• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Autonomous air and ground sensing systems for agricultural optimization and phenotyping II >Distinguishing plant population and variety with UAV-derived vegetation indices
【24h】

Distinguishing plant population and variety with UAV-derived vegetation indices

机译：用无人机衍生的植被指数区分植物种群和品种

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

Variety selection and seeding rate are two important choice that a peanut grower must make. High yielding varieties can increase profit with no additional input costs, while seeding rate often determines input cost a grower will incur from seed costs. The overall purpose of this study was to examine the effect that seeding rate has on different peanut varieties. With the advent of new UAV technology, we now have the possibility to use indices collected with the UAV to measure emergence, seeding rate, growth rate, and perhaps make yield predictions. This information could enable growers to make management decisions early in the season based on low plant populations due to poor emergence, and could be a useful tool for growers to use to estimate plant population and growth rate in order to help achieve desired crop stands. Red-Green-Blue (RGB) and near-infrared (NIR) images were collected from a UAV platform starting two weeks after planting and continued weekly for the next six weeks. Ground NDVI was also collected each time aerial images were collected. Vegetation indices were derived from both the RGB and NIR images. Greener area (GGA- the proportion of green pixels with a hue angle from 80° to 120°) and a* (the average red/green color of the image) were derived from the RGB images while Normalized Differential Vegetative Index (NDVI) was derived from NIR images. Aerial indices were successful in distinguishing seeding rates and determining emergence during the first few weeks after planting, but not later in the season. Meanwhile, these aerial indices are not an adequate predictor of yield in peanut at this point.
机译：品种选择和播种率是花生种植者必须做出的两个重要选择。高产品种无需增加投入成本即可增加利润，而播种率通常决定了种植者从种子成本中产生的投入成本。这项研究的总体目的是检验播种率对不同花生品种的影响。随着新无人机技术的出现，我们现在有可能使用通过无人机收集的指标来测量出苗率，播种率，生长率，并可能做出产量预测。该信息可以使种植者在季节初期基于出苗率低而植物种群少的情况下做出管理决策，并且可以成为种植者用来估算植物种群和生长率的有用工具，以帮助实现所需的农作物产量。种植后两周开始从无人机平台收集红绿蓝（RGB）和近红外（NIR）图像，并在接下来的六周每周进行一次。每次收集航拍图像时，也会收集地面NDVI。植被指数来自RGB和NIR图像。从RGB图像中获得较绿的区域（GGA-色相角为80°至120°的绿色像素的比例）和a *（图像的平均红色/绿色），而归一化差分营养指数（NDVI）为来自NIR图像。空中指数能够成功地区分播种率并确定播种后头几周内的出苗率，但并非本季后期。同时，这些航空指数目前还不足以预测花生的单产。

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