AI-Enhanced Multi-Spectral Image Analysis Platform for Precision Diagnostics and Predictive Analytics

Abstract

The present invention provides an AI-powered multi-spectral image analysis system for early detection and predictive analytics across various applications. This system comprises a multi-spectral sensor fusion unit, contextual deep neural network (CDNN), and predictive analytics engine. The sensor fusion unit captures data across diverse spectra, while the CDNN, with Context-Aware Modules, processes contextual spectral features to detect anomalies. The predictive analytics engine forecasts potential outcomes by assessing historical spectral trends. This real-time platform enables proactive interventions across fields such as agriculture, healthcare, and environmental monitoring by offering timely diagnostics and forecasting capabilities. Accompanied Drawing [FIG. 1]

Specification

Description:[001] The invention pertains to the technical field of artificial intelligence and multi-spectral imaging systems. More specifically, it relates to a platform that integrates AI-driven multi-spectral image analysis and sensor fusion technologies for early detection and predictive analytics. The platform leverages advanced deep neural network (DNN) architectures, including contextual DNNs, and predictive modeling engines that operate on multi-spectral data to enable highly accurate, early-stage anomaly detection and diagnosis. Applications of this technology span across multiple domains, including agriculture, healthcare, and environmental monitoring, where early detection of conditions or phenomena is crucial for timely intervention and mitigation of adverse outcomes. This invention combines innovative data fusion, spectral analysis, and deep learning to create a versatile platform capable of detecting subtle changes in spectral patterns, classifying anomalies in real time, and forecasting potential outcomes based on historical trends.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Conventional imaging and diagnostic systems often utilize single-spectrum data or standard RGB imaging, which limits their ability to detect subtle anomalies at early stages. Multi-spectral imaging, a technique that captures data across a range of wavelengths beyond the visible spectrum, offers the potential for enhanced diagnostic accuracy by revealing unique spectral signatures of various materials, biological tissues, or environmental conditions. However, current multi-spectral systems lack integration with advanced AI technologies capable of performing contextual analysis and predictive modeling, limiting their effectiveness in practical applications. These systems are often constrained by domain-specific limitations and lack the ability to generalize across multiple fields or provide real-time predictions.
[004] In agriculture, for instance, traditional diagnostic tools struggle to detect early signs of crop diseases, nutrient deficiencies, or environmental stressors until symptoms are visibly apparent, often resulting in significant crop losses. Similarly, in healthcare, early detection of conditions like cancer, infections, or degenerative diseases can significantly improve treatment outcomes; however, standard imaging techniques are often insufficiently sensitive at early stages. In environmental monitoring, where rapid response to pollution or ecosystem degradation is essential, conventional imaging techniques fall short of providing real-time alerts and long-term impact predictions.
[005] The advent of deep neural networks has shown promise in improving image analysis by automatically learning features from complex datasets. However, typical DNN models are limited in their ability to interpret data under varying environmental contexts or perform predictive analytics based on temporal changes. Existing AI-based multi-spectral systems, if any, are limited in scope, focusing narrowly on specific applications without offering the flexibility and adaptability needed to address a broader range of diagnostic challenges. There is a need for an AI-powered multi-spectral analysis platform that not only detects anomalies early across various fields but also provides actionable insights and predictive forecasts to facilitate proactive decision-making.
[006] Accordingly, to overcome the prior art limitations based on aforesaid facts. The present invention provides an AI-Enhanced Multi-Spectral Image Analysis Platform for Precision Diagnostics and Predictive Analytics. Therefore, it would be useful and desirable to have a system, method and apparatus to meet the above-mentioned needs.

SUMMARY OF THE PRESENT INVENTION
[007] The present invention provides an advanced AI-enhanced multi-spectral imaging platform that integrates multi-spectral sensor fusion, contextual deep neural networks (CDNNs), and a predictive analytics engine. This comprehensive system is designed for early anomaly detection, diagnostics, and trend forecasting, making it a valuable tool across fields such as agriculture, healthcare, and environmental monitoring. The multi-spectral sensor fusion unit (MSFU) captures data across multiple spectral bands, including visible, near-infrared, far-infrared, and ultraviolet, enabling a richer dataset for analysis. The CDNN processes this data using context-sensitive feature extraction techniques that account for environmental factors and application-specific conditions, thereby increasing diagnostic accuracy. Additionally, a predictive analytics engine uses historical spectral trends to forecast potential developments, offering proactive insights into detected anomalies.
[008] Through real-time edge processing and cloud integration, this platform facilitates both immediate diagnostics and long-term trend analysis. The edge computing component processes data locally, providing rapid feedback for real-time applications, while the cloud component performs deeper analysis and model updates, ensuring that the system continuously improves with ongoing data collection. This invention not only offers more precise early detection but also empowers users to anticipate and respond to potential issues before they escalate, making it an invaluable asset in fields requiring precision diagnostics and early intervention.
[009] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[010] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[011] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[012] Figure 1: Illustrates the system architecture of the AI-enhanced multi-spectral imaging platform, highlighting key components including the multi-spectral sensor fusion unit (MSFU), contextual deep neural network (CDNN), and predictive analytics engine. This figure provides a high-level overview of data flow and interactions among system components.
[013] Figure 2: Depicts the functional architecture of the multi-spectral sensor fusion unit, detailing how different spectral bands (visible, NIR, FIR, UV) are captured, synchronized, and pre-processed for enhanced accuracy and reduced noise.
[014] Figure 3: Presents a flowchart of the contextual deep neural network (CDNN) processing, showcasing the use of contextual feature extraction layers and the integration of context-aware modules (CAMs) that dynamically adjust to environmental and application-specific factors.
[015] Figure 4: Outlines the predictive analytics engine's functionality, showing the process of anomaly detection, historical data analysis, and trend forecasting for identified anomalies. This flow provides insight into how the system predicts potential outcomes and alerts users for proactive intervention.
[016] Figure 5: Shows various application scenarios, including (a) agriculture, (b) healthcare, and (c) environmental monitoring. Each example demonstrates the system's utility and output in respective fields, emphasizing its adaptability and effectiveness across different diagnostic contexts.
DETAILED DESCRIPTION OF THE INVENTION
[017] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[018] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[019] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[020] This invention presents an advanced image and video compression system that combines hybrid neural networks (Variational Autoencoders, Generative Adversarial Networks, and Transformers) with quantum computing and edge computing for enhanced efficiency and scalability. The system compresses data by encoding it into a latent space, reconstructing high-quality images, and capturing dependencies across video frames. Quantum processors handle intensive computations, while edge computing facilitates real-time compression closer to data sources. Auxiliary data and meta-learning optimize compression for varying content, and a reinforcement learning agent ensures adaptive data flow in fluctuating network conditions. This system is suited for applications requiring high-quality, low-latency compression, such as streaming, telemedicine, and AR/VR.
[021] Multi-Spectral Sensor Fusion Unit (MSFU)
The multi-spectral sensor fusion unit (MSFU) is a cornerstone of the invention, responsible for capturing and combining data across a range of spectral bands, including visible, near-infrared (NIR), far-infrared (FIR), and ultraviolet (UV) wavelengths. Unlike conventional imaging systems that rely on single-spectrum or RGB data, the MSFU's adaptive sensor array can dynamically adjust its spectral sensitivity based on environmental conditions and application requirements. This adaptive approach ensures optimal data quality by capturing the most relevant spectral information while minimizing noise and data redundancy. For instance, in agricultural applications, NIR sensors focus on chlorophyll levels to detect plant stress, while FIR sensors capture thermal variations useful for assessing crop health. In healthcare, UV and NIR spectra are particularly useful for identifying tissue anomalies and early signs of skin abnormalities.
[022] To ensure synchronized and high-quality multi-spectral data, the MSFU employs pre-processing techniques, including spectral alignment and spatial calibration. This alignment corrects any distortions caused by varying spectral resolutions or sensor positioning, enabling seamless data integration before the information is fed into the AI engine. This pre-processed multi-spectral data is then provided as input to the contextual deep neural network (CDNN), which interprets it for further analysis and diagnostic purposes.
[023] Contextual Deep Neural Network (CDNN)
At the core of the AI-enhanced multi-spectral imaging platform is the contextual deep neural network (CDNN), which processes multi-spectral data using a sophisticated, context-sensitive architecture. Traditional DNN models struggle with interpreting data under varying environmental or contextual conditions, often leading to inaccuracies in anomaly detection and classification. The CDNN overcomes this limitation by integrating Context-Aware Modules (CAMs), which allow the network to adjust feature extraction and interpretation based on specific contextual inputs, such as environmental conditions, operational settings, and time of day.
The architecture of the CDNN comprises multiple convolutional layers that can handle multi-channel inputs, allowing it to process data across various spectral bands simultaneously. Context-Aware Modules dynamically adjust the sensitivity of each layer, ensuring that relevant spectral features are emphasized while irrelevant variations, such as those caused by lighting or temperature changes, are minimized. This conditional feature extraction process is particularly beneficial in applications where spectral data is subject to variability due to environmental factors. For example, in agriculture, the CDNN can differentiate between crop stress caused by nutrient deficiency versus drought by analyzing unique spectral patterns associated with each condition. The output of the CDNN includes a classification of the detected anomalies, categorizing them as "normal," "early-stage anomaly," or "critical anomaly," depending on the severity and type of anomaly detected.
[024] Predictive Analytics Engine
The predictive analytics engine extends the diagnostic capabilities of the platform by forecasting potential developments based on historical spectral data trends and the current state of detected anomalies. Traditional diagnostic systems are limited to detecting present conditions without providing insights into future trends or outcomes. The predictive analytics engine addresses this gap by employing trend-based prediction models that analyze historical anomaly data, as well as temporal patterns within the multi-spectral data, to estimate the likely progression of a detected condition.
[025] In agricultural applications, for instance, the predictive analytics engine can use historical data on plant diseases and environmental stressors to predict disease spread or crop yield impact based on current spectral readings. Similarly, in healthcare, the engine can forecast disease progression in a patient by analyzing spectral changes over time. This capability is critical for proactive intervention, allowing users to take preventative actions or adjust treatment plans before conditions worsen. By operating in real-time, the predictive analytics engine empowers users across different domains to make data-driven decisions based on reliable, forward-looking insights.
[026] Real-Time Cloud Integration with Edge Computing
The invention's architecture is designed to support real-time analysis and scalability through a hybrid edge-cloud framework. An edge-computing layer within the system performs preliminary analysis on-site or within the device, providing immediate feedback and reducing latency for applications requiring real-time diagnostics. This is especially valuable in remote or field-based scenarios where immediate data processing is crucial, such as agricultural crop monitoring via drones or mobile devices.
For deeper analysis and predictive modeling, the system employs a cloud-based processing platform. This cloud integration enables large-scale data processing, model updates, and centralized data storage, ensuring that the platform continuously learns from accumulated data and improves over time. The edge-computing and cloud-based processing components work in tandem, creating a distributed workflow that balances efficiency with scalability. Data processed locally by edge devices can be uploaded to the cloud for further analysis, refinement of prediction models, and incorporation into long-term datasets. This approach ensures that the system remains responsive to real-time needs while benefiting from the expansive computational resources and storage capabilities of cloud infrastructure.
[027] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[028] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[029] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. An AI-enhanced multi-spectral image analysis system comprising a multi-spectral sensor fusion unit, a contextual deep neural network, and a predictive analytics engine, wherein the multi-spectral sensor fusion unit captures data across multiple spectral bands, the contextual deep neural network analyzes contextual spectral features, and the predictive analytics engine forecasts potential outcomes based on detected anomalies.

2. The system of Claim 1, wherein the multi-spectral sensor fusion unit comprises an array of adaptive sensors capable of dynamically adjusting spectral sensitivity based on environmental conditions to optimize data quality.

3. The system of Claim 1, wherein the contextual deep neural network includes Context-Aware Modules configured to perform conditional feature extraction based on environmental factors and contextual data.

4. The system of Claim 1, wherein the predictive analytics engine utilizes historical spectral data trends to assess and forecast the progression of detected anomalies.

5. The system of Claim 1, further comprising an edge-computing module that performs initial spectral data processing locally and a cloud-based processing module for model updates and real-time forecasting.

6. A method of diagnosing early anomalies, comprising capturing multi-spectral data across multiple spectra, processing data through a contextual deep neural network to identify anomalies, and applying predictive analytics to forecast potential progression of detected anomalies.

7. The method of Claim 6, wherein the contextual deep neural network is trained to recognize and differentiate spectral patterns under varying environmental conditions using Context-Aware Modules.

8. The method of Claim 6, wherein the predictive analytics engine assesses anomaly trends over time to predict likely developments of the detected condition.

9. An application of the system of Claim 1 in agriculture for early disease detection in crops, wherein multi-spectral imaging and predictive analytics assist in timely intervention.


10. An application of the system of Claim 1 in healthcare for early-stage diagnostics of tissue abnormalities, wherein multi-spectral imaging identifies spectral anomalies indicative of potential health conditions.

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