Tar Spot Disease Quantification Using Unmanned Aircraft Systems (UAS) Data

Remote Sensing

Brenden Z. Lane¹  Da-Young Lee¹  Darcy E. P. Telenko¹  C. D. Cruz¹  Mariela Fernandez-Campos¹  A. P. Cruz¹  Carlos Gongora-Canul¹  Joshua Carpenter²  Akash Ashapure²  Jinha Jung²  Sungchan Oh² 

[1] Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA;Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA;

关键词: tar spot;    corn;    disease management;    remote sensing;    unmanned aircraft systems;   

DOI  :  10.3390/rs13132567

来源: DOAJ

【 摘 要 】

Tar spot is a foliar disease of corn characterized by fungal fruiting bodies that resemble tar spots. The disease emerged in the U.S. in 2015, and severe outbreaks in 2018 caused an economic impact on corn yields throughout the Midwest. Adequate epidemiological surveillance and disease quantification are necessary to develop immediate and long-term management strategies. This study presents a measurement framework that evaluates the disease severity of tar spot using unmanned aircraft systems (UAS)-based plant phenotyping and regression techniques. UAS-based plant phenotypic information, such as canopy cover, canopy volume, and vegetation indices, were used as explanatory variables. Visual estimations of disease severity were performed by expert plant pathologists per experiment plot basis and used as response variables. Three regression methods, namely ordinary least squares (OLS), support vector regression (SVR), and multilayer perceptron (MLP), were used to determine an optimal regression method for UAS-based tar spot measurement. The cross-validation results showed that the regression model based on MLP provides the highest accuracy of disease measurements. By training and testing the model with spatially separated datasets, the proposed regression model achieved a Lin’s concordance correlation coefficient (ρ_c) of 0.82 and a root mean square error (RMSE) of 6.42. This study demonstrated that we could use the proposed UAS-based method for the disease quantification of tar spot, which shows a gradual spectral response as the disease develops.

【 授权许可】

Unknown