Algorithm for detecting the animal entry into the group milking plant
DOI:
https://doi.org/10.31558/2786-9482.2023.1.2Keywords:
information system, livestock farm, optimal filtering, identification, animal entry.Abstract
Based on the results of previous studies, it is known that in the case of errors in the radio frequency identification of animals during movement, when they enter the group milking plant, the information about the indicators of the technological process of milking, which concerns all animals in the group, is under high risk of lost. This circumstance is because the number of the milking machines of the group milking plant strictly corresponds to the number of animals in the queue for the plant. When a radio frequency identification error occurs, information about the fact of the animal's entry is lost, as a result, the server of the information system receives incorrect information about the correspondence of the animal's numbers in the herd to the numbers of the milking machines of the group milking plant. This leads to the fact that the results of measuring zootechnical parameters of animals and parameters of the technological process of milking are obtained with a false correspondence to herd numbers of animals. To prevent such information losses at group milking plants some means of counting the animals in the stream are used. Therefore, an important factor for obtaining reliable information about the parameters of the milk production technological process at group milking plants is the accurate counting of the animals during their movement to the milking machines. Existing means of counting animals are based on the interruption of the flow of infrared radiation by animals during movement and do not always ensure their accurate counting. To detect the errors in the radio frequency identification of moving animals on group milking plants, an algorithm based on optimal linear filtering of the output signal of the object presence sensor and a means of detecting the entry of animals to count them in the stream is proposed. As a result of the implementation of this algorithm, an increase in the accuracy of animal counting is ensured. The proposed algorithm detects more errors of radio frequency identification that leads to an increase in the reliability of the obtained information relative to the parameters of cow's milk production technological process at group milking plants.
References
RFID journal LLC. URL: http: // www.rfidjournal.com
Saha, H. N., Chakraborty, S., & Roy, R. (2021). Integration of RFID and sensors in agriculture using IOT. In AI, edge and IoT-based smart agriculture (pp. 361–372). Elsevier. DOI: 10.1016/B978-0-12-823694-9.00004-9.
Ranches, J., De Oliveira, R. A., Vedovatto, M., Palmer, E. A., Moriel, P., & Arthington, J. D. (2021). Use of radio-frequency identification technology to assess the frequency of cattle visits to mineral feeders. Tropical Animal Health and Production, 53(3). DOI: 10.1007/s11250-021-02784-2.
Achour, B., Belkadi, M., Saddaoui, R., Filali, I., Aoudjit, R., & Laghrouche, M. (2022). High-accuracy and energy-efficient wearable device for dairy cows’ localization and activity detection using low-cost IMU/RFID sensors. Microsystem Technologies, 28(5), 1241–1251. DOI: 10.1007/s00542-022-05288-7.
Noinan, K., Wicha, S., & Chaisricharoen, R. (2022). The IoT-based weighing system for growth monitoring and evaluation of fattening process in beef cattle farm. In 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022 (pp. 384–388). IEEE. DOI: 10.1109/ECTIDAMTNCON53731.2022.9720346.
Ding, X., Chen, L., & Gong, Y. (2019). An application of information collection method based on RFID and WSN technology in cow breeding. In Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019, (pp. 2663–2666). IEEE. DOI: 10.1109/IAEAC47372.2019.8998074.
Кучерук, В. Ю., Паламарчук Є. А., Кулаков П. І. (2014). Підвищення достовірності ідентифікації тварин у інформаційно-вимірювальних системах контролю зоотехнічних параметрів. Методи та прилади контролю якості, 2(33), 115–122.
Кулаков, П. І. (2015). Елементи теорії вимірювального контролю параметрів біотехнічної системи доїння. Вінниця, Вінницький національний технічний університет, 220 с.
Lancaster, P., Gyawali, P., Mavrogiannis, C., Srinivasa, S. S., & Smith, J. R. (2022). Optical proximity sensing for pose estimation during in-hand manipulation. IEEE International Conference on Intelligent Robots and Systems (Vol. 2022 – October, pp. 11818–11825). DOI: 10.1109/IROS47612.2022.9981692.
Polikarpus, A., Grasso, F., Pacelli, C., Napolitano, F., & De Rosa, G. (2014). Milking behaviour of buffalo cows: Entrance order and side preference in the milking parlour. Journal of Dairy Research, 81(1), 24–29. DOI: 10.1017/S0022029913000587.
Shepley, E., Lensink, J., & Vasseur, E. (2020). Cow in motion: A review of the impact of housing systems on movement opportunity of dairy cows and implications on locomotor activity. Applied Animal Behaviour Science, 230. DOI: 10.1016/j.applanim.2020.105026.
Su, L., Zhang, Y., Wang, J., Yin, Y., Zong, Z., & Gong, C. (2020). Segmentation method of dairy cattle gait based on improved dynamic time warping algorithm. Nongye Jixie Xuebao Transactions of the Chinese Society for Agricultural Machinery, 51(7), 52–59. DOI: 10.6041/j.issn.1000-1298.2020.07.007.
ПАТ “Брацлав” 01/05/2023. URL: https://www.bratslav.com/
Pallar LTD Co. & Musson Co. Корпоративний сайт компаній “Паллар ЛТД” та “Муссон”. 01/05/2023. URL: www.pallar.com.ua
