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公开(公告)号：US20180350010A1
公开(公告)日：2018-12-06
申请号：US15996432
申请日：2018-06-02
CPC分类号： G06Q50/02 , A01K29/005 , G06N7/00 , G06N20/00 , G06N20/20 , G06Q10/0639
摘要： 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.
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公开(公告)号：US20210081835A1
公开(公告)日：2021-03-18
申请号：US16852826
申请日：2020-04-20
摘要： 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.
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公开(公告)号：US20230153693A1
公开(公告)日：2023-05-18
申请号：US18094873
申请日：2023-01-09
CPC分类号： G06N20/00 , A01K11/008 , A01K29/005 , G06K7/10366 , H04W4/029 , H04W4/80 , A01K5/02 , H04W4/70
摘要： 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.
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公开(公告)号：US20240013097A1
公开(公告)日：2024-01-11
申请号：US18370046
申请日：2023-09-19
CPC分类号： G06N20/00 , G06K7/10366 , A01K29/005 , A01K5/02 , H04W4/029 , H04W4/80 , A01K11/008 , G06N7/00
摘要： 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.
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公开(公告)号：US20210326764A1
公开(公告)日：2021-10-21
申请号：US17364510
申请日：2021-06-30
摘要： 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.
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