Comparing State-of-the-Art Deep Learning Algorithms for the Automated Detection and Tracking of Black Cattle

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 1; p. 532
• Main Authors: Myat Noe, Su, Zin, Thi Thi, Tin, Pyke, Kobayashi, Ikuo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.01.2023; MDPI
• Subjects: Accuracy; Algorithms; Automation; Cameras; Cattle; cattle detection; cattle tracking; Dairy farming; Datasets; Deep learning; Farms; Internet of Things; Livestock; Machine learning; multi-object tracking; Multiple target tracking; Neural networks; precision livestock farming; Radio communications; re-identification; Reproduction (biology); Sensors; Surveillance; System effectiveness; Technology application; Tracking systems; Weight reduction; Japan; cattle detection; multi-object tracking; precision livestock farming; re-identification; deep learning; cattle tracking
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23010532

Effective livestock management is critical for cattle farms in today's competitive era of smart modern farming. To ensure farm management solutions are efficient, affordable, and scalable, the manual identification and detection of cattle are not feasible in today's farming systems. Fortunately, automatic tracking and identification systems have greatly improved in recent years. Moreover, correctly identifying individual cows is an integral part of predicting behavior during estrus. By doing so, we can monitor a cow's behavior, and pinpoint the right time for artificial insemination. However, most previous techniques have relied on direct observation, increasing the human workload. To overcome this problem, this paper proposes the use of state-of-the-art deep learning-based Multi-Object Tracking (MOT) algorithms for a complete system that can automatically and continuously detect and track cattle using an RGB camera. This study compares state-of-the-art MOTs, such as Deep-SORT, Strong-SORT, and customized light-weight tracking algorithms. To improve the tracking accuracy of these deep learning methods, this paper presents an enhanced re-identification approach for a black cattle dataset in Strong-SORT. For evaluating MOT by detection, the system used the YOLO v5 and v7, as a comparison with the instance segmentation model Detectron-2, to detect and classify the cattle. The high cattle-tracking accuracy with a Multi-Object Tracking Accuracy (MOTA) was 96.88%. Using these methods, the findings demonstrate a highly accurate and robust cattle tracking system, which can be applied to innovative monitoring systems for agricultural applications. The effectiveness and efficiency of the proposed system were demonstrated by analyzing a sample of video footage. The proposed method was developed to balance the trade-off between costs and management, thereby improving the productivity and profitability of dairy farms; however, this method can be adapted to other domestic species.