Non-intrusive automated measurement of dairy cow body condition using 3D video

Hansen, Mark F.; Smith, Melvyn L.; Smith, Lincoln; Hales, Ian John

doi:10.5244/c.29.mvab.1

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…In the Table 1 ii. Mostly, there are two types of models used: regression analysis models (as in) [10][11][12][13][14][15][16] and algorithms that measure cow's body angularity (as in) [17][18][19] according to the hypothesis that the body shape of a fatter cow is rounder than that of a thin cow. Moreover, three automation levels are described.…”

Section: Discussionmentioning

confidence: 99%

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Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

Alvarez

Arroqui

Mangudo

et al. 2017

JDVAR

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BCS is a method to estimate body fat stores and accumulated energy balance of cows. This value influences productivity, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve a better animal response. In practice, this task is performed by expert scorers mainly visually, and could vary between scorers and be time-consuming. For this reason, several studies have tried to automate BCS by applying image analysis and machine learning techniques. An overview of selected studies is provided in this mini review. 74% within 0.25, 91% within 0.5 2D, two dimensional; 3D, three-dimensional; ToF, time-of-flight; SRB, swedish red breed; GLM, generalized linear model; US-BCS, united state body condition score; UK-BCS, united kingdom body condition score; R, correlation coefficient; R 2 , coefficient of determination; LOOCV, leave one out cross validation; RMSE, root mean square error ConclusionThe literature attempts to automate BCS assessment look promising as a tool for supporting cattle decision-making, in a context where ICT technology is becoming more efficient, productive, and cheaper. Acceptable accuracy within the range of human error has been reported, with room for improvement as more effective computing processing methods became available. AcknowledgementsNone. Conflict of interestAuthor declares that there is no conflict of interest.

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Section: Discussionmentioning

confidence: 99%

“…In the medium level are, 11,13,20 where the input images used are manually selected, but the rest of the process is automatic. Finally, in the highest level are, 14,[16][17][18][19] where the process is completely automated. Among the latter studies, only 17,18 carry out real time estimations (i.e.…”

Section: Discussionmentioning

confidence: 99%

Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

Alvarez

Arroqui

Mangudo

et al. 2017

JDVAR

View full text Add to dashboard Cite

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“…Thus, in this work models composed by one (Depth) and two (Depth and Edge) input channels were also analyzed, using the same architecture model defined in [15] (Figure 3). Then, new models were identified as follow: Other combinations of one and two input channels were not analyzed because, firstly it is important to preserve a part or a partial variation of the original data (depth data), and secondly because data corresponding to the contour of the cows (edge data) have also proven to be a determinant feature (in machine learning terms) to guide the estimation of BCS from images, as evidenced in previous works [4,[6][7][8][9]24,25].…”

Section: Cnn Models Trained From Scratchmentioning

confidence: 99%

“…The increasing advances in technology availability at an accessible cost, automation, and digitalization of livestock farming tasks offer multiple opportunities to aid BCS estimation. In this context, different studies have particularly focused on BCS automation using digital images [4][5][6][7][8][9][10]. In these works the traditional model of pattern/image recognition was applied, in which a by hand-designed feature extractor gathers relevant information from the input image.…”

Section: Introductionmentioning

confidence: 99%

Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques

et al. 2019

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BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%).

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“…Therefore, the increasing advances in technology availability at an accessible cost, automation, and digitalization of livestock farming tasks offer multiple opportunities. In this context, different studies have particularly focused on BCS automation (Shelley, 2016;Fischer et al, 2015;Hansen et al, 2015;Bercovich et al, 2013;Halachmi et al, 2013;Azzaro et al, 2011;Spoliansky et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Body condition estimation on cows from depth images using Convolutional Neural Networks

Álvarez

Arroqui

Mangudo

et al. 2018

Computers and Electronics in Agriculture

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BCS ("Body Condition Score") is a method used to estimate body fat reserves and accumulated energy balance of cows. BCS heavily influences milk production, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve better animal response, but this is a time-consuming and subjective task performed visually by expert scorers. Several studies have tried to automate BCS of dairy cows by applying image analysis and machine learning techniques. This work analyzes these studies and proposes a system based on Convolutional Neural Networks (CNNs) to improve overall automatic BCS estimation, whose use might be extended beyond dairy production. The developed system has achieved good estimation results in comparison with other systems in the area. Overall accuracy of BCS estimations within 0.25 units of difference from true values was 78%, while overall accuracy within 0.50 units was 94%. Similarly, weighted precision and recall, which took into account imbalance BCS distribution in the built dataset, show similar values considering those error ranges. 1. uses images as the only information source (without external data such as weight, age, or lactation stage of the cow), 2. uses low-cost hardware resources, 3. gets real-time estimations,

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Non-intrusive automated measurement of dairy cow body condition using 3D video

Cited by 11 publications

References 4 publications

Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

Advances in Automatic Detection of Body Condition Score of Cows: A Mini Review

Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques

Body condition estimation on cows from depth images using Convolutional Neural Networks

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