• 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 >UAS imaging for automated crop lodging detection: a case study over an experimental maize field
【24h】

UAS imaging for automated crop lodging detection: a case study over an experimental maize field

机译：用于自动农作物倒伏检测的UAS成像：一个实验性玉米田的案例研究

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

Lodging has been recognized as one of the major destructive factors for crop quality and yield, particularly in maize. A variety of genetic and environmental contributing causes, e.g. disease and/or pest, weather conditions, excessive nitrogen, and high plant density, may interact leading to lodging before harvesting. Traditional lodging detection strategies mainly rely on ground data collection, which is insufficient in efficiency and accuracy. To address that problem, this research focuses on the use of unmanned aircraft systems (UAS) for detection of crop lodging. The study was conducted over an experimental maize field at the Texas A&M AgriLife Research and Extension Center at Corpus Christi, Texas, during the growing season of 2016. Nadir-view images of the maize field were taken by small UAS platforms equipped with consumer grade RGB and NIR cameras on a per week basis, enabling a timely observation of the plant growth. 3D structural information of the plants was reconstructed using structure-from-motion photogrammetry. The structural information was then applied to calculate crop height and growth. A grid-based lodging method was proposed afterwards. Ground truth data of lodging was collected on a per row basis and used for fair assessment and tuning of the detection algorithm. Results show the UAS-measured height correlates tightly with the ground-measured height. More importantly, the results proved that the proposed UAS-based lodging detection method has great potential to accurately reflect lodging severity in the open crop field environment.
机译：倒伏已被认为是影响作物质量和单产的主要破坏因素之一，特别是在玉米中。多种遗传和环境起因，例如疾病和/或害虫，天气条件，过量的氮和高植物密度可能相互作用，导致收割前倒伏。传统的倒伏检测策略主要依靠地面数据收集，效率和准确性不足。为了解决该问题，本研究着重于使用无人机系统（UAS）探测农作物倒伏。这项研究是在2016年生长季节期间在德克萨斯州科珀斯克里斯蒂市的Texas A＆M AgriLife研究和推广中心的一个实验性玉米田上进行的。玉米田的天底视图图像是由配备了消费级RGB的小型UAS平台拍摄的以及每周使用近红外摄像头，可以及时观察植物的生长。使用从运动结构摄影测量法重建植物的3D结构信息。然后将结构信息应用于计算作物高度和生长。随后提出了一种基于网格的住宿方法。逐行收集倒塌的地面真实数据，并用于公平评估和调整检测算法。结果表明，UAS测得的高度与地面测得的高度紧密相关。更重要的是，结果证明了基于UAS的倒伏检测方法具有很大的潜力，可以在空旷的田间环境中准确反映倒伏的严重程度。

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