Remote Crop Performance Mapping via Satellite Imagery Analysis
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Solution Overview
Problem
Conventional methods for measuring crop field performance are limited by their inability to analyze entire fields in real-time, often requiring physical sampling and lacking the capability to monitor changes over time, leading to delayed identification of crop health anomalies and inefficient resource allocation.
Innovation Solution
A system and method that utilizes remote monitoring data, such as satellite images, to generate performance maps and summaries for geographic regions, enabling real-time analysis, identification of performance anomalies, and normalization of metrics based on crop types, allowing for precise crop management and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If physical sampling methods are used to measure crop field performance, then measurement precision can be achieved, but the analysis is limited to small sampled areas and cannot cover entire fields in real-time
Solution Approach 1:
The patent replaces physical sampling methods with remote sensing technology (satellite imagery, aerial photography, drones) to capture crop field data. This substitution enables comprehensive coverage of entire fields while maintaining measurement precision through advanced image processing and analysis algorithms that extract vegetative performance values from remote sensing data.
Solution Approach 2:
The patent transitions from two-dimensional physical sampling to multi-dimensional remote sensing data analysis by processing satellite images, aerial photographs, and drone footage. This dimensional expansion allows simultaneous analysis of entire fields across multiple spectral bands and time points, achieving both comprehensive coverage and precise measurement.
2Speed
If conventional sampling methods are used, then measurement can be performed, but real-time monitoring capability is lost and identification of crop health anomalies is delayed
Solution Approach 1:
The patent implements continuous remote monitoring of crop fields by systematically capturing satellite imagery and aerial photographs at regular intervals throughout the growing season. This continuous data collection enables real-time detection of crop health anomalies, eliminating the time delays inherent in periodic physical sampling and allowing prompt intervention when problems are detected.
3Productivity
If remote monitoring data is used to generate performance maps, then real-time comprehensive analysis is enabled, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional integrated system that combines satellite imagery reception, aerial photography capture, drone operation, image processing, vegetative performance value calculation, and performance map generation. This universal system handles multiple functions through a unified platform, managing complexity by consolidating operations rather than using separate systems for each function.
Solution Approach 2:
The system performs automated processing of remote sensing data, including automatic generation of vegetative performance values and performance maps without requiring manual intervention. The system self-manages data collection, processing, analysis, and output generation, reducing operational complexity while maintaining high productivity in crop field analysis.
Data Source
AI summary
A method for measuring performance of a geographic region from an image including a set of image elements includes: receiving the image corresponding to a time unit, generating a geographic region performance map for the image, combining the geographic region performance map with a second geographic region performance map, and generating a geographic region performance summary map. Generating the geographic region performance map for the image can include mapping a set of image elements to a set of geographic sub-regions, generating a set of vegetative performance values for the set of image elements, mapping the set of image elements to a set of crop types, defining a subset of image elements corresponding to a subset of vegetative performance values, comparing vegetative performance values of the subset of vegetative performance values, and generating geographic region performance values for the subset of image elements.