Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC

Ragusa, Italy

Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC

Ragusa, Italy

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Azzaro G.,Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC | Caccamo M.,Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC | Ferguson J.D.,University of Pennsylvania | Battiato S.,University of Catania | And 6 more authors.
Journal of Dairy Science | Year: 2011

Body condition score (BCS) is considered an important tool for management of dairy cattle. The feasibility of estimating the BCS from digital images has been demonstrated in recent work. Regression machines have been successfully employed for automatic BCS estimation, taking into account information of the overall shape or information extracted on anatomical points of the shape. Despite the progress in this research area, such studies have not addressed the problem of modeling the shape of cows to build a robust descriptor for automatic BCS estimation. Moreover, a benchmark data set of images meant as a point of reference for quantitative evaluation and comparison of different automatic estimation methods for BCS is lacking. The main objective of this study was to develop a technique that was able to describe the body shape of cows in a reconstructive way. Images, used to build a benchmark data set for developing an automatic system for BCS, were taken using a camera placed above an exit gate from the milking robot. The camera was positioned at 3. m from the ground and in such a position to capture images of the rear, dorsal pelvic, and loin area of cows. The BCS of each cow was estimated on site by 2 technicians and associated to the cow images. The benchmark data set contained 286 images with associated BCS, anatomical points, and shapes. It was used for quantitative evaluation. A set of example cow body shapes was created. Linear and polynomial kernel principal component analysis was used to reconstruct shapes of cows using a linear combination of basic shapes constructed from the example database. In this manner, a cow's body shape was described by considering her variability from the average shape. The method produced a compact description of the shape to be used for automatic estimation of BCS. Model validation showed that the polynomial model proposed in this study performs better (error = 0.31) than other state-of-the-art methods in estimating BCS even at the extreme values of BCS scale. © 2011 American Dairy Science Association.


Di Silvestro L.,University of Catania | Burch M.,University of Stuttgart | Caccamo M.,Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC | Weiskopf D.,University of Stuttgart | And 2 more authors.
IVAPP 2014 - Proceedings of the 5th International Conference on Information Visualization Theory and Applications | Year: 2014

This paper addresses the problem of analyzing data collected by the dairy industry with the aim of optimizing the cattle-breeding management and maximizing profit in the production of milk. The amount of multivariate data from daily records constantly increases due to the employment of modern systems in farm management, requiring a method to show trends and insights in data for a rapid analysis. We have designed a visual analytics system to analyze time-varying data. Well-known visualization techniques for multivariate data are used next to novel methods that show the intrinsic multiple timeline nature of these data as well as the linear and cyclic time behavior. Seasonal and monthly effects on production of milk are displayed by aggregating data values on a cow-relative timeline. Basic statistics on data values are dynamically calculated and a density plot is used to quantify the reliability of a dataset. A qualitative expert user study conducted with animal researchers shows that the system is an important means to identify anomalies in data collected and to understand dominant data patterns, such as clusters of samples and outliers. The evaluation is complemented by a case study with two datasets from the field of dairy science.


Caccamo M.,Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC | Di Silvestro L.,University of Catania | Burch M.,University of Stuttgart | Weiskopf D.,University of Stuttgart | Gallo G.,University of Catania
Precision Livestock Farming 2013 - Papers Presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013 | Year: 2013

In many research fields, the amount of collected data surpasses the ability of the domain expert to analyze these data directly. One approach to overcome this data-analysis challenge is by using visual analytics that employs a visual metaphor to represent the data to detect patterns or simply identify outliers. In this paper, an integrated system for visual analysis of test-day milk and milk components yield records. Data collected in Ragusa province (Italy) from several herds were used to identify trends and between-breed differences in milk production curves. Besides common multivariate data visualizations, the developed system provides techniques for time-varying and seasonal data analysis. Scatter plots with interactive filtering allow users to highlight the correlation between milk, fat, and protein production changes over time with different coefficients depending on the breed of the cow, the parity and the season of calving. Histograms are used to explore density of data sampling for different dairy farms. Multiple line charts show time varying data pointing out distinct behavior for different months or seasons. A user friendly environment allows animal researchers to dynamically produce several kinds of plots to reveal well-known properties as well as find out new interesting characteristics in data, predict individual production for a specific animal at a particular parity and test-day, and identify possible outliers. It could be shown how graphically reproduce the effect of correlation between milk components according to the lactation curve and how to make clear production differences for different breeds.


Caccamo M.,Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC | Guarnera G.C.,University of Catania | Licitra G.,University of Catania | Azzaro G.,Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC | And 2 more authors.
Precision Livestock Farming 2015 - Papers Presented at the 7th European Conference on Precision Livestock Farming, ECPLF 2015 | Year: 2015

Body condition score is an indicator of cows' health status based on visual or tactile inspection. Human assessment of body condition score is the main limiting factor as it is subjective and requires time and well-trained experts. The objective of this study was to explore the potential for using computer vision to assist human experts in this task and for efficient automation of the process of quantitatively estimating the body condition score of cows based on a 5-point scale, using images acquired with commercial low-cost digital cameras. Images were acquired using a camera mounted on a portable device 3 m above the ground, placed in a position which made it possible to capture images of the dorsal area of cows. The body condition score of each cow was estimated on site by 2 technicians and properly associated with the cows' images. Cow shapes were extracted from the images automatically and aligned in a unique reference frame. Standard principal component analysis was applied to determine the components describing the many ways in which the body shape of different cows tends to deviate from the average shape. The proposed method was tested on a benchmark data set containing 286 images by means of the leave one out cross validation procedure. The error of the proposed method was compared to the performance of other estimation methods based on image evaluation which are reported in the literature. The experimental results confirmed the effectiveness of the proposed technique (error=0.26 body condition score points against human observation) which outperformed other state-of-the-art approaches proposed in the context of dairy cattle research.


PubMed | Consorzio Ricerca Filiera Lattiero Casearia CoRFiLaC
Type: Journal Article | Journal: Journal of dairy science | Year: 2011

Body condition score (BCS) is considered an important tool for management of dairy cattle. The feasibility of estimating the BCS from digital images has been demonstrated in recent work. Regression machines have been successfully employed for automatic BCS estimation, taking into account information of the overall shape or information extracted on anatomical points of the shape. Despite the progress in this research area, such studies have not addressed the problem of modeling the shape of cows to build a robust descriptor for automatic BCS estimation. Moreover, a benchmark data set of images meant as a point of reference for quantitative evaluation and comparison of different automatic estimation methods for BCS is lacking. The main objective of this study was to develop a technique that was able to describe the body shape of cows in a reconstructive way. Images, used to build a benchmark data set for developing an automatic system for BCS, were taken using a camera placed above an exit gate from the milking robot. The camera was positioned at 3 m from the ground and in such a position to capture images of the rear, dorsal pelvic, and loin area of cows. The BCS of each cow was estimated on site by 2 technicians and associated to the cow images. The benchmark data set contained 286 images with associated BCS, anatomical points, and shapes. It was used for quantitative evaluation. A set of example cow body shapes was created. Linear and polynomial kernel principal component analysis was used to reconstruct shapes of cows using a linear combination of basic shapes constructed from the example database. In this manner, a cows body shape was described by considering her variability from the average shape. The method produced a compact description of the shape to be used for automatic estimation of BCS. Model validation showed that the polynomial model proposed in this study performs better (error=0.31) than other state-of-the-art methods in estimating BCS even at the extreme values of BCS scale.

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