PSXII-15 Automatic Estimation of Body Condition Score in Finished Cattle Using Deep Learning Computational Architectures in Two-Dimensional Images

Abstract

Abstract The objective of this research was to investigate the possibility of using computational architectures of deep learning neural networks (deep learning) in two-dimensional images to automatically estimate the BCS (body condition score) in beef cattle. Data collection lasted from March 2021 to December 2022 and was carried out on a commercial farm in Mato Grosso do Sul, Brazil. Data from 1,570 animals of different genetic compositions, sex classes, ages and body weights were used. The animals were randomly selected in the confinement stalls, identified and concomitantly. To obtain the RGB digital images, the 3DBeef @Tech device was used, which was implemented in Python (based on OpenCV, 2015). The sensor was positioned in the passageway at a distance of 2.50 meters, allo wing the collection of images of only one animal at a time. The bBCS was evaluated remotely by trained professionals during the data collection period, ranging from 1 to 9, through the visualization of fat deposition and muscle coverage in certain anatomical points of the animal. The collected images and evaluations were used as input for a segmentation convolutional neural network (CNN; Figure 1). The labeled data set was divided into training, validation and testing, respectively in the proportions of 65%, 25% and 10%. A deep neural network was constructed to classify BCS using Python and the deep learning library Tensorflow and run through a Pro+ instance of Google Collaboratory. The results obtained show that, despite the small amount of data currently available for training, the neural network had a performance designed to classify BCS, with training accuracy of 80.5% and validation accuracy of 65.9% (Figure 2 and 3). when compared with professional raters. The validation still exhibited accuracy, recall, F1-score and AUC of 0.6676, 0.6562, 0.6562 and 0.9519 (P < 0.05), respectively. Despite the fact that the BCS assessment is a problem of difficult visual classification, both due to the variability of possible scenarios and the subtle difference between different BCS scores. It is believed that with a greater number of evaluated images, an accuracy greater than 90% will be achieved for the BCS. Therefore, it is necessary to continue with studies to obtain a greater number of images collected in search of greater accuracy.