Geotagged Agricultural Data Packaging for Tamper-Evident Verification
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Solution Overview
Problem
The collection and verification of agricultural data for determining eligibility for incentives or insurance claims is challenging due to the vast amount of image data captured by sensors, potential inaccuracies in image capture, and the risk of data tampering, which complicates the determination of field conditions.
Innovation Solution
Agricultural data is collected using vision sensors integrated with location sensors on farming equipment, geotagged with location and timestamp data, and packaged with authentication methods such as digital certificates, blockchain, or encryption to ensure data integrity and authenticity, allowing verification by a third-party service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image sensors are attached to farming equipment to capture field data, then the burden of data collection is reduced, but enormous amounts of data (terabytes or petabytes) are generated that consume significant computing resources for storage and review
Solution Approach 1:
The patent extracts only the essential verification elements from the full image dataset. Instead of processing all captured images, the system extracts geolocation metadata and generates cryptographic hashes of representative images, significantly reducing data volume while maintaining verification capability.
Solution Approach 2:
The system performs preliminary processing at the data collection stage by embedding geolocation information into image metadata and generating cryptographic hashes before transmission. This preliminary action reduces the computational burden on verification systems downstream.
2Loss of information
If image data is captured for field verification, then field conditions can be documented, but the images may not accurately reflect the actual field conditions or may be altered, resulting in incorrect analysis
Solution Approach 1:
The patent implements a feedback mechanism where geolocation data from GPS sensors is continuously compared with the spatial metadata embedded in images. This cross-validation feedback loop ensures that images are properly geotagged and have not been tampered with, maintaining both accuracy and reliability.
Solution Approach 2:
The system applies cryptographic hashing and digital signing to image data before transmission or storage. This preliminary cryptographic protection acts as a cushion against potential data alteration, ensuring that any tampering would be detectable through hash verification.
3Measurement precision
If all captured image data is reviewed to verify field conditions, then verification accuracy is improved, but the process consumes significant computing resources and time
Solution Approach 1:
Instead of reviewing all captured images, the system applies partial verification using cryptographic hashes and geolocation validation on a selective basis. Representative images or those containing verification-critical information are fully reviewed, while others are verified through metadata validation alone, reducing computing resource consumption while maintaining sufficient verification accuracy.
Data Source
AI summary
System and methods for generating a verifiable package of agricultural data to determine whether one or more stewardship criteria have been met. Sensor data from one or more sensors, including at least one vision sensor, can be retrieved with corresponding location data. Agricultural data can be generated that includes the sensor data or inferences determined from the sensor data and the agricultural data can be packaged with authentication data to generate a verifiable package. The authentication data can be utilized to determine the authenticity of the agricultural data and compliance with one or more stewardship criteria can be determined based on the agricultural data.