Browsing by Author "Ferraz, Priscila Assis"

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Accuracy of Early Pregnancy Diagnosis and Determining Pregnancy Loss Using Different Biomarkers and Machine Learning Applications in Dairy Cattle
(Elsevier Science inc, 2024) Ferraz, Priscila Assis; Poit, Diego Angelo Schmidt; Pinto, Leonardo Marin Ferreira; Guerra, Arthur Cobayashi; Neto, Adomar Laurindo; do Prado, Francisco Luiz; Pugliesi, Guilherme
This study aimed to compare the accuracy of IFN-tau stimulated gene abundance (ISGs) in peripheral blood mononuclear cells (PBMCs), CL blood perfusion by Doppler ultrasound (Doppler-US), plasma concentration of P4 on Day 21 and pregnancy-associated glycoproteins (PAGs) test on Day 25 after timed-artificial insemination (TAI) for early pregnancy diagnosis in dairy cows and heifers. Holstein cows (n = 140) and heifers (n = 32) were subjected to a hormonal synchronization protocol and TAI on Day 0. On Day 21 post-TAI, blood samples were collected for PBMC isolation and plasma concentration of P4. The CL blood perfusion was evaluated by Doppler- US. Plasma samples collected on Day 25 were assayed for PAGs. The abundance of ISGs ( ISG15 and RSAD2) ) in PBMCs was determined by RT-qPCR. Pregnancy was confirmed on Days 32 and 60 post-TAI by B-mode ultrasonography. Statistical analyses were performed by ANOVA using the MIXED procedure and GLIMMIX in SAS software. The pregnancy biomarkers were used to categorize the females as having undergone late luteolysis (LL); early embryonic mortality (EEM); late embryonic mortality (LEM); or late pregnancy loss (LPL). The abundance of ISGs, CL blood perfusion by Doppler-US, and concentrations of P4 on Day 21, and PAGs test on Day 25 were significant (P <0.05) predictors of early pregnancy in dairy cows and heifers. Dairy cows had a greater (P = 0.01) occurrence of LL than heifers, but there was no difference (P > 0.1) for EEM, LEM, and LPL in heifers compared to cows. Cows with postpartum reproductive issues had a greater (P = 0.008) rate of LEM and a lesser (P = 0.01) rate of LPL compared to cows without reproductive issues. In summary, the CL blood perfusion by Doppler-US had the highest accuracy and the least number of false negatives, suggesting it is the best predictor of pregnancy on Day 21 post-TAI. The PAGs test was the most reliable indicator of pregnancy status on Day 25 post- TAI in dairy heifers and cows. The application of machine learning, specifically the MARS algorithm, shows promise in enhancing the accuracy of predicting early pregnancies in cows.
Morphological Phenotyping for Cattle Breeds Classification From Unmanned Aerial Vehicle Imagery via Computer Vision and Deep Learning
(2025) Çakmakçı, Cıhan; Demırel, Ahmet Fatıh; Çakmakçı, Yusuf Çakmakçı; Hurma, Harun; Turan, Murat; Ferraz, Priscila Assis; Titto, Cristiane
Advancements in unmanned aerial vehicle (UAV) technologies have facilitated a novel approach to dairy cattle breed morphological identification. The objective of this study was to employ UAV images, analyzed through deep convolutional neural networks (DCNN), to classify dairy cow breeds. The dataset comprises of 2004 RGB UAV images of dairy cows, including Holstein, Simmental, and Brown-Swiss breeds, obtained from the cattle breeding facility at Van Yüzüncü Yıl University. The images were preprocessed and segmented to contain a single cow each, and subsequently categorized as training (70%), validation (20%), and testing (10%) datasets. To determine the most effective architecture for breed classification, we compared a custom DCNN (C-DCNN) model to well-established pre-trained models including Xception, VGG19, and ResNet50. The C-DCNN demonstrated remarkable performance, achieving precision, recall, accuracy, and F1 scores of 0.98. Among the pre-trained models, Xception demonstrated superior results, with perfect accuracy and an F1 score of 1.00. Conversely, the VGG19 model exhibited a higher level of accuracy; nevertheless, it exhibited lower precision, recall, and F1 scores when evaluated on the test set, compared to the C-DCNN and Xception models. In contrast, ResNet50 displayed the lowest level of performance, with an accuracy of 0.74 and the highest levels of loss. This study demonstrates the potential of integrating DCNN models with UAV technology in precision livestock farming, offering a robust and efficient system for cattle breed classification.