Automated body weight prediction of dairy cows using 3-dimensional vision

Song, Xiangyu; Bokkers, E.A.M.; Tol, P.P.J. van der; Koerkamp, P.W.G. Groot; Mourik, S. van

doi:10.3168/jds.2017-13094

Cited by 104 publications

(35 citation statements)

References 31 publications

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“…More recently, McPhee et al (2017) used a consumer 3D sensor (Kinect; Microsoft Corporation, Redmond, WA, USA) to predict muscle score and P8 fat of live animals with 80% correct classification rate. Further developments were done in pigs using the same sensors to predict LW and body dimensions with high accuracy and precision (Pezzuolo et al, 2018) and in dairy cows to predict BCS (Song et al, 2018). More advanced 3D imagery sensors have also been tested to measure BCS in dairy cattle with high accuracy and reproducibility (Fischer et al, 2015).…”

Section: Body Compositionmentioning

confidence: 99%

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Gonzalez

Kyriazakis

Tedeschi

2018

Animal

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A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data ‘as is’, further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

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Section: Body Compositionmentioning

confidence: 99%

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Gonzalez

Kyriazakis

Tedeschi

2018

Animal

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“…Computation, mainly in the area of image analysis and interpretation, has been used as a tool to, through photos or videos, extract characteristics that predict the weight of both dairy and beef cattle, fish, sheep, and pig (Tasdemir et al, 2011;Ozkaya, 2013;Song et al, 2018;Mortensen et al, 2016;Saberioon et al, 2017;Menesatti et al, 2014;Jun et al, 2018). There are difficulties in the automatic processing of these images, mainly in relation to the extraction of the object of interest by means of segmentation (Noviyanto and Arymurthy, 2012).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Girolando cattle weight by means of body measurements extracted from images

Weber¹,

Weber²,

Gomes³

et al. 2020

Revista Brasileira De Zootecnia

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The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HG P ), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HW I ), body length, tail distance to the neck, dorsum area (DA I ), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HG p was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation: WEIGHT (kg) = 6.15421 * HW I (cm) + 0.01929 * DA I (cm 2 ) + 70.8388. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images.

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“…Although in animal breeding their application is more scarce, modern livestock farming is beginning to benefit from access to these inexpensive sensor tools. Now, it is possible to remotely monitor behavior (Guzhva et al, 2016;Foris et al, 2019;Zehner et al, 2019) and animal welfare (Beer et al, 2016), assess movement (Chapinal et al, 2011), measure body confirmation (Van Hertem et al, 2013;Song et al, 2018), quantify individual food intake (Braun et al, 2014;Beer et al, 2016;Foris et al, 2019), maintain an optimum environment (Chen and Chen, 2019), or decrease instances of stillbirths (Palombi et al, 2013;Ouellet et al, 2016). These automated measurements rely on temperature (Palombi et al, 2013;Ouellet et al, 2016;Chen and Chen, 2019), pressure (Braun et al, 2014;Beer et al, 2016), movement (Chapinal et al, 2011), and visual (Van Hertem et al, 2013;Guzhva et al, 2016;Song et al, 2018;Foris et al, 2019;Zehner et al, 2019) sensors.…”

Section: Introductionmentioning

confidence: 99%

Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach

2020

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Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image. This method performs accurately when compared with manual classification of proportion of coat color with an adjusted R 2 = 0.926. Using the output mask, we are able to automatically extract useful phenotypic information for 14 additional morphological features. Using pedigree and image information from a web catalog (www.semex.com), we estimated high heritabilities (ranging from h 2 = 0.18-0.82), indicating that meaningful biological information has been extracted automatically from imaging data. This method can be applied to other datasets and requires only a minimal number of image annotations (∼50) to train this partially supervised machinelearning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https://github.com/lauzingaretti/deepaps.

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Automated body weight prediction of dairy cows using 3-dimensional vision

Cited by 104 publications

References 31 publications

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Prediction of Girolando cattle weight by means of body measurements extracted from images

Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach

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