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Cattle and sheep farming play a pivotal role in agricultural economies, yet parasitic diseases significantly impede industry development. Traditional manual diagnostic methods suffer from low efficiency and subjectivity. While art ificial intelligence (AI) based detection shows promise, its advancement is constrained by limited data availability. This study aims to integrate microscopic images, morphological characteristics and genomic data of bovine and sheep parasites, and establish a multimodal biometric database of bovine and sheep parasites. Standardized protocols were implemented to collect blood, fecal, and ectoparasite samples. Expert teams conducted cross-validated annotations following unified protocols to ensure data integrity. The database system, developed based on MySQL, comprises a structural design, a user front-end module, and an administrator backend module. This database provides comprehensive data support for AI model development and validation, promising enhanced efficiency and accuracy in parasitic disease detection, which facilitates the healthy development of the animal husbandry industry.

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Study on the Construction of a Biometric Database of Parasites in Cattle and Sheep

  • 会议论文

  • 发表日期：

    2026年

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  • Huikai Qin,
  • Xiangqing Sui,
  • Yongqi Sui,
  • Liangliang Liu,
  • Longxian Zhang

摘要

Cattle and sheep farming play a pivotal role in agricultural economies, yet parasitic diseases significantly impede industry development. Traditional manual diagnostic methods suffer from low efficiency and subjectivity. While art ificial intelligence (AI) based detection shows promise, its advancement is constrained by limited data availability. This study aims to integrate microscopic images, morphological characteristics and genomic data of bovine and sheep parasites, and establish a multimodal biometric database of bovine and sheep parasites. Standardized protocols were implemented to collect blood, fecal, and ectoparasite samples. Expert teams conducted cross-validated annotations following unified protocols to ensure data integrity. The database system, developed based on MySQL, comprises a structural design, a user front-end module, and an administrator backend module. This database provides comprehensive data support for AI model development and validation, promising enhanced efficiency and accuracy in parasitic disease detection, which facilitates the healthy development of the animal husbandry industry.