Abstract
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.
Highlights
Rice is one of the most important food crops in the world, providing a major source of nourishment for over half the world population
A commercial unmanned aerial vehicle (UAV) equipped with a high-resolution RGB and multispectral cameras was used to capture imagery data
Ground truth data of sheath blight (ShB) severity and Normalized Difference Vegetation Index (NDVI) were measured for comparisons
Summary
Rice is one of the most important food crops in the world, providing a major source of nourishment for over half the world population. Detection of rice ShB using an UAS with high-resolution color and multispectral imaging by Rhizoctonia solani AG1-1A, are among the most important factors limiting rice production worldwide [1]. The ShB disease usually develops in the later tillering or early internode elongation stage of rice. ShB spreads from plant to plant through the growth of the fungus and usually forms in a circular pattern in the field [2]. Under favorable conditions, this disease spreads quickly to top plant parts, causing lodgings of the plants. ShB has become the second most economically-important disease in rice in the world [2]
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