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基于图像的机器学习模型用于定制的土壤水分管理
Yooan Kim1, Taehyeong Kim2,3, Sungyong Lee4
1Institute of Construction and Environmental Engineering, Seoul National University, Seoul, South Korea.
PloS one
|February 17, 2026
概括
智能农业可以通过人工智能进行改进. 该系统使用传感器和图像来监测单个植物的土壤水分,优化作物管理和产量.
科学领域:
- 农业工程 农业工程
- 人工智能在农业中的应用
- 精准农业 精准农业 精准农业
背景情况:
- 传统的智能农业采用统一的处理方法,由于微环境变化,无法满足单个作物的需求.
- 不高效的资源使用和降低的产量是由于忽视了植物特定要求造成的.
- 缺乏用于单个植物土壤监测的非侵入性方法,这阻碍了定制作物管理.
研究的目的:
- 开发一个基于人工智能的系统,用于定制的土壤水分管理.
- 允许在单个植物层面对土壤状况进行非侵入性监测.
- 通过植物特定数据提高智能农业效率和作物产量.
主要方法:
- 利用RGB图像和土壤传感器数据 (3厘米,10厘米,15厘米深度) 来模拟野生人参.
- 采用深度学习模型 (DenseNet121) 进行表面湿度预测.
- 应用随机森林回归用于更深层的土壤层湿度动态.
主要成果:
- 在表面湿度预测方面,DenseNet121实现了高精度 (R2=97.3%).
- 随机森林回归表明,对于更深层的土壤层,其表现强 (R2=90.6%).
- 使用表面图像数据验证了土壤湿度的非侵入性估计.
结论:
- 由人工智能驱动的图像和传感器分析使定制的土壤水分管理成为可能.
- 该系统支持智能农业中的数据驱动,特定植物的方法.
- 未来的工作应该涉及多样化的作物/土壤验证和光谱数据集成.
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