Pi, Y.; Mendes, E.; Tedeschi, L.; Tao, J.; Reddy, S.; Duffield, N. Technical Note: Using Computer Vision’s Pixel Segmentation to Detect Beef Cattle Feeding Behavior. Preprints2023, 2023070705. https://doi.org/10.20944/preprints202307.0705.v1
APA Style
Pi, Y., Mendes, E., Tedeschi, L., Tao, J., Reddy, S., & Duffield, N. (2023). Technical Note: Using Computer Vision’s Pixel Segmentation to Detect Beef Cattle Feeding Behavior. Preprints. https://doi.org/10.20944/preprints202307.0705.v1
Chicago/Turabian Style
Pi, Y., Siddhanth Reddy and Nick Duffield. 2023 "Technical Note: Using Computer Vision’s Pixel Segmentation to Detect Beef Cattle Feeding Behavior" Preprints. https://doi.org/10.20944/preprints202307.0705.v1
Abstract
There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.
Biology and Life Sciences, Animal Science, Veterinary Science and Zoology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
