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In this study, we address the challenges of counting cattle in large pasture areas using Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras. Traditional manual counting methods are laborious and error-prone, while existing automated approaches struggle with duplicate animal detection. To overcome these limitations, we propose a novel graph-based method that incorporates multiple-attributes, including velocity, direction, state (lying down or standing), color, and distance, to improve duplicate removal and counting accuracy. We conducted extensive experiments involving automated hyper-parameter learning to effectively integrate these attributes into our method. By employing a Ford–Fulkerson graph algorithm, we detect and remove duplicated cattle based on their multiple attributes. An ablation study validates the contribution of each attribute. Additionally, we provide new datasets of drone images captured in large pastures to support research in this field. Our results demonstrate that our proposed method outperforms state-of-the-art techniques, achieving an average percentage error of 2.34%. Comparisons with mosaic-based and other graph-based methods validate the effectiveness of our approach, which contributes to more efficient cattle counting practices and enhances livestock management in agriculture. • Counting cattle in the field using multiple drone's (UAV's) images. • Multi-attribute analysis for detecting & removing duplicate cattle in multiple images. • Novel datasets for cattle classification and counting in large areas. [ABSTRACT FROM AUTHOR]

• Graph based method to detect and remove duplicated cattle using multiple UAV's images. • Competitive results with significant reduction of runtime. • Novel benchmark image collection for cattle detection and counting in large areas. Among the production areas with largest impact on global economy, agriculture and livestock play a prominent role. Technologies have been developed in order to automate and increase the efficiency of these fields. The use of Unmanned Aerial Vehicles (UAVs) has been extensively investigated to improve the efficiency of agricultural production and in the monitoring of animals. One of the most important and challenging tasks in animal monitoring is cattle counting. In this paper, we propose a method for detecting and counting cattle in aerial images obtained by UAVs, based on Convolutional Neural Networks (CNNs) and a graph-based optimization to remove duplicated animals detected in overlapped images. We show that maximizing the degree of matching between animals is a suitable strategy to reduce duplicate counting. We also offer a dataset of real images, obtained from large pasture areas, both for training as well as for testing/benchmarking of cattle counting techniques. Our results show that the proposed method is very competitive, outperforming the state of the art in detecting duplicated animals, while significantly reducing the computational cost of the overall counting task. [ABSTRACT FROM AUTHOR]