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 significant yield losses of approximately 4.5 million t. 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 known management strategies as a resource for understanding the pathosystem. This will in turn guide current and future research and aid in the development of effective management strategies for this disease.

Tar spot is a foliar disease of corn threatening production across the Americas. The disease was first documented in Mexico in 1904 and is now present in 15 additional countries throughout Central America, South America, and the Caribbean. Researchers and growers in Central America, South America, and the Caribbean consider tar spot to be a disease complex caused by multiple fungal pathogens. When environmental conditions are conducive for infection, these regions have experienced yield losses that can reach up to 100%. In 2015, tar spot was detected in the United States for the first time in Illinois and Indiana. Since that time tar spot has spread across the U.S. corn-growing region, and the disease has been found in Florida, Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, Pennsylvania, and Wisconsin. In 2020, tar spot was also found in southwest Ontario, Canada. Losses in the United States due to tar spot totaled an estimated 241 million bushels from 2018 to 2020. With the potential to continue to spread across the U.S. corn-growing states, much greater losses could result when environmental conditions are conducive.

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 (W_SB) are quite distinct in the field, symptoms on leaves (W_LB) are rarely reported because they are usually inconspicuos. Two field experiments were conducted in Bolivia to characterize the change in W_LB and W_SB intensity over time and determine whether multispectral imagery can be used to accurately assess W_SB. Disease progress curves (DPCs) were plotted from W_LB and W_SB data, and regression models were fitted to describe the nature of W_SB epidemics. W_LB incidence and severity changed over time; however, the mean W_LB severity was inconspicuous before wheat began spike emergence. Overall, both Gompertz and logistic models helped to describe W_SB intensity DPCs fitting classic sigmoidal shape curves. Lin’s concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of W_SB 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 W_SB severity can be quantified using a digital approach based on nongreen pixels. Quantification was precise (0.96 < r> 0.83) and accurate (0.92 < ρ > 0.69) at moderately low to high visual W_SB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of W_LB and W_SB at earlier stages.

This study aimed to evaluate the performance of fungicides against wheat head blast (WHB) under various environments and to determine scenarios best suited for fungicide applications. Field experiments were conducted at 23 environments in Brazil and Bolivia from 2012 to 2015. Data from all trials within the same country were combined for estimating mean WHB control efficacy and yield benefits from using a set of fungicides. Experiments were classified, based on disease index in the check treatment, as having low (CDI = 10), moderate (CDI = 40), and high (CDI = 70) disease pressure and this variable was tested as a covariate in the model. In Brazil, greater disease reduction and yield increase, in trials with moderate to high disease pressure, were obtained when using mancozeb-based fungicides, but with yield gains below 1276 kg/ha. In Bolivia, all fungicides reduced the disease at moderate to high disease pressure, but specific QoI + DMI premixes led to higher yield gains averaging 1834 kg/ha. Based on the evidence provided, we concluded that current WHB chemical strategies could have radically different results depending on country and disease pressure. Although WHB chemical control can be effective even under environmental conditions that favor the disease, integrated management strategies should be explored. Our results are useful for aiding decisions on fungicide application and identifying priorities for future research.

Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.

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.