Animal Sensor Network for Grazing Intake Monitoring
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Solution Overview
Problem
Current methods are inadequate for accurately measuring individual pasture intake in livestock, hindering precise grazing efficiency assessments and livestock management, especially under varying environmental conditions.
Innovation Solution
A sensor network with multiple sensors mounted on animals to collect movement data, processed by a server that evaluates machine learning models to determine grazing behavior and calculate pasture intake values, providing real-time monitoring and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to measure pasture intake, then the system is simple, but measurement precision is insufficient
Solution Approach 1:
The system segments the measurement task into multiple components: acceleration sensors mounted on animals collect movement data, wireless transmission sends data to a server, and machine learning models process the data to determine grazing behavior. This segmentation enables precise individual animal monitoring while distributing system complexity across multiple manageable modules.
Solution Approach 2:
The patent replaces traditional mechanical or manual pasture intake measurement methods with an electronic sensor-based system. Acceleration sensors detect head movements and biting motions, and machine learning algorithms automatically analyze the data to estimate pasture intake, eliminating the need for direct mechanical measurement of each animal's consumption.
2Productivity
If individual animal monitoring is implemented, then productivity information is improved, but device complexity increases
Solution Approach 1:
The monitoring system is designed to be universally applicable across multiple animals simultaneously. The same sensor and processing architecture can monitor any number of grazing animals, providing individual productivity data for each animal while maintaining a standardized system design that avoids unnecessary complexity.
Solution Approach 2:
The system enables self-service monitoring where animals are passively tracked without requiring manual intervention. Sensors automatically collect data as animals graze, and the machine learning model autonomously processes the information to generate productivity metrics, reducing the need for human labor in data collection and analysis.
3Measurement precision
If machine learning models are used to analyze behavior, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously refines its behavior classification based on patterns recognized in the acceleration data. The model learns from accumulated data to improve its accuracy in distinguishing between grazing, resting, and other behaviors, thereby maintaining high measurement precision while managing information loss through iterative optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for accurate and efficient monitoring of pasture intake, enhancing livestock management and breeding selection by quantifying grazing behavior and environmental factors, leading to improved productivity and genetic merit.
Implementation Method 1
The multiple sensors may comprise acceleration sensors
Data Source
AI summary
This disclosure relates to a system for monitoring pasture intake by a grazing animal species. The system comprises a sensor spatially associated with a body part of the animal to generate movement data indicative of movement of the body part of the animal associated with multiple points in time and a processing server. The processing server comprises a data interface to receive the movement data and a processor that is configured to evaluate the received movement data to determine for each of the multiple points in time an indication of a behaviour of the animal at that point in time. Based on the indication the processor determines a time value indicative of a time the behaviour was shown by the animal, and determines a pasture intake value based on the time value.