开发了一种监督深度学习模型,用于对巴西亚马逊地区的热应激性牛 (Bos indicus) 进行分类
Welligton Conceição da Silva1, Jamile Andréa Rodrigues da Silva2, Lucietta Guerreiro Martorano3
1Postgraduate Program in Animal Science (PPGCAN), Institute of Veterinary Medicine, Federal University of Para (UFPA), Federal Rural University of the Amazon (UFRA), Brazilian Agricultural Research Corporation (EMBRAPA), Castanhal 68746-360, PA, Brazil.
一个深度学习模型有效地识别了Nelore牛的热应激,但在舒适度分类方面存在困难. 为了提高精确的畜牧业,需要进一步完善输入数据和平衡类.
科学领域:
- 农业技术 农业技术
- 动物科学动物科学
- 人工智能的人工智能
背景情况:
- 在农业中实时监控可以提高决策能力,减少动物的压力.
- 非侵入性技术对于在各种气候条件下监测牲畜至关重要.
研究的目的:
- 开发一种深度学习模型,将Nelore牛 (Bos indicus) 分类为热舒适和热应激组.
- 评估模型在不同热条件下识别牛的准确性,回忆力和特异性.
主要方法:
- 在2023年6月至12月期间,从30头Nelore牛 (18-20个月) 收集了676个样本.
- 测量的生物变量:直肠温度 (RT) 和呼吸速率 (RR).
- 测量的非生物变量:空气温度 (AT) 和相对湿度 (RH).
- 使用深度学习监督神经网络模型进行分类.
主要成果:
- 该模型实现了72%的准确性和72%的回忆.
- 特异性低,为42%,表明在热舒适度下识别牛的困难.
- 挑战可能源于阶级不平衡和环境适应性不足的投入特征.
结论:
- 监督学习模型对于精密畜牧业非常有价值.
- 通过改进输入特征和平衡数据,可以提高模型性能.
- 需要进一步的研究来提高模型准确区分热舒适度的能力.
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