Estimation of beef cow body condition score: a machine learning approach using three-dimensional image data and a simple approach with heart girth measurements

Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis.

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Machine-learning approach for constructing control landscape maps of three-dimensional alignment of asymmetric-top molecules

The Journal of Chemical Physics ◽

10.1063/5.0012303 ◽

2020 ◽

Vol 153 (2) ◽

pp. 024120

Author(s):

Tomotaro Namba ◽

Masataka Yoshida ◽

Yukiyoshi Ohtsuki

Keyword(s):

Machine Learning ◽

Three Dimensional ◽

Learning Approach ◽

Machine Learning Approach ◽

Asymmetric Top ◽

Control Landscape

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Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements

Livestock Science ◽

10.1016/j.livsci.2020.104054 ◽

2020 ◽

Vol 236 ◽

pp. 104054 ◽

Cited By ~ 2

Author(s):

B.M. Martins ◽

A.L.C. Mendes ◽

L.F. Silva ◽

T.R. Moreira ◽

J.H.C. Costa ◽

...

Keyword(s):

Body Weight ◽

Dairy Cows ◽

Body Condition ◽

Body Condition Score ◽

Three Dimensional ◽

Body Measurements ◽

Condition Score ◽

Type Traits

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29 The effect of fleshing ability on cow and calf performance

Journal of Animal Science ◽

10.1093/jas/skz053.049 ◽

2019 ◽

Vol 97 (Supplement_1) ◽

pp. 22-22

Author(s):

Amanda Holder ◽

Aksel Wiseman ◽

Adam McGee ◽

David Lalman ◽

Claire Andresen

Keyword(s):

Body Weight ◽

Milk Yield ◽

Body Condition ◽

Body Condition Score ◽

Beef Cows ◽

Milk Composition ◽

Weight Changes ◽

Beef Cow ◽

Randomized Design ◽

Condition Score

Abstract Several factors influence the overall maintenance requirements of a mature beef cow including age, gain, lactation, pregnancy, and fleshing ability. However, limited research is available to distinguish what sets a hard-fleshing cow apart from an easy-fleshing cow. Cows that are hard-fleshing maintain a lower body condition score (BCS) throughout the year compared to easy-fleshing counterparts. The objectives of this experiment are to determine the differences in characteristics and production between cows classified as easy- vs. hard- fleshing. Characteristics of interest include feed intake, milk yield, milk composition, body weight changes, BCS changes, and other body composition measurements, as well as calf weaning weight. In this study, 24 spring-calving, mature Angus beef cows were classified as either hard-fleshing or easy-fleshing based on BCS and ultrasound measurements for back fat and rump fat. The intake study took place during the second trimester, cows were assigned to an easy- or hard-fleshing pen based on treatment where they remained for the entirety of the 45-day intake study. Each treatment was replicated three times in a completely randomized design. Milk data collection began one month after calving with monthly milkings from May-August. There were no differences (P = 0.9) in DMI, although hard-fleshing cows had greater DMI calculated on a metabolic body weight basis (P = 0.05). There was a trend (P = 0.12) for hard-fleshing cows to wean heavier calves, although there was no difference in mean milk yield (P = 0.44). Body condition score was positively correlated with protein and carbohydrate content of milk with easy-fleshing cows having greater contents of both (P = 0.02 and P < 0.01, respectively). Overall, an increase in BCS without an increase in DMI may be beneficial from a reproductive standpoint, though more research in this area is needed.

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Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes

Agriculture ◽

10.3390/agriculture11020162 ◽

2021 ◽

Vol 11 (2) ◽

pp. 162

Author(s):

Jimmy Semakula ◽

Rene A. Corner-Thomas ◽

Stephen T. Morris ◽

Hugh T. Blair ◽

Paul R. Kenyon

Keyword(s):

Machine Learning ◽

Body Condition ◽

Annual Cycle ◽

Body Condition Score ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Ovis Aries ◽

Gradient Boosting ◽

Support Vector ◽

Condition Score

Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k-nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0–2.0, 2.5–3.5, >3.5) scale was used due to high-class imbalance in the five-scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (>85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms.

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An Improved Approach to Automated Measurement of Body Condition Score in Dairy Cows Using a Three-Dimensional Camera System

Animals ◽

10.3390/ani12010072 ◽

2021 ◽

Vol 12 (1) ◽

pp. 72

Author(s):

Rodrigo I. Albornoz ◽

Khageswor Giri ◽

Murray C. Hannah ◽

William J. Wales

Keyword(s):

Dairy Cows ◽

Body Condition ◽

Body Condition Score ◽

Three Dimensional ◽

Data Sets ◽

Camera System ◽

Visual Method ◽

Condition Score ◽

Body Condition Scoring ◽

Over Time

Body condition scoring is a valuable tool used to assess the changes in subcutaneous tissue reserves of dairy cows throughout the lactation resulting from changes to management or nutritional interventions. A subjective visual method is typically used to assign a body condition score (BCS) to a cow following a standardized scale, but this method is subject to operator bias and is labor intensive, limiting the number of animals that can be scored and frequency of measurement. An automated three-dimensional body condition scoring camera system is commercially available (DeLaval Body Condition Scoring, BCS DeLaval International AB, Tumba, Sweden), but the reliability of the BCS data for research applications is still unknown, as the system’s sensitivity to change in BCS over time within cows has yet to be investigated. The objective of this study was to evaluate the suitability of an automated body condition scoring system for dairy cows for research applications as an alternative to visual body condition scoring. Thirty-two multiparous Holstein-Friesian cows (9 ± 6.8 days in milk) were body condition scored visually by three trained staff weekly and automatically twice each day by the camera for at least 7 consecutive weeks. Measurements were performed in early lactation, when the greatest differences in BCS of a cow over the lactation are normally present, and changes in BCS occur rapidly compared with later stages, allowing for detectable changes in a short timeframe by each method. Two data sets were obtained from the automatic body condition scoring camera: (1) raw daily BCS camera values and (2) a refined data set obtained from the raw daily BCS camera data by fitting a robust smooth loess function to identify and remove outliers. Agreement, precision, and sensitivity properties of the three data sets (visual, raw, and refined camera BCS) were compared in terms of the weekly average for each cow. Sensitivity was estimated as the ratio of response to precision, providing an objective performance criterion for independent comparison of methods. The camera body condition scoring method, using raw or refined camera data, performed better on this criterion compared with the visual method. Sensitivities of the raw BCS camera method, the refined BCS camera method, and the visual BCS method for changes in weekly mean score were 3.6, 6.2, and 1.7, respectively. To detect a change in BCS of an animal, assuming a decline of about 0.2 BCS (1–8 scale) per month, as was observed on average in this experiment, it would take around 44 days with the visual method, 21 days with the raw camera method, or 12 days with the refined camera method. This represents an increased capacity of both camera methods to detect changes in BCS over time compared with the visual method, which improved further when raw camera data were refined as per our proposed method. We recommend the use of the proposed refinement of the camera’s daily BCS data for research applications.

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