在 LUCAS 上使用深度学习进行作物识别作物封面照片
Momchil Yordanov1, Raphaël d'Andrimont2, Laura Martinez-Sanchez2
1SEIDOR Consulting S.L., 08500 Barcelona, Spain.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
计算机视觉精确地从近距离照片中识别主要的欧洲作物,使用最大的现场数据集. 这种方法通过利用土地利用覆盖面积框架调查 (LUCAS) 数据获得与政策相关的见解来加强农业监测.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
背景情况:
- 高质量的现场数据对于农业中的地球观测至关重要.
- 传统的现场测量是资源密集的.
- 计算机视觉为自动作物识别提供了一个潜在的解决方案.
研究的目的:
- 用现场照片对作物识别的计算机视觉模型进行基准测试.
- 利用来自土地利用覆盖面积框架调查 (LUCAS) 的最大的多年标记近距离照片数据集.
- 为农业政策提供及时准确的特定作物信息.
主要方法:
- 利用了169,460个标记的近距离作物图像 (2006-2018) 的数据集.
- 在后处理中使用MobileNet的超参数化和信息理论.
- 集成的作物日历来识别成熟的作物阶段.
主要成果:
- 最好的模型在8,642张测试图像中获得了0.75的宏F1 (M-F1).
- 信息理论指标的性能提高了6%.
- 使用最小的辅助数据实现了0.82的最佳M-F1.
结论:
- 计算机视觉可以有效地从近距离图像中识别主要作物.
- 卢卡斯数据集对于培训和验证农业监测模型非常有价值.
- 方法论证明了与政策相关的作物识别潜力,使用最小的外部数据.
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