Precise detection of Eimeria oocysts in sheep: a deep learning model based on microscopic images
Year: 2025
Type: article
Abstract: BACKGROUND: Parasitic infections remain a major cause of productivity loss in global livestock production. Traditional microscopic diagnostic methods are labor-intensive and require specialized veterinary expertise. Recent automated detection systems are hindered by limited annotated microscopy datasets and the difficulty of extracting discriminative features from small, overlapping targets. METHODS: We propose YOLO-GA, an enhanced object detection framework, for accurate identification of Eimeria oocysts in ovine microscopy images. Built upon the YOLOv5's architecture, the model incorporates two lightweight attention modules: (1) Contextual Transformer (CoT) blocks for local-global contextual enhancement and (2) Normalized Attention Mechanisms (NAM) for adaptive feature recalibration. The proposed model is optimized for both accuracy and computational efficiency. RESULTS: Experiments on a curated dataset of 2000 microscopy images (200× magnification) demonstrated that YOLO-GA achieves a mean (± standard deviation) average precision ([email protected]) of 98.9% ± 0.1, with 95.2% ± 0.3 precision and real-time inference speed. Comparative evaluations against recent detectors, including YOLOv8, YOLOv10 and DETR variants, confirmed the superior performance of YOLO-GA across multiple runs. CONCLUSIONS: YOLO-GA offers a high-accuracy solution with balanced computational efficiency for automated detection of Eimeria oocysts under complex microscopy conditions. This work lays a foundation for intelligent diagnostics of ovine Eimeria coccidiosis and provides a reference for scalable health monitoring of sheep flocks, with potential extension to other small ruminant coccidiosis (e.g. goat Eimeria) pending further validation.
Source: Parasites & Vectors
Authors: Liangliang Liu, Jinpu Xie, Huikai Qin, Xiangqing Sui, Longxian Zhang
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W4416205346