An intelligent, modular system for monitoring plant health

Abstract

An AI-supported, intelligent and modular system for monitoring plant health for precision agriculture, consisting of: a deployment device configured for field surveillance, the deployment device being selected either as an airborne drone platform for aerial surveillance or as a ground-based rover platform for ground-based surveillance, depending on the field size and terrain; A detachable sensor module attached to the deployment device for acquiring field monitoring data, wherein the detachable transmitting module comprises: an embedded edge computing unit; a red-green-blue (RGB) camera; a near-infrared (NIR) camera; a temperature sensor; a position sensor; calibration reference targets stored on the edge computing unit; and a wireless communication interface, wherein the embedded edge computing unit is configured to: synchronize data acquisition from the RGB camera, the NIR camera, and the temperature sensor; acquire high-resolution images with the RGB camera and the NIR camera; and record temperature data, humidity data, and metadata including timestamps, elevation, orientation, and coordinates; an external storage medium that is connected via the wireless communication interface to the embedded edge computing unit of the detachable sensor module and receives all data captured by multiple sensors and the camera in real time from the embedded edge computing unit; and A desktop application runs on an external computer device with a processor, and this external computer device is connected to an external storage medium. The data stored on the external storage medium is shared with the desktop application.The desktop application includes a computing module that performs the following functions: retrieving the captured data from the external storage medium; preprocessing the captured data through noise reduction, illumination normalization, radiometric correction, and image alignment; calculating vegetation indices, consisting of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI), from the preprocessed data; applying artificial intelligence models to identify plant stress states based on the vegetation indices; segmenting an agricultural field into multiple health zones based on the identified plant stress states; assigning a severity level to each health zone; and generating recommendations for each health zone based on the severity level.

Description

AREA OF INVENTION

[0001] The present disclosure relates to a plant health monitoring system called AgriHealth, in particular an intelligent, modular plant health monitoring system. More precisely, the invention relates to an AI-supported, intelligent and modular plant health monitoring system for precision agriculture, which combines thermal sensing, multispectral photography and artificial intelligence (AI) to continuously assess the health status of plants and determine their stress level. BACKGROUND OF THE INVENTION

[0002] Plant health is an essential and crucial aspect of agriculture, as it influences the overall productivity of a farm. Continuous monitoring of plant health and early detection of related problems are therefore important steps in modern agriculture.

[0003] Conventional field inspection methods are often time-consuming, labor-intensive, and in most cases insufficient for detecting stress factors in their early stages. Satellite-based monitoring systems have problems with low temporal resolution, cloud cover, or insufficient spatial detail on small and medium-sized farms.

[0004] Ground-based and drone-based surveillance systems lack adaptability to different field sizes, leading to inconsistent results.

[0005] Furthermore, most existing solutions generate unprocessed data, making it difficult for end users to interpret.

[0006] The aforementioned limitations underscore the need for an intelligent and scalable plant monitoring system that can provide farmers with timely insights to secure their harvests. SUMMARY OF THE INVENTION

[0007] This disclosure relates to an intelligent and scalable plant monitoring system that provides farmers with timely information to protect their crops. The system combines thermal sensors, multispectral imaging, and AI techniques to calculate vegetation indices and automatically analyze plant health. This helps farmers detect plant health problems early, enabling timely decision-making so that preventive measures can be taken proactively.

[0008] This disclosure describes an AI-powered, intelligent, and modular system for monitoring plant health in precision agriculture. The system comprises: a field monitoring application device that, depending on field size and terrain, can be used either as a drone for aerial monitoring or as a rover for ground monitoring. The system also includes a detachable sensor module for data acquisition in the field. This module includes: an integrated edge computing unit; an RGB camera; a near-infrared (NIR) camera; a temperature sensor; a position sensor; calibration reference targets stored on the edge computing unit; and a wireless communication interface.The integrated edge computing unit is configured to synchronize data acquisition from the RGB camera, NIR camera, and temperature sensor; capture high-resolution images with the RGB and NIR cameras; and store temperature and humidity data, as well as metadata such as timestamps, altitude, orientation, and coordinates. The system also includes an external storage device connected to the embedded edge computing unit of the detachable sensor module via a wireless communication interface. This external storage device receives all data acquired by the multiple sensors and the camera in real time from the embedded edge computing unit. The system further includes a desktop application running on an external computer with a processor. This external computer is connected to the external storage device. The data stored on the external storage device is shared with the desktop application.The desktop application includes a computing module that performs the following functions: retrieving the captured data from the external storage medium; preprocessing the captured data through noise reduction, illumination normalization, radiometric correction, and image alignment; calculating vegetation indices, consisting of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI), using artificial intelligence (AI) models to identify plant stress states; segmenting an agricultural field into multiple health zones based on the identified plant stress states; assigning a severity score to each health zone; and generating recommendations for each health zone based on the severity score.

[0009] The aim of the present invention is to provide an intelligent and scalable plant monitoring system that gives farmers timely insights to protect their crops.

[0010] Another objective of the present invention is to provide a modular and cross-platform system for monitoring plant health that can be used on both ground vehicles and drones to collect field data using a common sensor module.

[0011] Another objective of the present invention is the automation of the analysis of thermal and spectral data for the calculation of vegetation indices and for the early determination of stress levels.

[0012] Another objective of the present invention is the automated analysis of thermal and spectral data for the calculation of vegetation indices and for the early determination of stress levels.

[0013] Another objective of the present disclosure is to provide an AI-based assessment system for the health status of crops, which uses vegetation index data to divide agricultural areas into different health zones based on the condition of the plants.

[0014] However, another objective of the present disclosure is to provide a real-time dashboard that can convert complex sensor information into simple health values ​​so that countermeasures can be initiated in a timely manner to protect the crop.

[0015] To further clarify the advantages and features of the present disclosure, the invention is described in more detail with reference to specific embodiments illustrated in the accompanying drawing. It is understood that this drawing merely shows typical embodiments of the invention and is therefore not to be understood as limiting its scope of protection. The invention is described and explained in more detail and with reference to the accompanying drawing. BRIEF DESCRIPTION OF THE IMAGE

[0016] These and other features, aspects and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts, wherein: Fig. Figure 1 shows a block diagram of an AI-supported, intelligent and modular plant health monitoring system for precision agriculture according to an embodiment of the present disclosure.

[0017] Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. DETAILED DESCRIPTION:

[0018] To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention.

[0019] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0020] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0021] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0023] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0024] The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware such as processors, digital signal processors, central processing units, FPGAs, PALs, PLDs, cloud processing systems, or similar. Devices may also be implemented in software for execution by various processor types. An identified device may contain executable code and, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized as an object, procedure, function, or other construct. However, the executable files of an identified device need not be physically related; they may consist of different instructions stored in different locations that, when logically combined, constitute the device and fulfill its purpose.

[0025] The executable code of a device or module can consist of a single instruction or multiple instructions and can even extend across different code sections, applications, and storage media. Similarly, operational data within the device can be identified and represented, and can exist in any suitable form and be organized in any data structure. The operational data can be captured as a single data record or distributed across various storage media and may exist, at least partially, as electronic signals within a system or network.

[0026] References to “a selected embodiment”, “an embodiment”, or “an embodiment” in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the phrases “a selected embodiment”, “in an embodiment”, or “in an embodiment” appearing at different points in this description do not necessarily refer to the same embodiment.

[0027] Furthermore, the described features, structures, or properties can be combined in one or more embodiments in any suitable manner. The following description contains numerous specific details to enable a comprehensive understanding of the embodiments of the disclosed subject matter. However, a person skilled in the art will recognize that the disclosed subject matter can also be realized without one or more of the specific details or with other methods, components, materials, etc. In other cases, known structures, materials, or processes are not presented or described in detail so as not to obscure aspects of the disclosed subject matter.

[0028] According to the exemplary embodiments, the disclosed computer programs or modules can be executed in a variety of ways, for example, as an application running in the memory of a device or as a hosted application running on a server and communicating with the device application or browser via various standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs can be written in programming languages ​​that run either in the device's memory or on a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0029] Some of the described embodiments involve data transmission over a network, such as the transmission of various inputs or files. The network may include, for example, the internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., PSTN, ISDN, cellular networks, and xDSL), radio, television, cable, satellite, and / or other transmission or tunneling mechanisms for data. It may include multiple networks or subnetworks, each of which may, for example, have a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or another network for transmitting electronic data. For example, it may be based on the Internet Protocol (IP) or Asynchronous Transfer Mode (ATM) and support voice communication using VoIP, Voice over ATM, or similar protocols.In one embodiment, the network comprises a mobile network configured for the exchange of text or SMS messages.

[0030] Examples of networks include Personal Area Networks (PAN), Storage Area Networks (SAN), Home Area Networks (HAN), Campus Area Networks (CAN), Local Area Networks (LAN), Wide Area Networks (WAN), Metropolitan Area Networks (MAN), Virtual Private Networks (VPN), Enterprise Private Networks (EPN), the Internet, Global Area Networks (GAN), and so on.

[0031] Fig. Figure 1 shows a block diagram of an AI-supported, intelligent and modular plant health monitoring system for precision agriculture according to an embodiment of the present disclosure.

[0032] The AI-supported, intelligent and modular plant health monitoring system (100) according to Fig.1 comprises: a deployment device (102) for field monitoring, which, depending on field size and terrain, is used either as an airborne drone platform for aerial monitoring or as a ground-based rover platform for ground monitoring. The system (100) further comprises a detachable sensor module (104) attached to the deployment device (102) for data acquisition for field monitoring.This detachable sensor module (104) comprises: an embedded edge computing unit (104a); an RGB camera (104b); a near-infrared (NIR) camera (104c); a temperature sensor (104d); a position sensor (104e); calibration reference targets stored in the edge computing unit (104a); and a wireless communication interface (104f), wherein the embedded edge computing unit (104a) is configured to synchronize data acquisition from the RGB camera, the NIR camera, and the temperature sensor, capture high-resolution images with the RGB camera and the NIR camera, and record temperature and humidity data as well as metadata such as timestamps, altitude, orientation, and coordinates.The system (100) further comprises an external storage medium (106) which is connected via the wireless communication interface (104f) to the embedded edge computing unit (104a) of the detachable sensor module (104) and receives all acquired data from the various sensors and the camera in real time from the embedded edge computing unit (104a). The system (100) further comprises a desktop application (108) which runs on an external computer (110) with a processor. The external computer (110) is connected to the external storage medium (106), the data from which is shared with the desktop application (108).The desktop application (108) includes a computing module (108a) that performs the following functions: retrieving the acquired data from the external storage medium (106); preprocessing the acquired data by noise reduction, illumination normalization, radiometric correction, and image alignment; calculating vegetation indices, consisting of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI), from the preprocessed data; applying artificial intelligence models to identify plant stress states based on the vegetation indices; segmenting an agricultural field into multiple health zones based on the identified plant stress states; assigning a severity level to each health zone; and generating recommendations for each health zone based on the severity level.

[0033] In one embodiment, the preprocessing of the acquired data further includes: filtering for noise reduction from heterogeneous data formats; sensor-specific distortion correction; and optional georeferencing to link images with geographic coordinates.

[0034] In one embodiment, the detachable sensor module (104) is modular and interchangeable between drone and rover without requiring recalibration of the RGB camera, the NIR camera or the temperature sensor.

[0035] In one embodiment, the embedded edge computing unit (104a) is further configured to: perform automatic field calibration using the calibration reference targets; generate an optimized field traverse plan to ensure complete field coverage; and control the movement of the deployment platform according to the optimized field traverse plan.

[0036] In one embodiment, the desktop application (108) is further configured to allow the user to input field boundary details via a web-based dashboard interface and to select a suitable deployment device (102) from the group consisting of drone or rover based on the field size and terrain characteristics.

[0037] In one embodiment, the desktop application (108) is configured to: overlay images captured by the RGB camera with images captured by the NIR camera; link the preprocessed images with exact positions on a raster representation of the agricultural field; and maintain pixel-accurate correspondence between the RGB camera images and the NIR camera images.

[0038] In one embodiment, the desktop application (108) is configured to use artificial intelligence models to identify stress conditions in crops selected from a group consisting of: water stress; nutrient deficiency; disease risks; and pest indicators.

[0039] In one embodiment, the desktop application (108) is configured to divide the agricultural field into three health zones: a green zone representing healthy plants; a yellow zone representing plants with a moderate level of stress; and a red zone representing plants with a high level of stress that require immediate intervention.

[0040] In one embodiment, the desktop application (108) further comprises an AI-powered health assessment engine (108b) configured to: analyze stress patterns in each health zone; identify likely causes of stress such as water scarcity, nutrient imbalance, disease outbreak, and pest infestation; assign a severity score to each health zone based on the stress intensity; and prioritize the health zones according to the assigned severity scores.

[0041] In one embodiment, the desktop application (108) further comprises: a real-time dashboard (108c) configured to display the following: the multitude of health zones with color-coded visual representations, the severity level for each health zone, the recommendations for each health zone, and the current health status of the crops; and a reporting module (108d) configured to generate downloadable reports in Portable Document Format.

[0042] In one embodiment, the detachable transmitter module (104), the external storage medium (106), the desktop application (108) and the external computing device (110) can be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems or the like.

[0043] The present invention relates to a system for automated field analysis and plant health monitoring. First, it performs field planning to determine field boundaries and select the most suitable sensor device (drone or rover) for optimal coverage, depending on field size and geometry. The device is then calibrated and installed according to the field requirements. The device consists of position sensors, an embedded edge computing unit, an RGB camera, a near-infrared camera, a temperature sensor, calibration reference markers, and optional communication interfaces. These components enable real-time data acquisition using multiple sensors and cameras. The system stores the data on an external storage medium and transmits it to a desktop application running on the user's external PC.This application processes image and sensor data to calculate vegetation indices such as GNDVI and NDVI. Based on these vegetation indices, various plant health values ​​are generated, and stress levels are displayed. A clustering algorithm divides the field into three different health zones, each with a corresponding color code: red, yellow, and green. Green zones represent healthy plants; yellow zones indicate plants with medium stress levels; and red zones indicate plants with high stress levels that require immediate action. The system provides practical recommendations based on the type and intensity of stress. These recommendations can be viewed via the application dashboard or downloaded as a PDF report.

[0044] In one embodiment, the operation of the proposed system is divided into the following phases: field planning and equipment deployment, data acquisition, preprocessing and normalization, vegetation index calculation and zone segmentation, and plant condition monitoring and reporting. In the first phase, the system allows the user to define the boundaries of the agricultural area via a web-based dashboard. Depending on the field size and terrain, the system selects the most suitable monitoring device: a drone for aerial imagery or a rover for ground-based imaging. The device is equipped with a sensor module that automatically calibrates the field size using an integrated edge system and a calibration panel. It generates an optimized field survey plan to ensure complete field coverage and the collection of the necessary field data.In the second phase of data acquisition, the device is configured to scan the field and capture high-resolution images using RGB and NIR (near-infrared) modules. These images are then overlaid and stored in an external system. This system runs an application to process the images and extract relevant information. Additionally, the device captures other relevant parameters such as temperature, humidity, metadata (including timestamps), elevation, and coordinates. It integrates this information to create a raster view of the field, ensuring an accurate representation and enabling zone-based segmentation. In the third step of preprocessing and data normalization, the raw data may contain noise due to heterogeneous formats, leading to inaccurate predictions.Therefore, the system is configured to preprocess the data using filtering, noise reduction, illumination normalization, and sensor-specific distortion correction. This ensures pixel-perfect correspondence between different modules and correct spatial alignment. The processed images are then linked to their exact positions within the grid. In the fourth step of vegetation calculation and zone segmentation, the system calculates NVDI and GNVDI to determine the vegetation status. Various analytical models are used to assess the vegetation based on stress patterns and other indicators such as water level, nutrient deficiency, disease susceptibility, and pest risk, and to assign it to different zones. Furthermore, the system is configured to assign a severity level to each zone based on stress intensity and other stress factors.Based on this severity level, the zones are subdivided and color-coded according to the stress level of the plants. This labeling system allows for the rapid identification of the most affected zones, which require immediate intervention and attention. During the plant health analysis and reporting phase, the system analyzes each zone to determine the likely causes of plant stress, including water scarcity, nutrient imbalance, disease, or pest infestation. Based on this analysis, the system assigns a health score to each zone. The plant health analysis and reporting helps farmers address stress-related problems and prioritize their actions in affected areas. The system also supports the monitoring of lower-risk zones to prevent further escalation in the future.

[0045] In a preferred embodiment, the modular, AI-powered plant health monitoring system comprises a detachable sensor module that can be attached to both drones and rovers. The sensor module utilizes an integrated processing unit for synchronized data acquisition, an RGB and a near-infrared (NIR) camera, a thermal sensor, and calibration references. The system processes the multispectral and thermal data through noise reduction, illumination normalization, radiometric correction, image alignment, and optional georeferencing techniques, and calculates vegetation indices such as NDVI and GNDVI to assess plant health. Using AI models, the system detects stress factors such as pest infestation, water scarcity, nutrient deficiencies, or disease risks and divides the field into different health zones.An assessment system assigns a severity level to each zone and generates practical recommendations based on this. It supports farmers in making timely decisions and suggests countermeasures to prevent crop damage.

[0046] The present invention relates to an AI-supported system for the automated monitoring of plant health using thermal and multispectral imaging and data analysis. The system comprises a compact module with multiple sensors and an integrated processing unit to optimally cover the field and ensure precise and scalable plant health monitoring. It acquires data in various measurement modes to calculate vegetation indices and divide the field into health-based zones. Each zone is then analyzed using AI to assign health scores and highlight critical issues. The system also includes a user-friendly dashboard that presents detailed information to farmers in easily understandable language, enabling them to make faster decisions to protect their crops.

[0047] The drawing and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0048] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 An AI-powered, intelligent and modular system for monitoring plant health for precision agriculture. 102 - Deployment device 104 Detachable sensor module 104a Embedded Edge Computing Unit 104b Red-Green-Blue (Rgb) camera 104c Near-infrared camera (NIR) 104d temperature sensor 104e Location sensor 104f Wireless Communication Interface 106 External storage device 108 Desktop Application 108a Computing Module 108b AI-Supported Health Assessment Engine 108c Real-time Dashboard 108d Reporting Module 110 External computing device

Claims

An AI-powered, intelligent, and modular plant health monitoring system for precision agriculture, comprising: a deployment device configured for field monitoring, the deployment device being selected as either an airborne drone platform for aerial monitoring or a ground-based rover platform for ground-based monitoring, depending on field size and terrain; a detachable sensor module attached to the deployment device for capturing field monitoring data, the detachable transmitting module comprising: an embedded edge computing unit; a red-green-blue (RGB) camera; a near-infrared (NIR) camera; a temperature sensor; a position sensor; and calibration reference targets stored on the edge computing unit.and a wireless communication interface, wherein the embedded edge computing unit is configured to: synchronize data acquisition from the RGB camera, the NIR camera, and the temperature sensor; capture high-resolution images with the RGB camera and the NIR camera; and record temperature data, humidity data, and metadata including timestamps, altitude, orientation, and coordinates; an external storage medium connected via the wireless communication interface to the embedded edge computing unit of the detachable sensor module, which receives all data acquired by multiple sensors and the camera in real time from the embedded edge computing unit;A desktop application runs on an external computer device with a processor, which is connected to an external storage medium. The data stored on the external storage medium is shared with the desktop application. The desktop application includes a computing module that performs the following functions: retrieving the acquired data from the external storage medium; preprocessing the acquired data by noise reduction, illumination normalization, radiometric correction, and image alignment; calculating vegetation indices, consisting of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI), from the preprocessed data; and applying artificial intelligence models to identify plant stress states based on the vegetation indices.Segmentation of an agricultural field into several health zones based on the identified plant stress conditions; assignment of a severity level to each health zone; and generation of recommendations for each health zone based on the severity level. System according to claim 1, wherein the preprocessing of the acquired data further comprises: filtering to remove noise from heterogeneous data formats; sensor-specific distortion correction; and optional georeferencing to link images with geographic coordinates. System according to claim 1, wherein the detachable sensor module is modular and interchangeable between drone and rover without requiring recalibration of the RGB camera, the NIR camera or the temperature sensor. System according to claim 1, wherein the embedded edge computing unit is further configured to: perform automatic field calibration using the calibration reference targets; generate an optimized field traverse plan to ensure complete field coverage; and control the movement of the deployment platform according to the optimized field traverse plan. System according to claim 1, wherein the desktop application is further configured to allow the user to input field boundary details via a web-based dashboard interface and to select a suitable deployment device from the group consisting of drone or rover based on the field size and terrain characteristics. System according to claim 1, wherein the desktop application is configured to: overlay images captured by the RGB camera with images captured by the NIR camera; link the preprocessed images with exact positions on a raster representation of the agricultural field; and maintain a pixel-accurate correspondence between the RGB camera images and the NIR camera images. System according to claim 1, wherein the desktop application is configured to use artificial intelligence models to identify stress conditions in crops selected from a group consisting of: water stress; nutrient deficiency; disease risks; and pest indicators. System according to claim 1, wherein the desktop application is configured to divide the agricultural field into three health zones: a green zone representing healthy plants; a yellow zone representing plants with a moderate level of stress; and a red zone representing plants with a high level of stress requiring immediate intervention. System according to claim 1, wherein the desktop application further comprises an AI-powered health assessment engine configured to: analyze stress patterns in each health zone; identify likely causes of stress such as water scarcity, nutrient imbalance, disease outbreak, and pest infestation; assign a severity score to each health zone based on the stress intensity; and prioritize the health zones according to the assigned severity scores. System according to claim 1, wherein the desktop application further comprises: a real-time dashboard configured to display: the plurality of health zones with color-coded visual representations; the severity for each health zone; the recommendations for each health zone; and the current health status of the crop; and a reporting module configured to generate downloadable reports in Portable Document Format.

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