Abstract: Systems and methods are described for selecting a sound type of interest from a first (e.g., master/global) machine learning library comprising information derived from reference audio stream data acquired from a plurality of farm animal operation reference sound monitoring events, including from a first farm animal operation monitoring event of a first farm animal operation, wherein the sound type of interest is associated with a condition state of interest of a first collection of farm animals. Further, information associated with the selected sound type of interest can be included a second machine learning library, wherein the second machine learning library is operational on an edge computing device located in proximity to a second farm animal operation.

Abstract: Systems and methods are described for selecting a sound type of interest from a first (e.g., master/global) machine learning library comprising information derived from reference audio stream data acquired from a plurality of farm animal operation reference sound monitoring events, including from a first farm animal operation monitoring event of a first farm animal operation, wherein the sound type of interest is associated with a condition state of interest of a first collection of farm animals. Further, information associated with the selected sound type of interest can be included a second machine learning library, wherein the second machine learning library is operational on an edge computing device located in proximity to a second farm animal operation.

Abstract: Systems and methods are described for selecting a sound type of interest from a first (e.g., master/global) machine learning library comprising information derived from reference audio stream data acquired from a plurality of farm animal operation reference sound monitoring events, including from a first farm animal operation monitoring event of a first farm animal operation, wherein the sound type of interest is associated with a condition state of interest of a first collection of farm animals. Further, information associated with the selected sound type of interest can be included a second machine learning library, wherein the second machine learning library is operational on an edge computing device located in proximity to a second farm animal operation.

Abstract: Systems and methods are described for selecting a sound type of interest from a first (e.g., master/global) machine learning library comprising information derived from reference audio stream data acquired from a plurality of farm animal operation reference sound monitoring events, including from a first farm animal operation monitoring event of a first farm animal operation, wherein the sound type of interest is associated with a condition state of interest of a first collection of farm animals. Further, information associated with the selected sound type of interest can be included a second machine learning library, wherein the second machine learning library is operational on an edge computing device located in proximity to a second farm animal operation.

Abstract: Processor-based methods and systems and computer programs for remote animal health monitoring receive and process data relating to animal health parameters obtained from a plurality of different types of sensors. Baseline data signatures are determined from the data obtained for individual animals, and as data is subsequently collected is compared to the current data signature to assess animal health. Deviations from the signatures may serve to predictively diagnose certain conditions and facilitate medical intervention before adverse physical symptoms are manifested. Informational data and analytics are made available to animal owners, health care providers, and other interested persons.

Abstract: Methods and systems for collecting, managing and reporting feedlot data and feed additive consumption data are provided. More particularly, methods of determining effects of feed additives on livestock and methods of determining a number of doses of animal health product provided to livestock are disclosed. One method of determining effects of a feed additive on livestock includes steps: determining feed additive consumption by correlating feed consumption data to additive levels contained in feed; and matching feed additive consumption to a live performance record, a harvest performance record, and/or an animal health performance record. In another embodiment, a method of determining a number of doses of animal health product provided to livestock includes steps: determining a total quantity of animal health product provided to the livestock; determining a dose amount for the animal health product; and dividing the total quantity of animal health product provided by the dose amount.

Abstract: Methods and systems for collecting, managing and reporting feedlot data and feed additive consumption data are provided. More particularly, methods of determining effects of feed additives on livestock and methods of determining a number of doses of animal health product provided to livestock are disclosed. One method of determining effects of a feed additive on livestock includes steps: determining feed additive consumption by correlating feed consumption data to additive levels contained in feed; and matching feed additive consumption to a live performance record, a harvest performance record, and/or an animal health performance record. In another embodiment, a method of determining a number of doses of animal health product provided to livestock includes steps: determining a total quantity of animal health product provided to the livestock; determining a dose amount for the animal health product; and dividing the total quantity of animal health product provided by the dose amount.

Abstract: A method and system for collecting, managing and reporting feedlot cattle data and feed additive consumption data is provided. In particular, the present invention is directed to a method and system for collecting data including demographic data, animal health data, feedlot performance data, feed data, and carcass data from standard accounting systems, validating the data, normalizing the data using mapping and matching protocols, and reporting the data.