Generating consensus biomass estimates include providing a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model and providing a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model. The first biomass estimation model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second biomass estimation model. An aggregated biomass estimate is determined based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.

The invention relates to a device for tracking the consumption of foodstuffs by an animal, comprising: a front wall, a rear wall and side walls defining therebetween a chamber, and an opening formed in the front wall in order to allow an animal to gain access to the chamber; a receptacle in the chamber, defining a cavity for storing foodstuffs; a foodstuff retrieval element for retrieving foodstuffs discharged from the cavity and not consumed by the animal, which element extends substantially from the cavity as far as one of the walls; and a weighing system which is configured to determine the weight of foodstuffs that have been taken from the cavity and not retrieved by the retrieval element over a period of time.

An animal feeding system having a plurality of network connectable feeding stations, each with a base with a weight sensor for measuring a quantity of animal food placed in a food container, a processor, and network connection circuitry for connecting to a computer network. A server having a processor is coupled to a database to store pet information and use AI to analyze food and water consumption and recommend new foods or issue health alerts based on the consumption data. The server is configured for communication to feeding system bases and network connectable devices, such as smart phones, having executable food management software, wherein animal food intake information is transmitted to the server; and executable artificial intelligence loaded into and running on the server receives and processes data from the feeding stations and analyzes the data to make predictions and recommendations for foods individual animals prefer.

A bin monitoring system is useable with an elevated feed bin having a plurality of legs that support the bin above a pad so that each leg is connected to the pad through a load cell. The system includes an inner leg mount configured to be attached to a leg, the inner leg mount having an upper portion, and an outer bracket mounted on the load cell. The outer bracket receives the inner leg mount such that a bolt aperture in the outer bracket aligns with a bolt aperture in the upper portion of the inner leg mount and is secured with a threaded bolt. Rotation of the threaded bolt moves the inner leg mount toward the outer bracket to lift the leg so that the leg is supported by the load cell.

A feeding system for feeding individual animals of a herd feed, supplements, and combinations thereof. The feeding system includes one-to-many feed bin assemblies or feeding stations such that the system is able to dispense a specified mass of feed to specific animals at user-defined intervals. In this way, each animal of a herd is provided with optimal, individualized amounts of feed or feed material. Multiple feed bin assemblies in a single pasture, for example, can automatically form a network of feeders so that regardless of the specific feed bin assembly or feeding station within the feeding system that an animal visits, the specified amount and type of feed or feed material is delivered. Each feed bin assembly or feeding station can also be equipped to download, archive/store, and/or synthesize data into an individual animal record and/or to transmit data from additional onboard animal sensors such as activity and rumination sensors.

A system for providing nourishment to pets is provided. The system includes a base component and an electrically operated platform including a scale configured to move along one or more vertical shafts which brings one or more reservoirs positioned on the platform to various levels of height. The system further includes an affixed mount configured to retain a computing device which controls components of the system, particularly the movement of the platform along the one or more vertical shafts and the taking and storage of weight data of the nourishment.

A feed supply amount control method for sows and an apparatus therefor are disclosed. A feed supply control device recognizes morphological data of a sow through a sensor, determines a weight management information including a body condition score of the sow based on the morphological data, and controls the amount of feed to be supplied to the sow according to the weight management information.

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.

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.

This invention general relates to animal feeders, an animal feeding monitor and a method of training an animal to use a feeder. An animal feeder may have a housing comprising an aperture for a food bowl (8) and having a door for covering the aperture, the feeder comprising a door mechanism to control movement of the door over the aperture, wherein the feeder comprises a sensor to detect when an animal has moved away from the aperture, and the feeder is configured to be operable in a training mode wherein the door mechanism is operable to control the door to cover a portion of the aperture in response to a said detection and to respond to one or more later said detections by controlling the door to cover a portion of the aperture larger than the preceding said portion.