CowXNet: An automated cow estrus detection system
| dc.contributor.author | Lodkaew, Thanawat | |
| dc.contributor.author | Pasupa, Kitsuchart | |
| dc.contributor.author | Loo, Chu Kiong | |
| dc.date.accessioned | 2026-08-06T10:40:39Z | |
| dc.date.available | 2026-08-06T10:40:39Z | |
| dc.date.issued | 2023-01-01 | |
| dc.description.abstract | Estrus detection is essential for dairy farms to take cows for artificial insemination promptly. Conventional approaches for detecting estrus cows use electronic devices attached to cows to gather data for software analysis. However, electronic devices can be costly and make a cow moody and uncomfortable while moving. In a common approach, observers detect estrus cows by observing their behaviors. However, continuous observation can easily lead to errors due to the observer's fatigue. Therefore, we designed CowXNet, an automatic estrus detection system for cows, to assist farmers to detect estrus cows. CowXNet requires only a camera attached in a pen and a computer to analyze recorded videos. CowXNet analyzes the estrus behaviors of each cow in a pen and helps farmers to identify estrus cows. To develop and evaluate CowXNet efficiently and effectively, we collected data from Chokchai Farm, the biggest dairy farm in Asia (14.65483<sup>∘</sup>N, 101.34853<sup>∘</sup>E). CowXNet has four modules: (i) cow detection uses YOLOv4 to detect cows in recorded videos; (ii) body part detection uses a convolutional neural network to estimate locations of body parts of detected cows; (iii) estrus behavior detection uses body part coordinates to extract a set of discriminative features, and a classification algorithm to detect estrus behaviors, and (iv) behavior analysis module displays estrus behavior for analysis purposes. We evaluated CowXNet for two instances: module-independent evaluation and end-to-end framework evaluation. Overall, CowXNet was promising; it correctly detected estrus behavior interval of cows 83% of cases. | |
| dc.identifier.citation | Expert Systems with Applications, 211, 2023 | |
| dc.identifier.doi | 10.1016/j.eswa.2022.118550 | |
| dc.identifier.issn | 09574174 | |
| dc.identifier.other | 2-s2.0-85137164453 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/14202 | |
| dc.source | Expert Systems with Applications | |
| dc.subject | Behavior analysis | |
| dc.subject | Cattle | |
| dc.subject | Deep learning | |
| dc.subject | Estrus detection | |
| dc.title | CowXNet: An automated cow estrus detection system | |
| dc.type | Article |