Oestrus Detection Baseline Correction for Cattle Activity
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Solution Overview
Problem
Existing oestrus detection methods for dairy cattle are prone to false positives and false negatives due to varying activity patterns caused by milking robots, feeding robots, and environmental disturbances in free grazing systems, which complicates reliable reproduction management.
Innovation Solution
A method that compares current activity levels to baseline levels, correcting for travelling movements such as visits to milking or feeding machines, and herd movements to generate a more accurate oestrus attention signal, using activity data from devices like pedometers and animal identification systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If activity-based oestrus detection methods are used to monitor cattle, then oestrus detection capability is provided, but false positives and false negatives increase due to varying activity patterns from milking robots, feeding robots, and environmental disturbances
Solution Approach 1:
The patent extracts and removes the confounding factor of travelling movements from the activity data. By detecting when an animal visits milking or feeding machines and subtracting this travel-related activity from the total activity count, the system isolates the genuine oestrus-related activity increases, thereby improving measurement precision without losing detection capability
Solution Approach 2:
The system uses feedback from multiple data sources (activity data, location data, machine visit data) to continuously refine oestrus detection. By comparing current activity levels against baseline levels and adjusting for known non-oestrus activities, the system dynamically adapts to each animal's normal patterns, improving reliability while maintaining precision
2Reliability
If baseline activity comparison is used to detect oestrus, then detection capability is provided, but reliability decreases when animals exhibit varying activity patterns due to milking robots and free grazing systems
Solution Approach 1:
The patent implements dynamic baseline adjustment by continuously updating the expected activity pattern for each animal based on their individual behavior. Instead of using a fixed baseline, the system adapts to each animal's varying activity patterns caused by milking robots and free grazing, allowing reliable detection even when patterns change due to environmental factors or unexpected events
Solution Approach 2:
The system introduces an intermediary correction step that acts as a mediator between raw activity data and oestrus detection. By detecting and removing travelling movements as an intermediary process, the system reconciles the conflict between individual animal variability and reliable detection, allowing the baseline comparison to work effectively despite pattern variations
3Measurement precision
If activity data from multiple sources is collected to improve detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional processing device that handles multiple data types (activity data, location data, machine visit data) through a single integrated system. This universal approach consolidates what would otherwise require separate systems, improving measurement precision while minimizing the increase in device complexity by using one device to perform multiple functions
Data Source
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AI summary
The invention relates to a method for monitoring oestrus of a cattle animal, in particular a cow. The method comprises: a) collecting activity data of the animal, b) computing a current activity level based on the activity data, c) deciding whether to generate an oestrus attention signal by comparing at least one current activity level to a corresponding baseline activity level. Action b) further comprises b1) detecting travelling movements of the animal and b2) correcting the baseline activity level for detected travelling movements.