Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning

IntroductionTar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion.MethodsUAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models.Results and discussionThe developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin’s concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields

Temporal Dynamics of Wheat Blast Epidemics and Disease Measurements Using Multispectral Imagery

Wheat blast is a devastating disease caused by the Triticum pathotype of Magnaporthe oryzae (MoT). MoT is capable of infecting leaves and spikes of wheat. Although symptoms of spike blast (WSB) are quite distinct in the field, symptoms on leaves (WLB) are rarely reported since they are usually less noticeable. Two field experiments were conducted in Bolivia to characterize the change in WLB and WSB intensity over time and determine if multispectral imagery can be used to accurately assess WSB. Disease progress curves (DPCs) were plotted from WLB and WSB data, and regression models were fitted to describe the nature of WsB epidemics. Although WLB severity was low during the vegetative stages, there was a bimodal shape when WLB incidence DPCs were plotted. The Gompertz model best described WSB intensity change over time in both inoculated and non-inoculated plots from both locations (R2=0.94-0.98; RMSE=0.16-0.58). Lin’s concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of WSB intensity and to estimate accuracy, and reliability. Our findings suggest that the change of wheat blast intensity in a susceptible host population over time does not follow a pattern of a monocyclic epidemic. We have also demonstrated that WSB severity can be quantified using non-green vegetation pixels reliably (0.91-0.960.68-0.83) and accurately (0.86-0.920.56-0.71) at moderately-low-to-high visual WSB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of WLB and WSB at earlier stages

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

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 (ρc) 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

Temporal Dynamics of Wheat Blast Epidemics and Disease Measurements Using Multispectral Imagery.

Wheat blast is a devastating disease caused by the Triticum pathotype of Magnaporthe oryzae. M. oryzae Triticum is capable of infecting leaves and spikes of wheat. Although symptoms of wheat spike blast (WSB) are quite distinct in the field, symptoms on leaves (WLB) are rarely reported because they are usually inconspicuos. Two field experiments were conducted in Bolivia to characterize the change in WLB and WSB intensity over time and determine whether multispectral imagery can be used to accurately assess WSB. Disease progress curves (DPCs) were plotted from WLB and WSB data, and regression models were fitted to describe the nature of WSB epidemics. WLB incidence and severity changed over time; however, the mean WLB severity was inconspicuous before wheat began spike emergence. Overall, both Gompertz and logistic models helped to describe WSB intensity DPCs fitting classic sigmoidal shape curves. Lin's concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of WSB intensity and to estimate accuracy and precision. Our findings suggest that the change of wheat blast intensity in a susceptible host population over time does not follow a pattern of a monocyclic epidemic. We have also demonstrated that WSB severity can be quantified using a digital approach based on nongreen pixels. Quantification was precise (0.96 0.83) and accurate (0.92 0.69) at moderately low to high visual WSB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of WLB and WSB at earlier stages

Tar Spot: An Understudied Disease Threatening Corn Production in the Americas

Tar spot of corn has been a major foliar disease in several Latin American countries since 1904. In 2015, tar spot was first documented in the United States and has led to yield losses of approximately 4.5 million t annually. Tar spot is caused by an obligate pathogen, Phyllachora maydis, and thus requires a living host to grow and reproduce. Due to its obligate nature, biological and epidemiological studies are limited and impact of disease in corn production has been understudied. Here we present the current literature and gaps in knowledge of tar spot of corn in the Americas, its etiology, distribution, impact and management strategies as a resource for understanding the pathosystem. This review is intended to guide current and future research and aid in the development of more effective management strategies for this disease.This is a manuscript of an article published as Valle-Torres, J., T. J. Ross, D. Plewa, M. C. Avellaneda, J. Check, M. I. Chilvers, A. P. Cruz et al. "Tar spot: An understudied disease threatening corn production in the Americas." Plant disease 104, no. 10 (2020): 2541-2550. doi:10.1094/PDIS-02-20-0449-FE. Posted with permission