Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Tropical Animal Science Journal
Atmoko, B. A.
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Energy

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Comparative Performance Analysis of YOLOv10-Based Models with CBAM and SPPFCSPC for Body Condition Score Assessment in Beef Cattle Ariadi, F.; Utaminingrum, F.; Atmoko, B. A.
Tropical Animal Science Journal Vol. 48 No. 5 (2025): Tropical Animal Science Journal
Publisher : Faculty of Animal Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5398/tasj.2025.48.5.402

Abstract

Body condition score assessment serves as a critical metric for evaluating the health, nutritional status, and overall well-being of beef cattle, playing a pivotal role in herd management and productivity optimization. Traditional manual BCS assessment methods are inherently subjective, labor-intensive, and impractical for large-scale operations, thereby necessitating an automated and data-driven approach. This study investigates the performance of YOLOv10-based deep learning models, incorporating the convolutional block attention module (CBAM) and spatial pyramid pooling-fast cross-stage partial connections (SPPFCSPC) to enhance feature extraction, classification accuracy, and computational efficiency in BCS estimation. A total of 432 annotated images representing five BCS categories (1–5) were used for model training and evaluation. The models were assessed using precision, recall, and F1 Score, with expert-labeled ground truth ensuring robustness. Results show that the YOLOv10x variant achieved the highest classification accuracy of 88.2%, highlighting its superior detection capability. YOLOv10m exhibited a balanced trade-off between accuracy and computational efficiency, achieving an F1 Score of 79.2%. The integration of CBAM improved precision but slightly reduced recall, whereas SPPFCSPC enhanced recall at the expense of increased computational complexity. Notably, YOLOv10n achieved the fastest inference time of 1.0 ms but with a lower accuracy of 82.4%, underscoring the trade-off between model depth and real-time applicability. These findings validate the effectiveness of attention-based and multi-scale feature learning strategies for improving the automation of BCS classification in beef cattle.
Effect of Shearing on Thermo-Physiological, Behavior, and Productivity Traits of Two Indonesian Local Sheep Breeds Panjono; Ibrahim, A.; Ngadiyono, N.; Maulana, H.; Atmoko, B. A.
Tropical Animal Science Journal Vol. 47 No. 1 (2024): Tropical Animal Science Journal
Publisher : Faculty of Animal Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5398/tasj.2024.47.1.42

Abstract

Thin-tailed sheep (TTS) and Fat-tailed sheep (FTS) are local Indonesian sheep breeds characterized by coarse wool. This study aimed to investigate the effects of wool shearing on the thermo-physiological, behavior, and productivity traits of these sheep. Sixteen selected rams were utilized in this study. Animals were assigned to a factorial completely randomized design and divided into two groups (TTS and FTS) and two treatments (sheared and unsheared). The study spanned three months under controlled conditions. Variables observed included environmental conditions, thermo-physiological parameters (respiratory rate/RR, pulse rate/PR, rectal temperature/RT, and heat stress index/HSI), sheep behavior (feeding duration, drinking frequency, rumination duration, urination frequency, defecation frequency, standing duration, and lying duration), and sheep productivity (feed intake, average daily gain/ADG, and feed conversion ratio/FCR). Data were analyzed using two-way ANOVA. Throughout the study, average temperature and humidity ranged from 25.13-30.48 oC and 64.50%-91.67%, respectively. Wool shearing significantly influenced (p<0.05) sheep’s thermo-physiological, behavior, and productivity traits. These effects were consistent across sheep breeds, with no significant differences noted. Wool shearing significantly reduced (p<0.05) RR, PR, and RT, while the impact on average HSI was not significant. Additionally, sheared sheep exhibited increased (p<0.05) feeding, rumination, standing duration, and higher defecation frequency. Conversely, drinking frequency, urination frequency, and lying duration decreased in the sheared sheep group. Moreover, the sheared sheep demonstrated higher (p<0.05) feed intake and ADG, leading to a reduced (p<0.05) FCR compared to the unsheared group. In conclusion, shearing is a recommended practice for coarse wool-type sheep in tropical environments. This technique does not induce stress and enhances their thermo-physiological, behavior, and productivity traits.
Co-Authors Ariadi, F. Ibrahim, A. Maulana, H. N. Ngadiyono Panjono Utaminingrum, F.