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Improving sow lifetime performance through early detection of lameness and changes in body condition score

Grant number: 17/09893-9
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): March 01, 2019
Effective date (End): June 30, 2019
Field of knowledge:Agronomical Sciences - Agricultural Engineering - Rural Buildings and Ambience
Principal Investigator:Késia Oliveira da Silva
Grantee:Isabella Cardoso Ferreira da Silva Condotta
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil


Observation, control, and maintenance of the physical conditions of sows at acceptable levels are critical to maintaining animal welfare and production in adequate standards. Lameness causes pain and difficulty in locomotion, and, yet, it is a common disorder in sows that, in addition to the negative impact on welfare, also generates great impacts on production, since the animals that demonstrate this problem have fewer piglets born alive, fewer births per year, and are removed from the herd at a younger age than the ideal. It is also known that, during gestation, each sow should receive a different amount of ration according to its body condition. Underweight animals present nutritional deficiency and fewer piglets born per litter. On the other hand, the overweight sows present an abnormal development of the mammary glands, reducing the amount of milk produced during lactation, leading to economic losses. Both the lameness detection and the classification of body condition are made by subjective means and dependent on the opinion of the manager, which can generate differences between the classifications given by each individual. Therefore, the early recognition of animals that present physical conditions out of the required standards, aiming at the prevention of productive losses caused by both the worsening of the presented conditions and by the great impact on the animals' welfare, is important. Therefore, the present work aims to obtain three characteristics (body condition score, weight and backfat) using a Kinect® sensor, which proved effective in obtaining these characteristics in other animals (boars and dairy cows). In addition, with the same sensor, a method for the early detection of lameness in sows will be developed, using the animal kinematics approach, which has yielded good results and whose difficulties have the potential to be healed by using a depth sensor instead of the currently used reflective markers method. The general objective of the project is, therefore, to develop, validate and evaluate a new method of classification of the physical condition of sows based on depth images. It is expected that this research will develop methods for analyzing the physical condition of sows through depth images that can solve the present problem regarding the subjectivity of the methods used, ensuring a fast, accurate and safe measurement of this variable of great importance in the productive system. As a consequence of the development of these new methods, it is expected to improve the welfare of animals and workers and increase the efficiency of the production process. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CONDOTTA, ISABELLA C. F. S.; BROWN-BRANDL, TAMI M.; PITLA, SANTOSH K.; STINN, JOHN P.; SILVA-MIRANDA, KESIA O. Evaluation of low-cost depth cameras for agricultural applications. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 173, JUN 2020. Web of Science Citations: 0.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
CONDOTTA, Isabella Cardoso Ferreira da Silva. Depth images\' processing to improve the performance of sows through early detection of lameness and changes in body condition score. 2019. Doctoral Thesis - Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz Piracicaba.

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