20240013097. LIVESTOCK AND FEEDLOT DATA COLLECTION AND PROCESSING USING UHF-BAND INTERROGATION OF RADIO FREQUENCY IDENTIFICATION TAGS FOR FEEDLOT ARRIVAL AND RISK ASSESSMENT simplified abstract (PERFORMANCE LIVESTOCK ANALYTICS, INC.)
Contents
- 1 LIVESTOCK AND FEEDLOT DATA COLLECTION AND PROCESSING USING UHF-BAND INTERROGATION OF RADIO FREQUENCY IDENTIFICATION TAGS FOR FEEDLOT ARRIVAL AND RISK ASSESSMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LIVESTOCK AND FEEDLOT DATA COLLECTION AND PROCESSING USING UHF-BAND INTERROGATION OF RADIO FREQUENCY IDENTIFICATION TAGS FOR FEEDLOT ARRIVAL AND RISK ASSESSMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Original Abstract Submitted
LIVESTOCK AND FEEDLOT DATA COLLECTION AND PROCESSING USING UHF-BAND INTERROGATION OF RADIO FREQUENCY IDENTIFICATION TAGS FOR FEEDLOT ARRIVAL AND RISK ASSESSMENT
Organization Name
PERFORMANCE LIVESTOCK ANALYTICS, INC.
Inventor(s)
DANE T. Kuper of ST. ANSGAR IA (US)
DUSTIN C. Balsley of OSAGE IA (US)
PAUL Gray of CEDAR FALLS IA (US)
WILLIAM JUSTIN Sexten of COLUMBIA MO (US)
LIVESTOCK AND FEEDLOT DATA COLLECTION AND PROCESSING USING UHF-BAND INTERROGATION OF RADIO FREQUENCY IDENTIFICATION TAGS FOR FEEDLOT ARRIVAL AND RISK ASSESSMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240013097 titled 'LIVESTOCK AND FEEDLOT DATA COLLECTION AND PROCESSING USING UHF-BAND INTERROGATION OF RADIO FREQUENCY IDENTIFICATION TAGS FOR FEEDLOT ARRIVAL AND RISK ASSESSMENT
Simplified Explanation
The abstract describes an agricultural data collection framework that tracks and manages livestock, analyzes animal conditions, and uses RFID tags for data collection. It incorporates artificial intelligence techniques to develop machine learning models for evaluating animal conditions and provides alerts, predictions, and processing schedules for intervention when needed.
- The framework tracks and manages livestock by collecting individual animal data using ultra-high frequency RFID tags.
- It analyzes animal conditions such as health, growth, nutrition, and behavior.
- It uses artificial intelligence techniques to develop machine learning models for statistical process controls around each animal.
- It determines normality at an individual animal basis or for a specific location.
- It generates alerts, predictions, and processing schedules for prioritizing and delivering resources when intervention is needed.
Potential applications of this technology:
- Livestock management: The framework can be used by farmers and ranchers to track and manage their livestock more effectively, ensuring optimal health and growth.
- Animal research: Researchers can use the framework to collect data on animal conditions for scientific studies and experiments.
- Veterinary medicine: Veterinarians can utilize the framework to monitor and analyze the health and behavior of individual animals, enabling early detection of diseases or abnormalities.
Problems solved by this technology:
- Data collection: The framework provides a systematic and efficient way to collect individual animal data across multiple geographical locations using RFID tags.
- Animal condition evaluation: By incorporating artificial intelligence techniques, the framework can analyze and evaluate animal conditions more accurately and objectively.
- Resource allocation: The framework generates alerts, predictions, and processing schedules, allowing for prioritization and efficient allocation of resources when intervention is needed.
Benefits of this technology:
- Improved livestock management: The framework enables farmers and ranchers to make informed decisions based on accurate and real-time data, leading to improved animal health and productivity.
- Early detection of issues: By analyzing animal conditions, the framework can detect health issues or abnormalities at an early stage, allowing for timely intervention and treatment.
- Resource optimization: The framework helps optimize the allocation of resources by providing targeted processing schedules, ensuring that resources are delivered when and where they are needed most.
Original Abstract Submitted
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.
