...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Transactions of the ASABE >IN-FIELD VARIABILITY DETECTION AND SPATIAL YIELD MODELING FOR CORN USING DIGITAL AERIAL IMAGING
【24h】

IN-FIELD VARIABILITY DETECTION AND SPATIAL YIELD MODELING FOR CORN USING DIGITAL AERIAL IMAGING

机译：数字航空成像技术在玉米中的现场变异性检测和空间屈服模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

High-resolution color infrared (CIR) images acquired with an airborne digital camera were used to detect infield spatial variability in soil type, crop nutrient stress, and to analyze spatial variability in yield. Images were processed using an unsupervised learning (clustering) method. The clustered images were geo-referenced, and spatially analyzed using a GIS package. The image patterns in a processed image of bare soil matched well with soil type map with 76% accuracy. The CIR images of a cornfield indicated nitrogen stress areas from 75 days after planting (DAP). The CIR reflectance was better correlated to the yield after pollination of corn compared to the early images. The spatial variation in yield was linearly correlated to the spatial variation of individual reflectance bands (NIR, R, and G) as well as normalized intensity (NI) of CIR image. Spatial yield models on uncalibrated reflectance bands of image could predict 76 to 98% of yield variation in each field. A linear regression model on NI developed from one field image predicted yield with an accuracy of 55 to 91% in different fields and seasons. Digital aerial imaging proves to be a promising tool for obtaining spatial in-field variability in the crop field for site-specific management and yield prediction.
机译：使用机载数码相机获取的高分辨率彩色红外（CIR）图像用于检测土壤类型的田间空间变异，作物养分胁迫，并分析产量的空间变异。使用无监督学习（聚类）方法处理图像。将聚类的图像进行地理参考，并使用GIS软件包进行空间分析。裸土处理后的图像图案与土壤类型图非常吻合，准确度达76％。玉米田的CIR图像指示了种植后75天（DAP）的氮胁迫区域。与早期图像相比，CIR反射率与玉米授粉后的产量更好相关。产量的空间变化与单个反射带（NIR，R和G）的空间变化以及CIR图像的归一化强度（NI）线性相关。未校准图像反射带上的空间产量模型可以预测每个场中76％到98％的产量变化。从一个田间图像得出的关于NI的线性回归模型预测了不同田间和季节的产量，准确度为55％至91％。事实证明，数字航空成像是一种有前途的工具，可用于获得作物田间的空间内田间变异性，以进行特定地点的管理和产量预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号