农业机器学习应用的网络基础设施:经验,分析和愿景
Lucas Waltz1, Sushma Katari1, Chaeun Hong2
1Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, OH, United States.
Frontiers in artificial intelligence
|February 7, 2025
概括
这项研究开发了农业的网络基础设施 (CI) 组件,使机器学习 (ML) 模型训练能够更快地使用多式联网数据. 这些创新解决了数据挑战,并加速了农业中的ML应用.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 的进步很快,但农业应用受到缺乏高质量,多样化的数据集和可重复使用的网络基础设施 (CI) 的限制.
- 现有的计算资源和ML算法是有能力的,但数据收集,处理和培训基础设施落后,阻碍了农业创新.
研究的目的:
- 通过探索使用多式联络数据集收集,处理和训练ML模型来解决农业数据方面的挑战.
- 提出以农业为重点的CI的愿景,以加快ML解决方案在农业部门的开发和部署.
主要方法:
- 在2023年生长季节收集了1兆字节 (TB) 的多模式数据,包括无人机系统 (UAS) 图像 (RGB和多谱) 和土壤/天气传感器数据.
- 专注于玉米和大豆作物,培训ML模型来预测作物生长阶段,土壤水分和最终产量.
- 开发了四个关键CI组件:UAS图像管道,表格数据管道,适应视觉变压器 (ViT) 模型架构和数据可视化原型.
主要成果:
- 创建了四个可重复使用的CI组件,旨在提高农业中的ML模型准确性.
- 无人机图像管道改善了处理时间和图像质量.
- 表格式数据管道从多个来源汇总和调整了数据,ViT适应纳入了领域专业知识,改善了数据信任和异常者识别.
结论:
- 开发的CI组件为农业中更高准确度的ML预测提供了一条途径.
- 需要进一步的工作来成熟这些组件,并在高性能计算 (HPC) 基础设施上实施它们.
- 关于如何最好地利用CI来满足农业界对加速ML应用开发的需求,仍然存在一些未解决的问题.
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