Recognition of Cow Teats Using the 3D-ToF Camera When Milking in the “Herringbone” Milking Parlor

Dorokhov, Alexey; Кирсанов, В В; Pavkin, D. Yu.; Yurochka, S. S.; Vladimirov, F. E.

doi:10.1007/978-3-030-33585-4_13

Cited by 9 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…В результате нарушается усвояемость питательных веществ, изменяется нормофлора желудочно-кишечного тракта, снижаются его абсорбционные и барьерные функции [1]. ПАР может спровоцировать развитие других патологий, в том числе влияющих на репродуктивные функции животного [5,6]. Поскольку признаки ПАР малозаметные и часто проявляются не сразу после провоцирующего события, его диагностика затруднена [7].…”

Section: техника для животноводстваunclassified

Comparison of In-Rumen Systems for Monitoring the Physiological State of Cattle

2023

View full text Add to dashboard Cite

This paper explores a method for the early detection of subacute rumen acidosis through continuous monitoring of rumen content acidity using wireless pH sensors. The study emphasizes the importance of reliable device operation and measurement accuracy as primary requirements for such devices. (Research purpose) This study aims to evaluate the functionality and accuracy of the eBolus (USA) and BoviSan (Russia, FSBSI FSAC VIM) sensors throughout their declared service life of 150 days. (Materials and methods) Three dairy cows with a rumen fistula were administered two boluses from each manufacturer during a period of 154 days. In order to ensure accurate measurements, the boluses were activated and calibrated prior to use. On the first day of data collection, the activated and calibrated boluses were sequentially immersed in buffer solutions with a pH of 4 and pH of 7. The buffer solutions were maintained at a controlled temperature of 39 degrees Celsius for a minimum of 2 hours. At 14-day intervals, the boluses were extracted from the rumen and subjected to retesting in buffer solutions. (Results and discussion) For BoviSan, the confidence interval for the mean difference suggests a systematic negative pH bias (– 0.33 to – 0.25). The average lifespan of the eBolus was determined to be 82 days, with a range of 28 to 126 days. In contrast, BoviSan boluses consistently recorded pH levels throughout their announced lifespan. Statistical analysis of the data revealed a 95% confidence interval for the mean pH difference of the eBolus boluses (– 0.02 to 0.19), indicating no significant bias. Conversely, the confidence interval for the mean difference in pH for BoviSan suggested a consistent negative bias (– 0.33 to – 0.25). (Conclusions) In practical applications, it is essential to ensure enhanced reliability of eBolus boluses, while prioritizing higher measurement accuracy with BoviSan boluses.

show abstract

Section: техника для животноводстваunclassified

Comparison of In-Rumen Systems for Monitoring the Physiological State of Cattle

2023

View full text Add to dashboard Cite

show abstract

“…Finally, in [9], the authors use a 3D-ToF camera to collect both RGB images and a point cloud. They process the point cloud applying the k-nearest algorithm for segmentation, but such method cannot distinguish the udder from the teats, resulting in imprecise segmentation.…”

Section: B Automated Milking Literaturementioning

confidence: 99%

Teat Pose Estimation via RGBD Segmentation for Automated Milking

Borla,

Kuster,

Langenegger

et al. 2021

Preprint

View full text Add to dashboard Cite

We present initial results in the development of a novel robot using RGBD cameras, image segmentation, and a simple teat pose estimation algorithm for automated milking. We relate on the analysis of the accuracy of different commercial RGBD cameras in realistic conditions. Although preliminary, our initial implementation shows that 2D image segmentation combined with point cloud processing can achieve repeatable millimeter-scale precision in estimating (synthetic) teat tip positions and cup attachment approach. The solution is also applicable in a cloud robotics setup, with GPU-based segmentation executed on an edge device or cloud.

show abstract

“…In recent years, researchers have started to detect the position of cow teats from a visual perspective [4] to achieve higher milking efficiency [5] . Akhloufi M.A [6] development of a 3D vision system using a 3D-TOF camera and an RGBD camera for the detection of bovine udder position information; Aleksey Dorokhov [7] obtained an image point cloud using a 3D-TOF camera and segmented the point cloud using KNN nearest neighbor algorithm and Euclidean distance to achieve detection of cow teats; Pal [8] proposed a conceptual idea to combine TOF, thermography and RGBD imaging for teat identification; Ben Azouz [9] developed a vision system integrating optical stereo vision and thermal imaging, which was tested in various laboratory situations using a dummy thermal breast; Rastogi [10] applied the Haar feature extraction algorithm to milk cow teat detection, although there are differences in the lighting, color, position and size of the object, the teat can be detected from the cluttered background. With the development of artificial intelligence, some researchers also use the deep learning method to detect cow teats.…”

Section: Introductionmentioning

confidence: 99%

Research on tracking algorithm of milking robot arm based on improved SURF-KCF

Zheng

Weng

et al. 2023

Second International Conference on Digital Society and Intelligent Systems (DSInS 2022)

View full text Add to dashboard Cite

Aiming at the slow movement and small displacement of dairy cows in the pasture breeding process, but the target scale changes significantly, a SURF-KCF dairy cow teat tracking algorithm is proposed. The original KCF algorithm needs to locate the target before tracking, and the tracking frame cannot be adaptive with the scale of the target. The SURF feature detection is introduced to provide target features for the KCF algorithm to achieve automatic target matching; and the scale estimation strategy is used to achieve scale adaption of cow teat targets. The experimental results show that the proposed improved algorithm achieves cow teat target matching and localization as well as scale adaption of the target area, and long-term stable tracking. Finally, in the laboratory environment, the improved algorithm is applied to the three-degree-of-freedom robotic arm automatic milking robot to realize the cow teat positioning and tracking experiment. When the cow's teat is detected, the robotic arm can automatically move to the position of the cow's teat. Good results have been obtained after testing, which shows the feasibility and effectiveness of the cow teat positioning system.

show abstract

Recognition of Cow Teats Using the 3D-ToF Camera When Milking in the “Herringbone” Milking Parlor

Cited by 9 publications

References 4 publications

Comparison of In-Rumen Systems for Monitoring the Physiological State of Cattle

Comparison of In-Rumen Systems for Monitoring the Physiological State of Cattle

Teat Pose Estimation via RGBD Segmentation for Automated Milking

Research on tracking algorithm of milking robot arm based on improved SURF-KCF

Contact Info

Product

Resources

About