增长趋势预测和干预Panax的Notoginseng增长状态基于数据驱动的方法
Jiahui Ye1,2, Xiufeng Zhang1,2, Gengen Li1,2
1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China.
Plants (Basel, Switzerland)
|April 26, 2025
概括
这项研究介绍了一种智能农业方法,使用深度学习进行准确的作物灌预测. 数据驱动的方法显著提高了作物产量和植物生长,同时减少了用水量.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 农作物灌需要根据发育阶段和环境因素进行精确的调整.
- 智能农业需要准确的增长预测,及时干预以提高作物质量.
- 变化的环境条件给传统的灌管理带来了重大挑战.
研究的目的:
- 开发一种数据驱动的灌方法,以提高智能农业中的作物产量.
- 利用深度学习来准确预测作物生长状态和灌需求.
- 通过可控实验验证一种新的预测和干预策略.
主要方法:
- 开发了一个集成的深度学习架构,结合了Informer,长短期记忆 (LSTM) 网络和指数加权移动平均 (EWMA) 模型.
- 收集了大量的数据集来训练和优化深度学习模型.
- 进行了受控温室实验,以验证拟议战略的可靠性和实用性.
主要成果:
- 该模型准确地预测了灌需求,提前3-5天发出警告.
- 与传统的固定灌方法相比,灌频率显著降低.
- 与对照组相比,实验组的植物数量增加了410.0%,平均植物高度增加了21.8%.
结论:
- 拟议的数据驱动灌方法有效地提高了作物生长和产量.
- 综合深度学习方法为农业规划和管理提供了一个新的战略.
- 这种方法证明了优化智能农业灌实践的潜力.
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