用AI预测奶牛乳头形状和皮肤状态，提升养殖管理效率
AI-Based Teat Shape and Skin Condition Prediction for Dairy Management
- 基于视觉与AI识别乳头位置、形状及皮肤状况
- 乳头形状预测mAP达0.783，皮肤状态预测mAP达0.828
- 适合智慧牧场、动物健康监测领域应用
奶农需投入大量精力保障奶牛健康。尽管计算机视觉与人工智能（AI）有望降低此类成本，但将先进工具应用于农业环境仍面临挑战。本文将AI技术应用于奶牛乳头定位、乳头形状及乳头皮肤状况分类。同时构建了适用于机器学习（ML）流程的数据采集与分析方法。最终的乳头形状预测模型达到0.783的平均精度均值（mAP），皮肤状况模型则达到0.828的mAP。本工作利用现有ML视觉模型，实现对个体乳头健康与皮肤状态的精准识别，推动AI在奶业管理中的落地应用。
原文摘要 · Abstract (English)
Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow teat localization, teat shape, and teat skin condition classifications. We also curate a data collection and analysis methodology for a Machine Learning (ML) pipeline. The resulting teat shape prediction model achieves a mean Average Precision (mAP) of 0.783, and the teat skin condition model achieves a mean average precision of 0.828. Our work leverages existing ML vision models to facilitate the individualized identification of teat health and skin conditions, applying AI to the dairy management industry.
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