health sensors integrated with multispectral and hyperspectral imaging capabilities. These sensors assess critical plant stress indicators such as chlorophyll levels and moisture content, providing valuable insights into plant health. By continuously monitoring these factors, the drone can adapt pesticide or nutrient application to the specific needs of each crop section, thus optimizing resource utilization.

[0044] The hyperspectral sensors can detect subtle differences in plant color and texture, signaling early signs of stress that may be invisible to the human eye. This early detection capability helps growers intervene before problems escalate and promotes healthier, more resilient plants. By applying nutrients or pesticides according to actual plant health, the system saves chemicals and reduces the need for sweeping, untargeted treatments.

[0048] Furthermore, the drone's soil moisture measurements help optimize pesticide concentrations based on hydration levels, ensuring that application rates are neither too high nor too low for effective pest control.This approach supports balanced plant nutrition and pest control, ultimately leading to higher crop yields and improved soil health while conserving water and pesticide resources.

[0045] The present invention consists of a synergistic combination of a micro weather station and real-time soil and plant health sensors, providing a comprehensive environmental monitoring system. This dual configuration enables the drone to make data-driven adjustments in response to weather and plant conditions, thus maximizing the effectiveness of pesticide and nutrient application. By considering both atmospheric and plant-specific factors, the system optimizes resource use while minimizing environmental impact.

[0046] The environmental adaptability provided by the micro-weather station ensures that pesticides are applied under optimal conditions, reducing drift and off-target exposure. At the same time, soil and plant health sensors further refine application by identifying the specific needs of each growing area and enabling tailored treatments. This precision not only saves chemicals but also supports sustainable agricultural practices by preventing excessive pesticide buildup in the soil and surrounding ecosystem.

[0047] Together, these monitoring components transform the drone into an intelligent, responsive tool for modern agriculture. The system enables farmers to achieve high crop yields and resilience through data-driven decision-making, while reducing costs and promoting environmentally friendly practices. By improving both application accuracy and resource efficiency, the invention contributes significantly to the advancement of precision agriculture.

[0048] The present invention consists of an automatic dilution and adjustment system that dynamically adjusts the concentration of pesticides during spraying to the specific needs of plants and pests. This system continuously adjusts the dilution level to meet preprogrammed requirements for different plants, ensuring that the optimal pesticide concentration is applied at all times. By adjusting the concentration in real time, this function helps reduce chemical use and prevent overdose, thus protecting both plants and the surrounding environment.

[0049] The system works by receiving inputs from the drone's pest detection and plant health sensors, which transmit real-time data to determine the appropriate pesticide concentration. For example, if a low-density pest area is identified, the system further dilutes the pesticide, while higher concentrations are applied in areas of intense pest activity. This precise application saves pesticides, reduces the risk of crop damage from overapplication, and reduces the risk of chemical buildup in the soil.

[0050] Furthermore, the drone's automatic dilution function allows it to seamlessly adapt to different crop types and growth stages during a single flight. This adaptability enables effective pest control under diverse field conditions without the need for manual intervention. The efficiency of the automatic dilution and adjustment system results in cost savings, improved plant health, and minimized environmental impact, making it a valuable tool for sustainable and efficient agriculture.

[0051] The present invention consists of a powerful combination of an integrated data analytics hub and a cloud-connected predictive AI module, providing a comprehensive data-driven platform for efficient crop management. Together, these components enable farmers to make informed, proactive decisions by integrating predictive insights and historical data into real-time field management practices. In this way, the system improves both current and future crop care and reduces reliance on reactive measures.

[0052] The Analytics Hub's data collection provides a foundation of detailed field information, while predictive AI builds on this foundation to predict pest activity and other potential problems. This integrated approach enables precise intervention before problems escalate, optimizing the application of pesticides and nutrients based on precise field requirements. With a focus on proactive management, the system ensures that resources are used efficiently and environmental impact is minimized. The predictive power of the combined Analytics Hub and AI module transforms conventional pest management into a preventative, efficient process, enabling farmers to optimize resources while protecting crop health and promoting environmentally friendly agriculture.

[0053] The present invention consists of interchangeable power modules designed to extend the flight time of the drone, which is particularly useful for large-scale agricultural applications. These modules include quick-swappable battery packs that allow operators to easily swap out empty batteries for new ones, minimizing downtime and enabling continuous operation. This feature is essential for covering extensive fields in a single pass and making the large-scale application of pesticides or nutrients more efficient and less labor-intensive.

[0058] In addition to interchangeable battery packs, the system includes solar-assisted charging that provides the drone with additional power during flight. This solar assist utilizes sunlight to extend flight time, conserve battery power, and reduce the dependence on frequent battery changes.This feature is particularly beneficial during peak daytime hours, allowing longer operating times in sunny environments.

[0054] Together, the drone's swappable battery packs and solar-assisted charging enable longer operation with fewer interruptions. This capability not only improves operational efficiency but also reduces overall energy costs associated with pesticide and nutrient application. By enabling longer flights, the invention supports efficient field coverage and is consistent with sustainable energy use practices in agriculture.

[0055] The present invention also includes an intelligent power allocation system that intelligently distributes power to the drone's essential components based on real-time operating requirements. By prioritizing power supply to critical systems such as the propulsion and pesticide sprayer, the intelligent allocation system saves energy and extends the drone's operating time in the air. This selective distribution is particularly useful in energy-intensive applications where maximizing flight endurance is critical.

[0056] The intelligent allocation feature dynamically adjusts power consumption, allowing the drone to respond to changing field conditions and mission requirements. For example, in areas where less spraying is required, the system can reduce power flow to the sprayer and prioritize flight stability to conserve energy and optimize battery life. This responsiveness ensures that power is used efficiently, extending battery life and improving the drone's overall performance.

[0057] Furthermore, the system's adaptability minimizes energy waste and reduces the frequency of battery replacement or recharging. By ensuring that each component receives the optimal power it needs, the intelligent allocation system supports uninterrupted operation over extended periods. This efficiency in energy management allows the drone to cover larger areas with minimal downtime, making it ideal for modern, resource-intensive agriculture.

[0058] In summary, the AI-based drone pesticide spraying system is revolutionizing precision agriculture by integrating advanced technologies that optimize resource utilization and improve pest control efficiency. Through its adaptive components, including the environmental monitoring system, the pest detection and decision module, and variable-rate spray nozzles, the drone ensures that pesticide applications are both targeted and environmentally responsible. The ability to adjust spray patterns and fluid types in real time, combined with intelligent energy management, enables comprehensive coverage of large fields while minimizing chemical waste and reducing operating costs.

[0059] This invention ultimately provides farmers with a proactive, data-driven solution for effective crop management. By reducing reliance on manual intervention and improving the accuracy of pesticide application, the drone system promotes sustainable agricultural practices and supports higher crop yields. Its optimized workflow and advanced energy management make it a viable tool for large-scale agricultural applications, meeting modern demands for environmentally friendly and efficient farming. This AI-based drone technology thus represents a significant advance in agricultural productivity and promotes a healthier, more sustainable approach to pest control and crop care. Key features of the AI-based drone system for spraying pesticides on agricultural land • AI-powered pest detection and decision-making model: This module incorporates an advanced image recognition camera and a real-time decision processor that identifies pest hotspots and evaluates plant health indicators. The AI ​​adjusts spray patterns and pesticide application rates based on live analytics, enabling targeted and efficient application. • Adaptive variable-rate spray nozzle system: Equipped with intelligent nozzles and multi-fluid injection capabilities, the system dynamically adjusts spray rates based on pest density and crop requirements. This feature saves resources and allows the drone to switch between multiple pesticides or nutrients within a single flight. • Environmental monitoring system: Includes a micro-weather station to monitor local wind, temperature, and humidity for optimal spray accuracy. Real-time soil and plant health sensors detect plant stress indicators, enabling precise adjustments to pesticide or nutrient application. • Field data analysis and predictive AI module: An integrated data hub collects and stores environmental, pest, and plant health data. Cloud-connected AI uses historical field data to predict future pest occurrences and optimizes spraying schedules, improving long-term crop management. • Advanced flight and power management system: With swappable battery modules and solar-assisted charging, the drone achieves longer operating times. Intelligent power allocation directs energy to critical systems, extending flight time and maximizing field coverage. BENEFITS OF AN AI-BASED DRONE SYSTEM FOR SPRAYING PESTICIDES ON ARABLE FIELDS

[0060] The present invention offers the following advantages.

[0061] Precision and efficiency: The AI-powered pest detection and decision module enables precise identification and targeted spraying of pest-affected areas. This reduces the amount of pesticide used, minimizes environmental impact, and protects non-targeted plants, while improving pest control effectiveness.

[0062] Resource conservation: The adaptive variable-rate spray nozzle system dynamically adjusts spray rates based on real-time pest density and crop needs. By saving pesticides and nutrients, it reduces chemical consumption and overall operating costs, thus supporting sustainable resource management.

[0063] Extended Flight Time and Coverage: The advanced flight and power management system with interchangeable power modules and intelligent power allocation enables longer operating times. This allows the drone to cover larger fields without the need for frequent recharging, making it ideal for large-scale agricultural operations.

[0064] Proactive Crop Management: The integrated data analytics hub and predictive AI module provide valuable insights into pest activity and crop health trends. Leveraging historical and real-time data, growers can anticipate pest outbreaks and optimize spraying schedules, reducing the need for reactive interventions and supporting long-term crop resilience.

[0065] Reduced environmental impact: The environmental monitoring system ensures that spraying occurs under optimal weather conditions, minimizing pesticide drift and protecting surrounding areas. This feature, combined with the efficient use of chemicals, promotes environmentally friendly pest control practices.

[0066] Labor and time savings: By automating pest detection, spray adjustments, and data analysis, drones significantly reduce the need for manual labor and field monitoring. This streamlining allows farmers to focus on other important tasks, saving time and improving operations.

Claims

[1] An autonomous agricultural drone system for precise spraying of pesticides, consisting of: an advanced image recognition camera integrated with an artificial intelligence (AI) module specifically trained on pest and plant health datasets, with the camera configured to detect specific pests, identify pest hotspots, and differentiate between healthy and stressed plants; a real-time decision processor operatively connected to the image recognition camera, the processor being configured to: Data received from the image recognition camera is interpreted to assess pest density and plant health, adjusts the drone’s pesticide output and spray patterns based on real-time field conditions, determines the severity of pest infestations to avoid overuse of pesticides and apply pesticides precisely and only where needed; a multitude of intelligent spray nozzles equipped with sensors and actuators, with each nozzle configured to dynamically adjust its spray rate based on pest density and plant species, as determined by the real-time decision processor, thereby minimizing chemical use and environmental impact; characterized by , that the drone system includes a multi-fluid injector system that enables the drone to carry and selectively apply multiple solutions, including pesticides and nutrients, in a single flight, with the system configured to switch between the solutions in response to real-time data from the image recognition camera and decision processor. [2] The precision pesticide spraying system of claim 1, wherein the advanced image recognition camera detects early signs of plant stress, such as discoloration or structural changes that indicate the presence of pests or nutrient deficiencies, enabling proactive intervention before the stress spreads to other crop areas. [3] A system for precise pesticide spraying according to claim 1, wherein the intelligent spray nozzles are configured to adjust spray rates for each nozzle individually, enabling fine-tuned pesticide distribution, with increased concentration in high-density pest areas and reduced or no application in healthy zones. [4] The precision pesticide spraying system of claim 1, wherein the multi-fluid injection nozzle system enables application to multiple targets in different crop areas without the need for reloading, thereby enabling the drone to target different pest species and crop needs in a single flight. [5] The precision pesticide spraying system of claim 1, wherein the micro weather station adjusts the flight altitude and spray angle of the drone in response to environmental conditions such as high wind speed or low humidity to maximize pesticide effectiveness and reduce unintentional chemical spread. [6] A system for precise spraying of pesticides according to claim 1, wherein the soil and plant health sensors provide real-time data on plant stress indicators such as chlorophyll and moisture levels, thus enabling differentiated treatment of the plant sections based on specific nutrient or pesticide needs. [7] A system for precision spraying pesticides according to claim 1, wherein the automatic dilution and adjustment system dynamically changes the pesticide concentration in response to the pest density and plant species encountered during flight, thereby minimizing chemical usage and reducing the risk of plant damage from overapplication. [8] The precision pesticide spraying system of claim 1, wherein the integrated data analytics hub and cloud-connected predictive AI module enable remote access to field data, support predictive insights for proactive crop management, and optimize current and future pesticide and nutrient applications. [9] A system for precision spraying pesticides according to claim 1, wherein the interchangeable power modules enable uninterrupted operation through rapid battery replacement and additional solar power, thus enabling comprehensive field coverage in a single deployment. [10] The precision pesticide spraying system of claim 1, wherein the intelligent power allocation system dynamically prioritizes power supply to critical components such as the engine and sprayer based on real-time operating requirements, thereby extending flight time and improving overall system efficiency.

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