ARTIFICIAL INTELLIGENCE DRIVEN MULTI DRONE AGRICULTURAL MONITORING SYSTEM AND METHOD THEREOF

Abstract

Disclosed herein is an artificial intelligence driven multi drone agricultural monitoring system and method thereof (100) that comprises a plurality of drones (102) equipped with an image capturing unit (104), a processing unit (106) connected to the image capturing unit (104), housing an artificial intelligence module (108), an adaptive coordination unit (112) linked to each drone, a predictive analytics module (110) in communication with the artificial intelligence module (108), a variable-rate spraying unit (114) connected to the plurality of drones (102), a communication network (116) operatively connecting the processing unit (106) to a cross-platform user interface (120), wherein the communication network (116) enables real-time transmission of monitoring data and analysis results from the plurality of drones (102) to the cross-platform user interface (120) for immediate user access and decision-making, a cross-platform user interface (120) integrated in a user device (118), a data storage unit (124) for archiving historical crop.

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of agricultural technology, more specifically, relates to artificial intelligence driven multi drone agricultural monitoring system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] This invention offers significant advantages in improving the efficiency and productivity of agricultural practices. First, it allows farmers to monitor their fields continuously without needing to be physically present. This remote monitoring capability not only saves time but also provides a constant stream of data, enabling farmers to address issues promptly. As a result, it reduces the need for labour-intensive field checks, allowing farmers to focus on other essential tasks and ultimately lowering operational costs.
[0003] Second, the system provides actionable insights that support informed decision-making. By analysing data on various crop conditions, it guides farmers on when and how to intervene, optimizing the timing of tasks like irrigation, pest control, and fertilization. This level of guidance helps farmers use resources more efficiently, which can lead to healthier crops, higher yields, and lower environmental impact due to minimized use of chemicals and water.
[0004] Third, this invention enhances agricultural planning through predictive analysis, allowing farmers to prepare for future growing seasons. By identifying trends and patterns in crop health and soil conditions, the system enables long-term planning and risk mitigation. This helps farmers maximize their output by aligning their farming practices with data-driven predictions, improving resilience to changing environmental conditions and supporting sustainable farming methods.
[0005] In contrast, existing agricultural monitoring systems face several limitations. One major drawback is their reliance on manual data collection, which can be time-consuming and prone to human error. Without automated or real-time monitoring, farmers often face delays in detecting issues, leading to slower response times and potentially higher crop losses, especially when dealing with fast-spreading pests or diseases.
[0006] Additionally, traditional systems often provide limited insights, focusing only on specific aspects of crop health without offering a comprehensive view. This narrow focus means farmers may miss crucial information needed to make balanced decisions. As a result, these systems are less effective at optimizing resource usage, leading to either under-treatment or over-treatment, both of which can negatively affect crop yield and quality.
[0007] Many existing solutions lack scalability and adaptability, making them impractical for large-scale or diverse farming operations. These systems are often designed with a one-size-fits-all approach, which fails to account for the unique needs of different crops, climates, or farm sizes. Consequently, they may not perform well under varying conditions, limiting their usefulness and efficiency in modern agriculture.
[0008] Thus, in light of the above-stated discussion, there exists a need for a artificial intelligence driven multi drone agricultural monitoring system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an artificial intelligence driven multi drone agricultural monitoring system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to enhance crop health by enabling early detection of diseases and stress factors through advanced monitoring techniques to empower farmers to take timely actions ensuring optimal growth conditions for their crops.
[0012] Another objective of the present disclosure is to improve resource efficiency in agricultural practices by utilizing precise application methods to minimize the use of fertilizers and pesticides thereby reducing waste and environmental impact.
[0013] Another objective of the present disclosure is to provide real-time data analysis that offers actionable insights for farmers to support informed decision-making allowing for better management of farm operations and resource allocation.
[0014] Another objective of the present disclosure is to enhance farm security by monitoring and deterring threats from stray animals to protect crops from potential damage caused by wildlife thereby safeguarding farmers' investments.
[0015] Another objective of the present disclosure is to facilitate adaptive management strategies by continuously analysing environmental conditions to enable the system to respond effectively to changing circumstances promoting long-term sustainability in farming practices.
[0016] Another objective of the present disclosure is to promote financial viability for farmers by optimizing crop yields and quality to increase profitability and support the economic stability of agricultural enterprises.
[0017] Another objective of the present disclosure is to support the integration of cutting-edge technology in agriculture fostering innovation and modernization within the industry to encourage the adoption of advanced practices and tools among farmers.
[0018] Another objective of the present disclosure is to provide a comprehensive monitoring solution that combines various data sources for a holistic understanding of farm health enhancing the ability to manage complex agricultural systems effectively.
[0019] Another objective of the present disclosure is to reduce the operational burden on farmers by automating monitoring and management processes to support labour efficiency allowing farmers to focus on strategic planning and other critical aspects of their operations.
[0020] Yet another objective of the present disclosure is to create a user-friendly interface that facilitates accessibility for farmers of all technical backgrounds ensuring that advanced agricultural technologies are approachable and usable for a broad range of users.
[0021] In light of the above, in one aspect of the present disclosure, an artificial intelligence driven multi drone agricultural monitoring system is disclosed herein. The system comprises a plurality of drones equipped with an image capturing unit for collecting real-time images of agricultural fields. The system includes. The system also includes a processing unit connected to the image capturing unit, housing an artificial intelligence module including a convolutional neural network model, configured to analyses the captured images and differentiate animal behaviour patterns and crop health based on historical and environmental data. The system also includes an adaptive coordination unit linked to each drone, enabling dynamic adjustments of drone paths based on environmental factors such as soil moisture and weather conditions. The system also includes a predictive analytics module in communication with the artificial intelligence module, designed to anticipate potential crop diseases by analysing seasonal and soil condition patterns. The system also includes a variable-rate spraying unit connected to the drones, applying precise chemical amounts based on plant health analysis to minimize chemical waste and environmental impact. The system also includes a communication network operatively connecting the processing unit to a cross-platform user interface, wherein the communication network enables real-time transmission of monitoring data and analysis results from the drones to the user interface for immediate user access and decision-making. The system also includes a cross-platform user interface integrated in a user device for accessing real-time monitoring, financial analysis, and long-term soil health insights. The system also includes a data storage unit for archiving historical crop and soil health data.
[0022] In one embodiment, the artificial intelligence module within the processing unit includes an animal behaviour analysis model, enabling it to predict and prevent specific crop threats by recognizing patterns in animal interactions with crops.
[0023] In one embodiment, the adaptive coordination unit further adjusts drone routes based on real-time changes in humidity, wind speed, and temperature, enhancing efficiency and resource management.
[0024] In one embodiment, the adaptive coordination unit synchronizes with external weather databases, providing weather-based alerts that influence drone route adjustments and intervention strategies.
[0025] In one embodiment, the predictive analytics module identifies disease trends by analysing historical data on crop health, soil fertility, and seasonal conditions, allowing proactive disease prevention.
[0026] In one embodiment, the processing unit is configured to integrate data from additional sensors located on the drones, including thermal and infrared sensors.
[0027] In one embodiment, the cross-platform user interface provides a financial analysis module, offering farmers real-time cost and revenue projections based on crop health and suggested management actions.
[0028] In one embodiment, the adaptive coordination unit uses machine learning algorithms to generate customized flight paths for each drone based on the crop type, growth stage, and field topography.
[0029] In one embodiment, the data storage unit is connected to a cloud-based database that enables multi-seasonal data comparison, allowing the system to analyse long-term trends in soil health and crop productivity, which facilitates adaptive planning for upcoming growing cycles.
[0030] In light of the above, in one aspect of the present disclosure, an artificial intelligence driven multi drone agricultural monitoring method is disclosed herein. The method comprises capturing real-time images of agricultural fields through a plurality of drones equipped with an image capturing unit. The method includes transmitting the captured images to a processing unit, where an artificial intelligence module including a convolutional neural network model analyses the images to identify distinct animal behaviour patterns and assess crop health based on historical and environmental data, providing insights into potential crop threats. The method also includes coordinating the drone paths dynamically through an adaptive coordination unit based on environmental factors, adjusting the drones' activities according to real-time soil moisture and weather conditions. The method also includes predicting and preventing crop diseases by analysing seasonal and soil condition patterns through a predictive analytics module in communication with the artificial intelligence module. The method also includes applying precise amounts of chemicals through a variable-rate spraying unit connected to the drones, based on the health analysis of the plants to minimize environmental impact and reduce chemical waste. The method also includes transferring the monitoring data and analysis results in real-time to a cross-platform user interface via a communication network, enabling farmers to make immediate decisions and take prompt actions. The method also includes archiving data on soil and crop health trends in a data storage unit to support long-term crop planning.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an artificial intelligence driven multi drone agricultural monitoring system and method thereof, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart of artificial intelligence driven multi drone agricultural monitoring system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of artificial intelligence driven multi drone agricultural monitoring method, in accordance with an exemplary embodiment of the present disclosure.
[0039] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0040] The artificial intelligence driven multi drone agricultural monitoring system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0041] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0042] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0043] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0044] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0045] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0046] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a perspective view of a performance measuring system and method thereof 100, in accordance with an exemplary embodiment of the present disclosure.
[0047] The system 100 may include a plurality of drones 102 equipped with an image capturing unit 104 for collecting real-time images of agricultural fields. The system 100 may also include a processing unit 106 connected to the image capturing unit 104, housing an artificial intelligence module 108 including a convolutional neural network model, configured to analyse the captured images and differentiate animal behaviour patterns and crop health based on historical and environmental data. The system 100 may also include an adaptive coordination unit 112 linked to each drone, enabling dynamic adjustments of drone paths based on environmental factors such as soil moisture and weather conditions. The system 100 may also include a predictive analytics module 110 in communication with the artificial intelligence module 108, designed to anticipate potential crop diseases by analysing seasonal and soil condition patterns. The system 100 may also include a variable-rate spraying unit 114 connected to the plurality of drones 102, applying precise chemical amounts based on plant health analysis to minimize chemical waste and environmental impact. The system 100 may also include a communication network 116 operatively connecting the processing unit 106 to a cross-platform user interface 120, wherein the communication network 116 enables real-time transmission of monitoring data and analysis results from the plurality of drones 102 to the cross-platform user interface 120 for immediate user access and decision-making. The system 100 may also include a cross-platform user interface 120 integrated in a user device 118 for accessing real-time monitoring, financial analysis, and long-term soil health insights. The system 100 may also include a data storage unit 124 for archiving historical crop and soil health data.
[0048] The artificial intelligence module 108 within the processing unit 106 includes an animal behaviour analysis model, enabling it to predict and prevent specific crop threats by recognizing patterns in animal interactions with crops.
[0049] The adaptive coordination unit 112 further adjusts drone routes based on real-time changes in humidity, wind speed, and temperature, enhancing efficiency and resource management.
[0050] The adaptive coordination unit 112 synchronizes with external weather databases, providing weather-based alerts that influence drone route adjustments and intervention strategies.
[0051] The predictive analytics module 110 identifies disease trends by analysing historical data on crop health, soil fertility, and seasonal conditions, allowing proactive disease prevention.
[0052] The processing unit 106 is configured to integrate data from additional sensors located on the plurality of drones 102, including thermal and infrared sensors.
[0053] The cross-platform user interface 120 provides a financial analysis module 122, offering farmers real-time cost and revenue projections based on crop health and suggested management actions.
[0054] The adaptive coordination unit 112 uses machine learning algorithms to generate customized flight paths for each drone based on the crop type, growth stage, and field topography.
[0055] The data storage unit 124 is connected to a cloud-based database that enables multi-seasonal data comparison, allowing the system to analyse long-term trends in soil health and crop productivity, which facilitates adaptive planning for upcoming growing cycles.
[0056] The method 100 may include capturing real-time images of agricultural fields through a plurality of drones 102 equipped with an image capturing unit 104. The method 100 may also include transmitting the captured images to a processing unit 106, where an artificial intelligence module 108 including a convolutional neural network model analyses the images to identify distinct animal behaviour patterns and assess crop health based on historical and environmental data, providing insights into potential crop threats. The method 100 may also include coordinating the plurality of drones 102 paths dynamically through an adaptive coordination unit 112 based on environmental factors, adjusting the plurality of drones 102 activities according to real-time soil moisture and weather conditions. The method 100 may also include predicting and preventing crop diseases by analysing seasonal and soil condition patterns through a predictive analytics module 110 in communication with the artificial intelligence module 108. The method 100 may also include applying precise amounts of chemicals through a variable-rate spraying unit 114 connected to the plurality of drones 102, based on the health analysis of the plants to minimize environmental impact and reduce chemical waste. The method 100 may also include transferring the monitoring data and analysis results in real-time to a cross-platform user interface 120 via a communication network 116, enabling farmers to make immediate decisions and take prompt actions. The method 100 may also include archiving data on soil and crop health trends in a data storage unit 124 to support long-term crop planning.
[0057] The plurality of drones 102 comprises a fleet of specialized aerial vehicles designed for comprehensive agricultural monitoring and data collection across extensive fields. Each individual in the plurality of drones 102 operates autonomously or in coordination, following predefined or dynamically adjusted paths to maximize field coverage. High-resolution, multi-spectral imaging equipment mounted on each individual in the plurality of drones 102 allows for precise data capture across various wavelengths, including infrared, which enhances the identification of crop health indicators such as chlorophyll content, moisture levels, and potential pest infestations. The plurality of drones 102 is equipped with real-time image processing capabilities, transmitting crucial visual data to the processing unit 106 for further analysis. By collaborating within the plurality of drones 102, the system ensures minimal data overlap and missed areas, enhancing the accuracy of agricultural monitoring. Each individual in the plurality of drones 102 also contributes to a collective dataset that supports long-term analysis and trend identification, providing farm managers with a detailed view of their crop health. The collective efforts of the plurality of drones 102 lay the foundation for informed decision-making across farm management activities.
[0058] The plurality of drones 102 also incorporates robust GPS and geolocation technology, allowing each individual in the plurality of drones 102 to autonomously navigate complex terrains with precision. The plurality of drones 102 is equipped with collision-avoidance systems to ensure safe operation, even in densely populated agricultural environments or challenging weather conditions. Additionally, the plurality of drones 102 includes weather-resilient designs that enable consistent performance in varying climates, from high temperatures to heavy rain. Each individual in the plurality of drones 102 supports extended flight durations, providing comprehensive field coverage in a single operational cycle. With a modular structure, each individual in the plurality of drones 102 can be equipped with additional sensors as needed, such as humidity and temperature gauges, to further expand data collection capabilities. The adaptability of each individual in the plurality of drones 102 ensures that farm managers can receive detailed, high-quality data across diverse agricultural conditions, facilitating a highly customizable monitoring system.
[0059] The image capturing unit 104, integrated with each individual in the plurality of drones 102, serves as the primary component for visual and spectral data acquisition. The image capturing unit 104 comprises high-resolution cameras and multispectral imaging sensors designed to capture diverse data layers essential for assessing plant health, environmental conditions, and soil characteristics. Each image capturing unit 104 captures images in visible and infrared spectra, enabling the identification of crop stress indicators, water content, and pest activity. Data collected by each image capturing unit 104 is transmitted to the processing unit 106 in real-time, where it undergoes analysis to generate actionable insights for farm managers. The advanced imaging capabilities of each image capturing unit 104 allow for early detection of crop issues, which supports proactive management practices. Each image capturing unit 104 also enables the differentiation of crop stages, allowing farm managers to customize care routines based on plant maturity. The image capturing unit 104 plays a pivotal role in the system's ability to provide comprehensive monitoring, facilitating a sustainable approach to agriculture through precision insights.
[0060] The processing unit 106 functions as the central hub of computational analysis within the agricultural monitoring system 100. The processing unit 106 is responsible for aggregating data from the image capturing unit 104, adaptive coordination unit 112, and other system components to derive actionable insights. Equipped with an artificial intelligence module 108 and a predictive analytics module 110, the processing unit 106 employs advanced algorithms to analyse images, assess crop health, and identify potential threats. The processing unit 106 not only performs real-time processing but also enables the integration of historical data from the data storage unit 124, enhancing analytical accuracy through contextual insights. As data is processed, the processing unit 106 generates notifications and recommendations that are transmitted via the communication network 116 to the user device 118 for immediate review. The processing unit 106 plays a critical role in converting raw agricultural data into usable information, facilitating timely and effective farm management decisions that optimize resources and productivity.
[0061] The processing unit 106 employs advanced data fusion techniques to integrate information from each image capturing unit 104 and adaptive coordination unit 112, providing farm managers with a unified dataset that reflects real-time agricultural conditions. The processing unit 106 uses scalable architecture, allowing it to handle large datasets generated by extensive fields monitored by the plurality of drones 102. Additionally, the processing unit 106 includes redundancy protocols to safeguard data against system failures, ensuring consistent data processing even in adverse conditions. The processing unit 106 also features energy-efficient processing techniques to reduce operational costs, making it suitable for long-term use in resource-intensive environments. By consolidating and analysing data with precision, the processing unit 106 contributes to a sustainable approach in modern agriculture, supporting proactive measures in crop health management.
[0062] The artificial intelligence module 108, housed within the processing unit 106, utilizes machine learning algorithms and pattern recognition techniques to interpret data collected by the image capturing unit 104. Through image classification and trend analysis, the artificial intelligence module 108 identifies deviations in crop health, including nutrient deficiencies, water stress, and pest infestations. By comparing current data with historical datasets stored in the data storage unit 124, the artificial intelligence module 108 can distinguish between normal growth patterns and potential threats. This analytical capability allows the artificial intelligence module 108 to generate early warnings for farm managers, supporting proactive interventions. Additionally, the artificial intelligence module 108 continues to improve its analytical accuracy by learning from each data cycle, thus enhancing its predictive power over time. The artificial intelligence module 108 is a vital asset for precision agriculture, providing insights that contribute to both immediate actions and long-term planning for crop health and productivity.
[0063] The artificial intelligence module 108 is continually updated with data from each image capturing unit 104 and processing unit 106 to improve accuracy in crop health assessment. The artificial intelligence module 108 uses deep learning algorithms to detect patterns that indicate early stages of plant diseases or stress. Each pattern detected by the artificial intelligence module 108 undergoes rigorous verification, enhancing the reliability of predictions. Additionally, the artificial intelligence module 108 collaborates with the adaptive coordination unit 112 to optimize drone deployment based on identified risk areas, enabling targeted interventions. The artificial intelligence module 108 also leverages data from external sources, such as weather forecasts and soil studies, to contextualize its analysis further. This comprehensive approach allows the artificial intelligence module 108 to provide actionable insights that empower farm managers to maintain optimal crop health across changing environmental conditions.
[0064] The predictive analytics module 110, integrated within the processing unit 106, focuses on forecasting potential agricultural issues by analysing historical data from the data storage unit 124 alongside current conditions. Using sophisticated algorithms, the predictive analytics module 110 identifies trends related to seasonal patterns, soil health, and pest activity, enabling early predictions of crop diseases, pest outbreaks, and other threats. The predictive analytics module 110 provides farm managers with a strategic advantage by generating data-driven forecasts, allowing for timely preparations and preventive measures. By correlating various environmental and crop health factors, the predictive analytics module 110 offers a holistic perspective on agricultural risk management. The predictive capabilities of the predictive analytics module 110 are essential for maximizing yield and reducing resource wastage, helping farmers to achieve sustainable and profitable outcomes in their agricultural practices.
[0065] The predictive analytics module 110 includes a specialized algorithm that correlates current field data with historical data from the data storage unit 124, identifying emerging trends before they impact yield. By assessing long-term patterns in crop growth cycles, pest populations, and weather events, the predictive analytics module 110 generates highly accurate forecasts that guide resource planning. The predictive analytics module 110 integrates with external databases, such as climate data repositories, to enhance predictive precision. Additionally, the predictive analytics module 110 supports scenario analysis, allowing farm managers to test the potential outcomes of various agricultural strategies. This capability makes the predictive analytics module 110 a powerful tool in strategic decision-making, enabling farm managers to adopt sustainable practices that enhance both yield and profitability.
[0066] The adaptive coordination unit 112 manages the flight paths and operational timing of each individual in the plurality of drones 102, ensuring optimal data collection. Through real-time adjustments based on environmental feedback and data from the processing unit 106, the adaptive coordination unit 112 enhances coverage and minimizes overlap among individuals in the plurality of drones 102. The adaptive coordination unit 112 continuously monitors factors such as soil moisture, humidity, and weather conditions, recalibrating flight paths to account for real-time changes. By synchronizing the activities of each individual in the plurality of drones 102, the adaptive coordination unit 112 ensures efficient and effective agricultural monitoring across large areas. The adaptive coordination unit 112 is instrumental in maintaining the overall efficiency of the system, enabling seamless coordination and data collection for precise agricultural insights.
[0067] The adaptive coordination unit 112 utilizes machine learning models to predict optimal flight paths for each individual in the plurality of drones 102 based on real-time field data. The adaptive coordination unit 112 continuously refines its algorithms to adapt to new environmental conditions, ensuring optimal drone performance. The adaptive coordination unit 112 communicates with the processing unit 106 to adjust drone activities, prioritizing areas with urgent crop health issues. Furthermore, the adaptive coordination unit 112 allows the plurality of drones 102 to dynamically avoid obstacles, minimizing operational risks. By coordinating drone actions with precision, the adaptive coordination unit 112 enhances the system's efficiency, supporting comprehensive and accurate agricultural monitoring across large fields.
[0068] The variable-rate spraying unit 114 is a critical component for applying agrochemicals with precision, minimizing environmental impact. By utilizing crop health data processed by the processing unit 106, the variable-rate spraying unit 114 adjusts its application based on the specific needs of different areas within the field. This targeted approach allows the variable-rate spraying unit 114 to reduce chemical usage and avoid over-application, supporting environmentally sustainable practices. The variable-rate spraying unit 114 is equipped with sensors and nozzles that control chemical distribution, ensuring only necessary amounts are applied to each crop area. By integrating advanced analytics from the artificial intelligence module 108, the variable-rate spraying unit 114 enhances crop health management while reducing chemical waste.
[0069] The variable-rate spraying unit 114 operates in conjunction with the artificial intelligence module 108 to customize chemical application based on specific crop health needs identified by the processing unit 106. Each sensor within the variable-rate spraying unit 114 continuously monitors environmental variables, such as wind speed and humidity, to adjust spraying parameters for maximum effectiveness. The variable-rate spraying unit 114 also includes anti-drift technology to minimize chemical dispersal beyond targeted areas, protecting surrounding ecosystems. The integration of intelligent spraying patterns in the variable-rate spraying unit 114 contributes to reduced chemical wastage and supports sustainable farming practices. The variable-rate spraying unit 114 enables precise chemical application, leading to healthier crops and a lower environmental impact.
[0070] The communication network 116 connects all components, including the processing unit 106, the user device 118, and each individual in the plurality of drones 102, facilitating seamless data transmission. The communication network 116 employs wireless protocols, enabling real-time updates and remote monitoring capabilities. This allows users to access critical information from the agricultural monitoring system 100 anytime, improving responsiveness to crop health issues. The communication network 116 provides the foundation for an integrated system, enabling continuous data flow and immediate access to analysis results for informed decision-making.
[0071] The communication network 116 uses secure protocols to protect data transmitted between each component, ensuring the integrity of sensitive agricultural information. The communication network 116 features adaptive bandwidth allocation, prioritizing data from critical operations to prevent delays in information flow. Each component connected to the communication network 116 remains synchronized, enabling seamless integration across the agricultural monitoring system 100. The communication network 116 includes backup channels to maintain connectivity during network disruptions, ensuring continuous data availability. By providing a robust infrastructure for data exchange, the communication network 116 enhances the reliability and responsiveness of the monitoring system, supporting efficient farm management.
[0072] The user device 118 serves as the primary access point for farm managers to monitor and control various system functions. Linked to the communication network 116, the user device 118 displays data insights, operational statuses, and alerts generated by the processing unit 106. The user device 118 offers a user-friendly interface for remotely managing the agricultural monitoring system 100, allowing farm managers to adjust drone operations and spraying protocols based on real-time data. By offering instant access to critical information, the user device 118 enhances farm management capabilities, allowing for efficient, data-driven decisions.
[0073] The cross-platform user interface 120 provides a comprehensive view of agricultural data, transforming complex information into digestible visual formats such as graphs and alerts. Located on the user device 118, the cross-platform user interface 120 allows users to monitor crop health trends and receive updates on environmental factors affecting field productivity. The cross-platform user interface 120 ensures that farm managers, regardless of technical expertise, can easily interpret and act on the system's data analysis, fostering efficient agricultural management and planning.
[0074] The cross-platform user interface 120 is designed to display complex agricultural data in an easily understandable format, using visual aids like heat maps and trend graphs to highlight critical areas of concern. The cross-platform user interface 120 adapts to different devices, ensuring consistent user experience on smartphones, tablets, and computers. Each display within the cross-platform user interface 120 is customizable, allowing farm managers to focus on specific metrics relevant to their needs. The cross-platform user interface 120 also includes multi-user access, enabling collaborative decision-making among farm management teams. By presenting data clearly and concisely, the cross-platform user interface 120 enhances data-driven decision-making in agriculture.
[0075] The financial analysis module 122, integrated with the cross-platform user interface 120, assists farm managers by providing cost-benefit analyses of agricultural practices. By examining yield projections, resource usage, and potential revenue, the financial analysis module 122 supports informed financial decisions. This economic insight helps farmers allocate resources effectively, optimizing profitability while maintaining sustainable practices.
[0076] The financial analysis module 122 integrates cost-related data with agricultural insights from the processing unit 106, providing a comprehensive economic assessment of farm activities. The financial analysis module 122 evaluates costs associated with chemical applications, water usage, and labour, offering a holistic view of operational expenses. Additionally, the financial analysis module 122 generates profitability forecasts based on yield projections and market conditions, enabling farm managers to optimize resource allocation. The financial analysis module 122 is designed to support sustainable farming practices by highlighting areas where cost savings can be achieved without compromising crop health.
[0077] The data storage unit 124 stores all collected and processed agricultural data, creating a valuable archive for long-term trend analysis. Integrated with cloud services, the data storage unit 124 enables secure, multi-seasonal storage of crop health information, environmental data, and operational logs. The data storage unit 124 provides historical context for the artificial intelligence module 108 and predictive analytics module 110, improving predictive accuracy and supporting data-driven planning.
[0078] The data storage unit 124 employs a distributed storage architecture, ensuring redundancy and data integrity for all information collected by each component in the agricultural monitoring system 100. The data storage unit 124 supports multi-seasonal data retention, allowing farm managers to access historical data across multiple growth cycles. Additionally, the data storage unit 124 includes encryption protocols to safeguard sensitive agricultural data, ensuring compliance with data protection regulations. The data storage unit 124's ability to archive vast amounts of information enables farm managers to make data-driven decisions based on comprehensive, longitudinal analysis.
[0079] FIG. 2 illustrates a flowchart of artificial intelligence driven multi drone agricultural monitoring system, in accordance with an exemplary embodiment of the present disclosure.
[0080] At 202, drones equipped with image capturing units collect real-time images of agricultural fields.
[0081] At 204, the captured images are transmitted to the processing unit.
[0082] At 206, the processing unit, using an artificial intelligence module with a convolutional neural network, analyses images to detect animal behaviour patterns and assess crop health.
[0083] At 208, the adaptive coordination unit adjusts drone paths dynamically based on environmental conditions such as soil moisture and weather.
[0084] At 210, a predictive analytics module analyses historical and environmental data to anticipate potential crop diseases.
[0085] At 212, drones apply chemicals precisely based on plant health analysis, minimizing chemical waste.
[0086] At 214, monitoring data and analysis results are sent to a cross-platform user interface for user access, and data is archived for long-term planning.
[0087] FIG. 3 illustrates a flowchart of artificial intelligence driven multi drone agricultural monitoring method, in accordance with an exemplary embodiment of the present disclosure.
[0088] At 302, capturing real-time images of agricultural fields through a plurality of drones equipped with an image capturing unit.
[0089] At 304, transmitting the captured images to a processing unit, where an artificial intelligence module including a convolutional neural network model analyses the images to identify distinct animal behaviour patterns and assess crop health based on historical and environmental data, providing insights into potential crop threats.
[0090] At 306, coordinating the drone paths dynamically through an adaptive coordination unit based on environmental factors, adjusting the drones' activities according to real-time soil moisture and weather conditions.
[0091] At 308, predicting and preventing crop diseases by analysing seasonal and soil condition patterns through a predictive analytics module in communication with the artificial intelligence module.
[0092] At 310, applying precise amounts of chemicals through a variable-rate spraying unit connected to the drones, based on the health analysis of the plants to minimize environmental impact and reduce chemical waste.
[0093] At 312, transferring the monitoring data and analysis results in real-time to a cross-platform user interface via a communication network, enabling farmers to make immediate decisions and take prompt actions.
[0094] At 314, archiving data on soil and crop health trends in a data storage unit to support long-term crop planning.
[0095] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0096] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0097] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0098] Disjunctive language such as the phrase "at least one of X, Y, Z," unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0099] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An artificial intelligence driven multi drone agricultural monitoring system (100), the system (100) comprising:
a plurality of drones (102) equipped with an image capturing unit (104) for collecting real-time images of agricultural fields;
a processing unit (106) connected to the image capturing unit (104), housing an artificial intelligence module (108) including a convolutional neural network model, configured to analyse the captured images and differentiate animal behaviour patterns and crop health based on historical and environmental data;
an adaptive coordination unit (112) linked to each drone, enabling dynamic adjustments of drone paths based on environmental factors such as soil moisture and weather conditions;
a predictive analytics module (110) in communication with the artificial intelligence module (108), designed to anticipate potential crop diseases by analysing seasonal and soil condition patterns;
a variable-rate spraying unit (114) connected to the plurality of drones (102), applying precise chemical amounts based on plant health analysis to minimize chemical waste and environmental impact;
a communication network (116) operatively connecting the processing unit (106) to a cross-platform user interface (120), wherein the communication network (116) enables real-time transmission of monitoring data and analysis results from the plurality of drones (102) to the cross-platform user interface (120) for immediate user access and decision-making;
a cross-platform user interface (120) integrated in a user device (118) for accessing real-time monitoring, financial analysis, and long-term soil health insights;
a data storage unit (124) for archiving historical crop and soil health data.
2. The system (100) as claimed in claim 1, wherein the artificial intelligence module (108) within the processing unit (106) includes an animal behaviour analysis model, enabling it to predict and prevent specific crop threats by recognizing patterns in animal interactions with crops.
3. The system (100) as claimed in claim 1, wherein the adaptive coordination unit (112) further adjusts drone routes based on real-time changes in humidity, wind speed, and temperature, enhancing efficiency and resource management.
4. The system (100) as claimed in claim 1, wherein the adaptive coordination unit (112) synchronizes with external weather databases, providing weather-based alerts that influence drone route adjustments and intervention strategies.
5. The system (100) as claimed in claim 1, wherein the predictive analytics module (110) identifies disease trends by analysing historical data on crop health, soil fertility, and seasonal conditions, allowing proactive disease prevention.
6. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to integrate data from additional sensors located on the plurality of drones (102), including thermal and infrared sensors.
7. The system (100) as claimed in claim 1, wherein the cross-platform user interface (120) provides a financial analysis module (122), offering farmers real-time cost and revenue projections based on crop health and suggested management actions.
8. The system (100) claimed in claim 1, wherein the adaptive coordination unit (112) uses machine learning algorithms to generate customized flight paths for each drone based on the crop type, growth stage, and field topography.
9. The system (100) as claimed in claim 1, wherein the data storage unit (124) is connected to a cloud-based database that enables multi-seasonal data comparison, allowing the system to analyse long-term trends in soil health and crop productivity, which facilitates adaptive planning for upcoming growing cycles.
10. An artificial intelligence driven multi drone agricultural monitoring method (100) comprising:
capturing real-time images of agricultural fields through a plurality of drones (102) equipped with an image capturing unit (104);
transmitting the captured images to a processing unit (106), where an artificial intelligence module (108) including a convolutional neural network model analyses the images to identify distinct animal behaviour patterns and assess crop health based on historical and environmental data, providing insights into potential crop threats;
coordinating the plurality of drones (102) paths dynamically through an adaptive coordination unit (112) based on environmental factors, adjusting the plurality of drones (102) activities according to real-time soil moisture and weather conditions;
predicting and preventing crop diseases by analysing seasonal and soil condition patterns through a predictive analytics module (110) in communication with the artificial intelligence module (108);
applying precise amounts of chemicals through a variable-rate spraying unit (114) connected to the plurality of drones (102), based on the health analysis of the plants to minimize environmental impact and reduce chemical waste;
transferring the monitoring data and analysis results in real-time to a cross-platform user interface (120) via a communication network (116), enabling farmers to make immediate decisions and take prompt actions;
archiving data on soil and crop health trends in a data storage unit (124) to support long-term crop planning.

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