TY - GEN
T1 - A Portable Device for Obtaining Body Condition Score of Dairy Cattle Based on Image Processing and Convolutional Neural Networks
AU - Oblitas, Edgar
AU - Villarreal, Rober
AU - Sanchez, Alonso
AU - Kemper, Guillermo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The present work develops an image classifier algorithm to measure the body condition score in Holstein cows. The algorithm aims to reduce the subjectivity that arises when evaluating cattle through visual inspection by specialists. This score measures how thin or overweight are cows in stables, which impacts milk production and the quality of life of the cattle. Although state-of-the-art attempts to solve the subjectivity problem, an efficient and satisfactory method for classification has not yet been found. Moreover, implementations have only considered placing fixed devices in the stables under certain restrictions. Therefore, a portable device with a graphical user interface was designed, and the images were captured and then segmented in a DeepLab3 + convolutional neural network. With this segmented database, the classifier algorithm was trained. For the validation of image segmentation, the Coefficient of Intersection over Union was used, achieving results over 0.9. This finally allowed us to obtain satisfactory results in the calculation of the body condition score.
AB - The present work develops an image classifier algorithm to measure the body condition score in Holstein cows. The algorithm aims to reduce the subjectivity that arises when evaluating cattle through visual inspection by specialists. This score measures how thin or overweight are cows in stables, which impacts milk production and the quality of life of the cattle. Although state-of-the-art attempts to solve the subjectivity problem, an efficient and satisfactory method for classification has not yet been found. Moreover, implementations have only considered placing fixed devices in the stables under certain restrictions. Therefore, a portable device with a graphical user interface was designed, and the images were captured and then segmented in a DeepLab3 + convolutional neural network. With this segmented database, the classifier algorithm was trained. For the validation of image segmentation, the Coefficient of Intersection over Union was used, achieving results over 0.9. This finally allowed us to obtain satisfactory results in the calculation of the body condition score.
KW - Body condition score
KW - CNN
KW - Classification algorithm
KW - Dairy cattle
KW - Image processing
KW - Image segmentation
UR - https://www.scopus.com/pages/publications/85161438150
U2 - 10.1007/978-3-031-31007-2_42
DO - 10.1007/978-3-031-31007-2_42
M3 - Contribución a la conferencia
AN - SCOPUS:85161438150
SN - 9783031310065
T3 - Smart Innovation, Systems and Technologies
SP - 447
EP - 460
BT - Proceedings of the 8th Brazilian Technology Symposium, BTSymn 2022 - Emerging Trends and Challenges in Technology
A2 - Iano, Yuzo
A2 - Saotome, Osamu
A2 - Kemper Vásquez, Guillermo Leopoldo
A2 - de Moraes Gomes Rosa, Maria Thereza
A2 - Arthur, Rangel
A2 - Gomes de Oliveira, Gabriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th Brazilian Technology Symposium, BTSym 2022
Y2 - 24 October 2022 through 26 October 2022
ER -