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基于机器学习的南中国温室的温度和相对湿度预测
Xinyu Wei1,2, Yizhi Luo1, Xingxing Zhou1
1Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China.
Scientific reports
|July 10, 2025
概括
准确的温室气候预测对农业至关重要. 这项研究发现,15分钟间隔的最小平方支向量机 (LSSVM) 模型有效预测温度和湿度,改善了作物管理.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 温室气候控制对于最佳的作物生产至关重要.
- 现有的预测系统存在不一致的数据分辨率和缺乏标准化的协议,阻碍了互操作性.
- 准确预测温度和相对湿度可以及时进行手动干预.
研究的目的:
- 评估BPPSO,LSSVM和RBF模型对温室温度和相对湿度的预测性能.
- 确定在中国南方的温室中获取环境数据的最佳时间间隔.
- 确定最适合用于准确温室气候预测的模型.
主要方法:
- 使用了反向传播粒子集群优化 (BPPSO),最小平方支向量机 (LSSVM) 和辐射基函数 (RBF) 模型.
- 在中国南方不同时间间隔收集温室环境数据.
- 使用R平方 (R2),平均绝对误差 (MAE),平均绝对百分比误差 (MAPE) 和根平均平方误差 (RMSE) 评估模型性能.
主要成果:
- 温度和相对湿度预测的R2值随着时间间隔的减少而增加,其中15分钟产生了最高的R2.
- 与BPPSO和RBF模型相比,LSSVM表现出优越的性能,相对湿度的R2值为0.952,温度为0.923.
- 对于相对湿度的预测准确性通常高于所有模型和间隔的温度.
结论:
- 具有15分钟数据采集间隔的LSSVM模型非常适合预测中国南方温室的温度和相对湿度.
- 这种预测能力支持积极的温室气候管理和早期干预策略.
- 这些发现为提高农业环境控制系统的效率和互操作性提供了宝贵的见解.
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