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通过基于转移学习的模型提高氨度预测:在养猪场的应用

Sunhyoung Lee¹, Rack-Woo Kim², Hakjong Shin³

¹Department of Agriculture Engineering, College of Industrial Sciences, Kongju National University, 54 Daehak-ro, Yesan-eup, Yesan-gun 32439, Chungcheongnam-do, Republic of Korea.

Animals : an open access journal from MDPI

|February 27, 2026

概括

人工智能使用转移学习准确地预测猪舍中的氨 (NH3),优于仅在本地数据上训练的模型. 这种方法可以在智能农业中实现高效的环境管理.

科学领域:

农业工程农业工程
环境科学环境科学
人工智能的人工智能

背景情况:

猪养殖的强化引发了人们对氨 (NH3) 排放的担忧.
智能农业技术需要可靠的NH3监测,而不仅仅依赖昂贵的传感器.

研究的目的:

开发一种基于人工智能的模型,用于预测商业猪舍中的NH3度.
评估数据收集间隔和学习策略对预测准确性的影响.
将独立模型与转移学习模型进行 NH3 预测的比较.

主要方法:

为NH3度开发一个人工智能预测模型.
在本地数据上训练的独立模型与转移学习模型的比较.
使用随机森林和XGBoost算法对各种数据收集间隔 (10,20,30,60分钟) 的评估.

主要成果:

转移学习模型在所有测试的数据收集间隔中始终优于独立模型.
最好的随机森林和XGBoost模型实现了高精度,R2为0.969,RMSE~1.0 ppm,MAPE<5%.
即使使用转移学习的稀疏数据,也可以实现准确的NH3预测.

结论:

转移学习提供了一种可靠且数据效率高的方法,用于预测猪舍中的NH3度.
这种人工智能驱动的方法支持猪业的可持续和改进的环境管理.
这些发现有助于在智能农业环境中集成先进监测.

关键词:

在XGBoost中使用.氨是一种氨.人工智能的人工智能是人工智能.猪房子的猪房子转移学习转移学习

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