最大限度地集成多源数据,最大限度地减少温室番茄作物用水需求预测的参数
Xinyue Lv1, Youli Li2,3, Lili Zhangzhong4
1Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
Scientific reports
|August 9, 2025
概括
这项研究引入了一种新的模型,用于利用融合图像和环境数据预测温室番茄的水需求. 堆叠融合模型实现了最低的预测误差,改善了灌管理.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 准确预测作物用水需求对于保护农业的有效灌管理至关重要.
- 粮农组织认可的Penman-Monteith模型虽然是标准的,但由于参数复杂性和经验不准确性而面临挑战.
- 现有的方法往往难以准确预测温室作物的水需求.
研究的目的:
- 为温室番茄作物开发一种新的数据驱动的水需求预测模型.
- 整合多个来源的数据,包括从图像细分和环境因素获得的树冠覆盖范围.
- 提高作物用水需求估计科学灌的准确性和可靠性.
主要方法:
- 使用ExG算法和通过图像细分来提取树冠覆盖面的最大类别间差异.
- 雇佣的斯皮尔曼相关性和随机森林特征对最佳变量选择 (Tmax,Ts,CC) 有重要意义.
- 使用RandomForest,LightGBM和CatBoost机器学习算法开发和比较平均,加权和堆叠的融合模型.
主要成果:
- 与其他模型相比,堆叠融合模型表现出优异的预测性能,误差指标 (MSE,MAE,RMSE) 较低.
- 选择的特征组合 (Tmax,Ts,CC) 显著减少了预测错误,并增加了R2值.
- 新型模型实现了MSE,MAE和RMSE的减少,分别超过4%,14%和3%,R2增加1%.
结论:
- 开发的堆叠融合模型为预测温室番茄水需求提供了更准确,更可靠的方法.
- 将图像来源的树冠覆盖面与环境数据相结合,为科学灌实践提供了创新的技术支持.
- 这种方法有效地解和最小化了在受保护农业中改善水资源管理的特征参数.
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