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 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.

Plant diseases impair the yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, because traditional methods to measure plant diseases are resource intensive, which often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral, and thermal imaging, as well as light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, airplanes, and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modeling have rarely been integrated. To address this gap, we first review the state of the art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modeling. We discuss how optical sensing can inform epidemiological modeling by improving model selection and parameterization and providing accurate maps of host plants. Epidemiological modeling can inform optical sensing by boosting measurement accuracy, improving data interpretation, and optimizing sensor deployment. We consider outstanding challenges in (A) identifying particular diseases; (B) data availability, quality, and resolution; (C) linking optical sensing and epidemiological modeling; and (D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose standardizing methods and protocols for optical sensing of plant health and developing open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work. [ ] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

The spatiotemporal progress of wheat blast (WB) epidemics within the plant canopy remains poorly known due to complex pathogen–host–environment interactions. Although deterministic methods are popular and useful, robust stochastic methods, such as generalized additive models for location, scale and shape (GAMLSS) and probability matrix or Markov transition model (MTM), have seldom been used to analyse plant disease epidemics. Hence, both methods were employed to derive valuable insights into WB epidemiology at the vertical canopy level. We conducted experiments in three climatic zones in Bolivia, using three wheat cultivars, with disease data corresponding to different canopy positions (lower, L; middle, M; flag leaf, F; and spike, S). Using WB severity data (AUDPC, progress rate and maximum severity [ K _max ]), we implemented the GAMLSS and MTM to test our hypothesis that WB is affected by host resistance, location and canopy level. Results showed that the AUDPC, progress rate and K _max differed across sites, cultivars and canopy positions. Clearly, L and M canopies showed a lower progress rate than F and S. The disease showed an ascending movement from L and M canopies to F and S across locations and cultivars. However, descending transitions also occurred from M to L early or F to M canopy later in the season. Both ascending and descending movements can arise at a single state or several recurrent states, indicating indirect evidence of autoinfection within the canopy before and after spike emergence. Our findings contribute knowledge to improve monitoring and managing WB.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present the Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1), without the need to empirically search for optimal decision-making input parameters (DMIPs) while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar spot–like region proposals for a given input corn leaf Red-Green-Blue (RGB) image and (ii) a pretrained convolutional neural network (CNN) classifier identifying true tar spot stromata from the region proposals. To demonstrate the enhanced performance of the SCDA v2, we used datasets of RGB images of corn leaves from field (low, middle, and upper canopies) and glasshouse conditions under variable environments, exhibiting different tar spot severities at various corn developmental stages. Various accuracy analyses (F1 score, linear regression, and Lin’s concordance correlation) showed that SCDA v2 had a greater agreement with the reference data (human visual annotation) than SCDA v1. SCDA v2 achieved 73.7% mean Dice values (overall accuracy) compared with 30.8% for SCDA v1. The enhanced F1 score primarily resulted from eliminating overestimation cases using the CNN classifier. Our findings indicate the promising potential of SCDA v2 for glasshouse and field-scale applications, including tar spot phenotyping and surveillance projects. [ ] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Background. Jatropha curcas L. is a crop whose oil can be converted to biodiesel. However, there is lack of varieties with high yields that limiting commercial production of the crop. An alternative is to obtain in vitro plants from unpollinated female gametophytes is known as gynogenesis which can provide an alternative to produce varieties with increased seed production. Objective. The objective was to establish a protocol for obtaining in vitro plants from unfertilized ovules of Jatropha curcas L. ovules. Methodology. The ovules were extracted from unfertilized inflorescences pretreated for 0, 1, 3 and 7 days at 2 to 5 ° C. Three combinations of plant growth regulator treatments were applied to culture media for induction of gynogenic calli. Gynogenic calli were cultured on MS basal medium with BAP and IBA for development of green, friable. The gynogenic calli from the development treatment were transferred to experiments to determine a regeneration treatment. The gynogenic plantlets were transferred to different treatments for root development. Results. The results show that pretreatment of inflorescences at 2 to 5°C had no significant effect on the number of ovules that formed friable white calli. Induction of gynogenic friable white calli occurred in the dark conditions, on half-strength Murashige and Skoog basal medium (½ MS) supplemented with two combinations of growth regulators: (i) 5.37 µM naphthalene acetic acid (NAA) combined with 6.65 µM 6-benzylaminopurine (BAP) and, (ii)8.05 µM NAA with 22.09 µM BAP. Development of green, friable, gynogenic calli under light–dark conditions was possible under treatment with complete Murashige and Skoog (1962) (MS) basal medium with 6.66 µM BAP and 4.9 µM indole-butyric acid (IBA).Friable green callus formed gynogenic embryos on MS basal medium supplemented with 22.09 µMBAP and 3.40 µM paclobutrazol (PBZ), embryos regeneration occurred in photoperiod conditions. Embryos were able to develop and convert to plantlets in the treatment on MS basal medium containing 2.22 µM BAP and 0.28 µM of indole-3-acetic acid (IAA). Root development of plantlets occurred in ½ MS basal medium with 18.65 µM IBA. Implications. A protocol of in vitro gynogenesis in Jatropha curcas would contribute to the improvement of its cultivation, reducing the time required for the generation of pure lines that would allow us to obtain varieties with increased seed production. Conclusions. The work presented here describes a reproducible protocol to produce plants in vitro from unfertilized ovules of Jatropha curcas L. This methodology will facilitate to obtain homozygotic lines with significant reduction in the time required by conventional methods.