Anhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei, Anhui, China.
Texas A&M AgriLife Research Center, Texas A&M University System, Beaumont, Texas, United States of America.
PLoS One. 2018 May 10;13(5):e0187470. doi: 10.1371/journal.pone.0187470. eCollection 2018.
Detection and monitoring are the first essential step for effective management of sheath blight (ShB), a major disease in rice worldwide. Unmanned aerial systems have a high potential of being utilized to improve this detection process since they can reduce the time needed for scouting for the disease at a field scale, and are affordable and user-friendly in operation. In this study, a commercialized quadrotor unmanned aerial vehicle (UAV), equipped with digital and multispectral cameras, was used to capture imagery data of research plots with 67 rice cultivars and elite lines. Collected imagery data were then processed and analyzed to characterize the development of ShB and quantify different levels of the disease in the field. Through color features extraction and color space transformation of images, it was found that the color transformation could qualitatively detect the infected areas of ShB in the field plots. However, it was less effective to detect different levels of the disease. Five vegetation indices were then calculated from the multispectral images, and ground truths of disease severity and GreenSeeker measured NDVI (Normalized Difference Vegetation Index) were collected. The results of relationship analyses indicate that there was a strong correlation between ground-measured NDVIs and image-extracted NDVIs with the R2 of 0.907 and the root mean square error (RMSE) of 0.0854, and a good correlation between image-extracted NDVIs and disease severity with the R2 of 0.627 and the RMSE of 0.0852. Use of image-based NDVIs extracted from multispectral images could quantify different levels of ShB in the field plots with an accuracy of 63%. These results demonstrate that a customer-grade UAV integrated with digital and multispectral cameras can be an effective tool to detect the ShB disease at a field scale.
检测和监测是有效管理稻瘟病（ShB）的首要步骤，ShB 是全球范围内水稻的主要病害。由于无人飞行器（UAV）系统可以减少在田间范围内对该疾病进行侦察所需的时间，并且在操作上具有经济实惠且易于使用的特点，因此具有很高的潜力可用于改善该检测过程。在这项研究中，使用配备数字和多光谱相机的商业化四旋翼 UAV 来捕获具有 67 个水稻品种和优良品系的研究小区的图像数据。然后处理和分析收集的图像数据，以描述 ShB 的发展并量化田间的不同疾病水平。通过对图像进行颜色特征提取和颜色空间变换，发现颜色变换可以定性地检测田间 ShB 感染区域。然而，它在检测不同疾病水平方面的效果较差。然后从多光谱图像中计算了五个植被指数，并收集了疾病严重程度和 GreenSeeker 测量的 NDVI（归一化差异植被指数）的地面实况。关系分析的结果表明，地面测量的 NDVI 与从图像中提取的 NDVI 之间存在很强的相关性，R2 为 0.907，均方根误差（RMSE）为 0.0854，并且图像提取的 NDVI 与疾病严重程度之间存在很好的相关性，R2 为 0.627，RMSE 为 0.0852。使用从多光谱图像中提取的基于图像的 NDVI 可以以 63％的精度量化田间不同水平的 ShB。这些结果表明，集成数字和多光谱相机的客户级 UAV 可以成为在田间范围内检测 ShB 疾病的有效工具。
