Rotary Milking Platform Animal Identification Using RFID Image Cross-Check
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Solution Overview
Problem
Existing animal identification systems in milking parlours, such as RFID systems, often fail to accurately determine the identity of animals in stalls, leading to incorrect feeding and milk yield monitoring.
Innovation Solution
A method and apparatus using image capturing and deep learning models to compare captured animal images with stored reference images or feature vectors, ensuring accurate animal identification by detecting the best match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If RFID animal identification systems are used, then animal identification can be performed, but the accuracy of determining animal identity in stalls is not always correct
Solution Approach 1:
The patent replaces RFID identification systems with an image-based recognition system. Instead of using radio frequency tags and readers, the system captures images of animals in stalls and uses image processing algorithms to identify them. This substitution addresses the accuracy issues of RFID systems by directly observing and analyzing the animals' visual characteristics, thereby improving both measurement precision and reliability of animal identification.
2Measurement precision
If image capturing and comparison methods are used, then animal identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the animal identification process into distinct segments: capturing images of animals, extracting feature vectors from the images, comparing the extracted features with stored reference data, and determining the animal's identity. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while maintaining high accuracy in animal identification.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between the captured images and the identification decision. Instead of directly comparing complex images, the system extracts simplified feature vectors that capture essential animal characteristics. This intermediary approach reduces computational complexity and makes the identification process more efficient while preserving accuracy.
3Measurement precision
If multiple images of each animal are captured and compared, then identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by capturing multiple images of each animal during its first visit and storing these images as reference data. This preliminary image collection establishes a baseline for future identifications, allowing rapid comparison during subsequent visits without requiring extensive processing time. The reference images are prepared in advance, enabling quick matching when animals return to the facility.
Solution Approach 2:
The patent captures and stores multiple images of each animal (excessive action) during initial visits, but only performs partial comparisons (comparing current images against stored references) during subsequent visits. This approach ensures high accuracy by having comprehensive reference data while reducing processing time during routine identifications by leveraging the pre-existing image library rather than capturing and processing images in real-time.
Data Source
AI summary
A rotary milking platform (3) comprises a plurality of stalls (5) and an RFID animal identifying system (9) for identifying animals entering the stalls (5) of the platform (3). A microprocessor (14) reads signals from an image capturing device (15) and computes a feature vector from the captured image of each animal. A plurality of reference feature vectors comprising respective matrices of metrics already derived from images of the respective animals captured by the image capturing device (15) are stored and cross-referenced with the identity of the respective animals. The microprocessor (14) compares computed feature vectors of each animal with the stored reference feature vectors until a best match has been determined with one of the reference feature vectors. The identity of the animal of that matching reference feature vector is then determined as the identity of the animal of that computed feature vector. The determined identity of the animal in the relevant stall (5) is compared with the identity of the animal determined for that stall (5) by the RFID system (9). On a favourable comparison the identity of the animal determined from the captured image of that animal is confirmed as the identity of the animal. In the event of a conflict between the two identities being determined, a conflict alert signal is produced.