REAL-TIME TEMPORAL ANALYSIS FOR EARLY-STAGE DETECTION IN LARGE AREA CROP MONITORING

Abstract

This invention provides a real-time temporal analysis system for large-area crop monitoring that uses multi-sensor data fusion and advanced machine learning algorithms to detect early-stage crop anomalies. The system integrates data from satellite and UAV imagery, IoT sensors, and user-generated observations, providing real-time alerts to facilitate rapid interventions and improve crop yields while minimizing resource waste.

Specification

Description:FIELD OF THE INVENTION
This invention relates to the field of precision agriculture, specifically systems and methods for large-area crop monitoring and the early detection of crop anomalies using real-time temporal analysis of multi-source data.
BACKGROUND OF THE INVENTION
Traditional methods for monitoring crop health across large agricultural areas, such as manual inspections or periodic satellite imagery, suffer from significant limitations. These methods are often time-consuming, labor-intensive, and provide only infrequent, incomplete data, making it difficult to detect early-stage problems such as disease outbreaks, nutrient deficiencies, or pest infestations. Delays in detection can lead to significant crop losses and increased resource waste.
Existing technologies, including remote sensing (satellite and UAV imagery), Internet of Things (IoT) sensors, and machine learning algorithms, offer improved capabilities for crop monitoring. However, many current systems lack the ability to provide real-time, continuous analysis of data from diverse sources, hindering the timely detection of subtle changes indicative of early-stage issues. There is a lack of effective integration of data from various scales (field-level IoT sensors, UAVs, satellite data) in real time.
Deep learning models, while effective for analyzing spatial patterns in crop images, often struggle with the temporal aspect. Real-time processing of large volumes of data from multiple sources presents significant computational challenges, leading to delays in detecting critical issues. The need to process large datasets and identify subtle changes over time adds significant complexity. Furthermore, the heterogeneous nature of data from different sources requires advanced data fusion and analysis techniques.
Therefore, a need exists for a robust, scalable, and efficient system capable of providing real-time, continuous monitoring and early detection of crop anomalies across large areas. This system should integrate data from multiple sources (satellite, UAV, IoT sensors), leverage advanced data fusion and temporal analysis techniques, and provide timely alerts to facilitate proactive intervention.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This invention discloses a real-time temporal analysis system for large-area crop monitoring that uses a multi-sensor data fusion approach and advanced machine learning algorithms to detect early-stage crop anomalies. The system integrates data from satellite and UAV imagery, IoT sensors, and user-generated observations, processing this information continuously to identify subtle changes indicative of developing problems. The system provides real-time alerts to facilitate rapid interventions, improving crop yields and reducing resource waste.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: BLOCK DIAGRAM SHOWING THE ARCHITECTURE OF THE REAL-TIME TEMPORAL ANALYSIS SYSTEM, ILLUSTRATING THE INTEGRATION OF DATA FROM MULTIPLE SOURCES (SATELLITE/UAV IMAGERY, IOT SENSORS, USER INPUT).
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The present invention comprises a real-time temporal analysis system for early detection of crop anomalies in large-scale agricultural monitoring. The system integrates data from multiple sources:
1. Remote Sensing: Satellite and UAV (Unmanned Aerial Vehicle) imagery provides large-area, multispectral and hyperspectral data on crop health, growth stages, and environmental stressors.
2. IoT Sensors: Ground-based sensors (soil moisture, temperature, etc.) collect high-frequency, localized data on microclimatic conditions and soil properties.
3. User-Generated Data: Farmers can input observations and management actions (irrigation, fertilization) via mobile applications, enhancing the data's richness and accuracy.
This multi-source data is fused using advanced algorithms, and temporal analysis (e.g., using LSTMs or other time-series models) identifies subtle patterns indicative of early-stage problems. Anomaly detection algorithms compare current data against historical trends and generate alerts based on pre-defined thresholds. A user-friendly interface provides visualizations of this data, allowing timely intervention.
The system incorporates several novel features: (1) Real-time multi-resolution data fusion from various sources; (2) Advanced machine learning algorithms (LSTMs, CNNs, etc.) for temporal analysis; (3) Predictive analytics for forecasting potential issues; (4) A user-friendly interface for visualizing data and providing actionable insights; (5) Scalability to handle large datasets and diverse environmental conditions.
, Claims:1. A system for real-time crop monitoring comprising a data acquisition module for gathering multi-source data, a data processing module for analyzing said data using temporal analysis techniques, and an alert generation module for providing notifications of detected anomalies.
2. The system, as claimed in Claim 1, wherein the data acquisition module gathers data from satellite and UAV imagery, IoT sensors, and user-generated observations.
3. The system, as claimed in Claim 2, wherein the data processing module employs machine learning algorithms (LSTMs, CNNs, etc.) for anomaly detection.
4. The system, as claimed in Claim 3, wherein the alert generation module provides real-time alerts via a user interface and mobile applications.
5. A method for early detection of crop anomalies comprising acquiring multi-source data, fusing said data, performing temporal analysis using machine learning algorithms, and generating alerts based on detected anomalies.
6. The method, as claimed in Claim 5, further comprising providing predictive analysis of potential future crop health issues.
7. A system for optimizing resource usage in agriculture by providing early detection of crop issues and facilitating timely intervention.
8. A system for enhancing agricultural productivity and sustainability through real-time monitoring and data-driven decision-making.
9. A method for improving the efficiency of agricultural operations through the integration of multiple data sources, advanced data processing, and real-time alerts.

10. Use of a multispectral remote sensing system in conjunction with a real-time temporal analysis system for early detection of crop anomalies.

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