在人工智能驱动下优化农业用水管理,以提高可持续性
Zhigang Ye1, Shan Yin1, Yin Cao1
1College of Geographical Science, Inner Mongolia Normal University, Hohhot, 010022, China.
Scientific reports
|October 29, 2024
概括
这项研究引入了一个新的深度学习框架,用于预测农业用水需求,提高灌效率和作物产量. UNet-ConvLSTM模型通过整合来自遥感的空间和时间数据来增强预测.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 优化农业水资源管理对于全球粮食安全至关重要.
- 当前的水需求预测方法与复杂的空间和时间数据依赖性作斗争.
- 提高灌效率和作物产量需要精确的水资源管理策略.
研究的目的:
- 开发一种新的深度学习框架,用于准确预测农业用水需求.
- 从遥感数据中有效整合空间和时间特征.
- 提高灌效率,支持可持续的粮食生产.
主要方法:
- 一个深度学习框架,将遥感技术与UNet-ConvLSTM (UCL) 模型相结合.
- 整合来自UNet的空间数据和由ConvLSTM捕获的时间依赖.
- 使用MODIS和GLDAS数据集进行模型培训和验证.
主要成果:
- UCL模型显著提高了对农业用水需求的预测准确度.
- 在MODIS数据集上实现了0.927的高性能指标 ().
- 在GLDAS数据集上实现了0.935的高性能指标.
结论:
- 拟议的UCL模型在现有方法中表现出优越的性能.
- 人工智能和遥感技术为农业水资源管理挑战提供了强大的解决方案.
- 这种方法有助于更可持续和更有效的食品生产系统.
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