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.

A method for monitoring disease across agricultural areas of interest is provided comprising displaying at least one virtual zone corresponding to an agricultural geographic area of interest on a map in an application on a first device, and receiving an alert message when the first device is in proximity to a virtual zone. The at least one virtual zone is defined by at least one geofence. Each virtual zone is associated with a level of risk that indicates a likelihood of an outbreak of a disease detrimental to agriculture. Each virtual zone is configured to receive access notification information from each geofence when tracked devices enter an area defined by that geofence. The access information includes the level of risk associated with other virtual zones from which the tracked devices came. The alert message indicates if the first device should enter that virtual zone.

Methods to conserve a sensor's power include training a model to predict a condition of an animal with a training data set, wherein the training data set comprises known outcomes associated with behavioral data and health data for a plurality of animals; sensing, with a sensor worn by a monitored animal, behavioral data and health data of the monitored animal; inputting the behavioral data and health data of the monitored animal into the model; predicting a condition of the monitored animal with the model based on the inputted behavioral data and health data; and configuring a parameter of the sensor associated with the monitored animal based on the predicted condition.

A three-dimensional accelerometer in an animal tag registers a first set of acceleration parameters expressing a respective acceleration of the tag along each of three independent spatial axes. A processor in the tag derives a respective estimated gravity-related component in each parameter in the first set, and compensates for the respective estimated gravity-related components in the first set to obtain a second set of acceleration parameters representing respective accelerations of the animal tag along each of three independent spatial axes each in which the parameter is balanced around a base level with no influence of gravitation. The processor determines behavior-related data of rise-up and/or lie-down movements of an animal carrying the animal tag based on deviations in a single parameter in the second set of acceleration parameters relative to the base level.

A method for monitoring disease across agricultural areas of interest is provided comprising displaying at least one virtual zone corresponding to an agricultural geographic area of interest on a map in an application on a first device, and receiving an alert message when the first device is in proximity to a virtual zone. The at least one virtual zone is defined by at least one geofence. Each virtual zone is associated with a level of risk that indicates a likelihood of an outbreak of a disease detrimental to agriculture. Each virtual zone is configured to receive access notification information from each geofence when tracked devices enter an area defined by that geofence. The access information includes the level of risk associated with other virtual zones from which the tracked devices came. The alert message indicates if the first device should enter that virtual zone.

A method includes training, by at least one processor, an artificial intelligence model using a library of data associated with poultry, receiving, by the at least one processor, realtime information associated with a plurality of poultry from at least one imaging device at a particular location, determining, by the at least one processor, at least one benchmark based on the realtime information associated with the plurality of poultry from the at least one imaging device and using the artificial intelligence model, and generating and sending, by the at least one processor, an alert in realtime to a client computing device based on the realtime information associated with the plurality of poultry.

A system and method are disclosed for tracking animals, which may include production animals as well as pets. The system and method may comprise a tag system that is attached to an animal that generally has at least a near-field-communication (NFC) tag. The tag system comprises at least an NFC tag and a radio-frequency identification (RFID) tag. For NFC tags, such tags may be read with a portable computing device, such as a mobile telephone running NFC reader application software. The phone may communicate with a communications network and ultimately a computer server in order to relay information received from a respective electronic tag. The electronic tags may be fastened, embedded, or ingested by an animal. The electronic tags may be part of a mechanical coupling. Each mechanical coupling may comprise a different structure depending on the whether the tags are fastened to, embedded in, or ingested by the animal.

A method is described that comprises receiving a filtered signal corresponding to an acoustic event, applying one or more tests to the filtered signal in a sequence, wherein each test of the one or more tests assesses the filtered signal for the presence of one or more characteristics, the determining the presence of the one or more characteristics comprising a failure to identify the acoustic event as a first event, terminating the applying the one or more tests when a test determines the presence of the one or more characteristics, and identifying the acoustic event as the first event upon an occurrence of completing all tests of the one or more tests, the completing all tests including each test in the sequence determining the absence of the one or more characteristics.

An apparatus for monitoring a living target has a lock, a tamper-evidencing mechanism, and a mechanism for monitoring a GPS location of the apparatus. The apparatus has a mechanism for determining the apparatus is within at least one of a first threshold distance from a second apparatus or a first threshold distance from a pre-set GPS location, and a mechanism for warning the living target. The apparatus has a mechanism for determining the apparatus is in a banned location. The apparatus has a mechanism for emitting a debilitating sound and a mechanism for visually marking the living target. The apparatus has a communication mechanism for communicating a GPS location of the living target to at least the second apparatus and a third apparatus responsive to the determining the apparatus is in the banned location.

This device and process is a Special Search Dog Pack and Process for Faster Dog Aided Search, Dog Only Search and Rescue and other Dog Aided Uses for enabling Faster, Dog-Only Search. Particularly this process uses a search pack device that contains specific rescue contents used for and related to a faster search method. Here, the search dog has an attached pack with a tracking device and rescue contents that can be carried directly to a victim without the need of an attending person. Once found the victim can then communicate back to a search party and/or the search party can quickly and accurately locate the victim via the cell phone or GPS device for a precise and faster rescue.