UHF RFID Tag Interrogation for Bulk Livestock Data Collection
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Solution Overview
Problem
Current livestock tracking technologies using low-frequency RFID scanners are limited by short scanning distances and inability to simultaneously read multiple tags, hindering data collection and analysis over large geographical areas, which impedes advanced data analytics and predictive analytics in livestock management.
Innovation Solution
The implementation of ultra-high frequency (UHF) RFID tag interrogation systems that enable simultaneous reading of multiple tags across wide areas, combined with machine learning and artificial intelligence for data analytics, allowing for the collection and analysis of livestock data across multiple regions, farms, and enclosures, and the integration of additional data sources for predictive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If low-frequency RFID scanning is used, then data collection from individual tags is possible, but scanning distance is limited to centimeters and only one tag can be scanned at a time
Solution Approach 1:
The patent changes the operating frequency parameter from low-frequency to ultra-high-frequency RFID, which fundamentally alters the electromagnetic wave properties. This enables longer scanning distances (from centimeters to meters) and simultaneous interrogation of multiple tags, directly resolving the contradiction between reading accuracy and data collection efficiency
Solution Approach 2:
The system transitions from static, sequential tag scanning to dynamic, parallel multi-tag interrogation. The UHF RFID system can dynamically scan multiple tags simultaneously across wide areas, enabling real-time data collection from entire herds rather than individual animals, thus dramatically improving productivity while maintaining measurement precision
2Loss of information
If low-frequency RFID scanning is used, then individual tag data can be obtained, but data collection over large geographical areas is severely limited
Solution Approach 1:
By changing the frequency parameter to ultra-high-frequency, the electromagnetic wave propagation characteristics are improved, enabling signals to travel much longer distances. This allows a single scanner to cover large geographical areas such as entire feedlots or pastures, eliminating the need for multiple scanners and ensuring complete data collection across wide areas without information loss
3Productivity
If UHF-band RFID interrogation is implemented, then simultaneous reading of multiple tags across wide areas is enabled, but system complexity increases
Solution Approach 1:
The UHF RFID scanner is designed as a universal device that can simultaneously perform multiple functions: reading multiple tags across wide areas, collecting data from entire herds, and integrating with existing livestock management systems. This multi-functionality consolidates what would otherwise require multiple separate systems, managing complexity while maximizing productivity gains
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and range of data collection, enabling advanced data analytics and predictive capabilities for animal health, growth, and resource allocation, improving livestock management and sustainability.
Implementation Method 1
utilizing ultra-high frequency electromagnetic radiation in the UHF-band for simultaneously and rapidly bulk reading a large number of RFID tags
Data Source
AI summary
An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.