Development of an Automated Cows In-Heat Detection and Monitoring System Using Image Recognition with GSM Based Notification System

Article Details

Nilo M. Arago , nan, nan
August C. Thio-Ac, , nan
Miguel C. Apostol, , nan
Irwin James E. De Guzman, , nan
Albert Eli D. Reyes, , nan
Kathleen G. Rodriguez, , nan
Roi Aldrin B. Toring, , nan

Journal: Journal of Computational Innovations and Engineering Applications
Volume 4 Issue 2 (Published: 2020-01-01)

Abstract

Precise estrus detection is a factor for the reproductive performance of cows. The primary sign of estrus is the standing heat wherein a cow stands still for a few seconds while mating with other cows. Visual monitoring is the most common method used for detection of estrus that requires farmers’ time and attention for high yield. In this study, an estrus detection using image recognition is used to detect the standing heat. The system is comprised of detection, identification, and notification system. Scale Invariant Feature Transform (SIFT) is responsible for the detection and identification of in-heat cows. Using SIFT, the images of cows were registered in the database, these images were used for detection and identification of cows and an algorithm for feature overlapping was created to detect the standing heat. When a standing heat is detected, it is recorded into the computer and simultaneously the Global System for Mobile Communications (GSM) module will send an alert message.

Keywords: estrus, in-heat cows, standing heat, image recognition, artificial insemination

DOI: https://www.dlsu.edu.ph/wp-content/uploads/pdf/research/journals/jciea/vol-4-2/2arago.pdf
  References:

1. Department of Science and Technology. (n.d.). Beef Cattle. Retrieved from Ruminants Information Network: http://www.pcaarrd.dost.gov.ph/home/ momentum/ruminants/index.php?option=com_co com_content&task=view&id=173 &itemid=216

2. Bureau of Agricultural Statistics. (2010-2015). Cattle Industry Performance Report. Quezon City: Philippine Statistics Authority. Retrieved from http://psa.gov.ph/content/cattle-industryperformance-report-1?page=1

3. O’Connor, M. L. (2016). Heat Detection and Timing of Insemination for Cattle. Retrieved from PennState Extension: http://extension.psu.edu/animals/dairy/ health/reproduction/insemination/ec402

4. Dalton, J. C. (2012, September 24). Strategies for Success in Heat Detection and Artificial Insemination. Retrieved from eXtension.org:http:// articles.extension.org/pages/65460/strategiesfor-success-in-heat-detection-and-artificialinsemination

5. Neves, R. (2011). Investigation of Automated Activity Monitoring Systems for Reproduction in Dairy Cattle. Journal of Dairy Science, 10- 15. Retrieved from http://www.hachaklait.org.il/ files/351204.pdf

6. Chen, C.-H., & Lin, H.-R. (2015). Estrus Detection for Dairy Cow Using ZigBee-Based Sensor Networks.

7. Anda, K. M., Delos Reyes, J., Dinglasan, R. M., Floresca, F.M., Mendooza, V. R., Pacundan, J. M., Ramirez, G. M., & Torres, A. K. (2014). Development of a Computer-Based Monitoring System for Detection and Identification of In-Heat Cows Using High-Definition Cameras

8. Yuang, C. J., Lin, Y., & Peng, S. (2017). Develop a video monitoring system for dairy estrus detection at night. IEEE-International Conference on Applied System Innovation, 1900-1903.

9. Tsai, D. M., & Huang, C. Y. (2014). A motion and image analysis method for automatic detection of estrus and mating behavior in cattle. Computers and Electronics in Agriculture, 25-31.

10. Face First (2018). What is Feature Recognition? Retrieved from https://www.facefirst.com/facerecognition-glossary/what-is-feature-recognition/

11. Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. Int. Journals in Computer Vision.

12. Ray, S. (2015). Essentials of Machine Learning Algorithms (with Python and R Codes). Retrieved from https://www.analyticsvidhya.com/ blog/2017/09/common-machine-learning-algorithm

  Cited by:
     None...