Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and system is provided in an adaptive framework for modeling livestock growth. The adaptive framework processes input data relative to livestock growth in an ensemble of one or more models and an artificial intelligence layer configured to select the most appropriate or primary model to optimize, predict, and recommend livestock feed operations based upon environmental, physiological, location and time variables within such input data. The adaptive framework also optimizes workflow by pen and by producer, based upon historical performance, gender and breed and the management practices of the producer.

Abstract: A method and system is provided in a framework and workflow for assisting in livestock feeding operations by tracking and weighing ingredients from an electronic scale associated with feed mixing equipment. The framework and workflow captures, transfers, stores and processes parameters for cattle feeder ration weight data collection. The method and system include initializing a collection of weight data related to components of a feed ration for a mixing of feed for livestock, capturing a scale identifier from a scale interface on mixing equipment, and capturing weight information from the scale interface as the components of a feed ration are loaded into the mixing equipment. Such information is broadcasted over a wireless radio communication connection, such as via a Bluetooth® device, to a mobile application, and displayed during feed mixing operation.