Plant diseases significantly impact food security and food safety. It was estimated that food production needs to increase by 50% to feed the projected 9.3 billion people by 2050. Yet, plant pathogens and pests are documented to cause up to 40% yield losses in major crops, including maize, rice, and wheat, resulting in annual worldwide economic losses of approximately US$220 billion. Yield losses due to plant diseases and pests are estimated to be 21.5% (10.1 to 28.1%) in wheat, 30.3% (24.6 to 40.9%) in rice, and 22.6% (19.5 to 41.4%) in maize. In March 2023, The American Phytopathological Society (APS) conducted a survey to identify and rank key challenges in plant pathology in the next decade. Phytopathology subsequently invited papers that address those key challenges in plant pathology, and these were published as a special issue. The key challenges identified include climate change effect on the disease triangle and outbreaks, plant disease resistance mechanisms and its applications, and specific diseases including those caused by Candidatus Liberibacter spp. and Xylella fastidiosa. Additionally, disease detection, natural and man-made disasters, and plant disease control strategies were explored in issue articles. Finally, aspects of open access and how to publish articles to maximize the Findability, Accessibility, Interoperability, and Reuse of digital assets in plant pathology were described. Only by identifying the challenges and tracking progress in developing solutions for them will we be able to resolve the issues in plant pathology and ultimately ensure plant health, food security, and food safety.

Unmanned aircraft systems are increasingly used in data-gathering operations for precision agriculture, with compounding benefits. Analytical processing of image data remains a limitation for applications. We implement an unsupervised machine learning technique to efficiently analyze aerial image data, resulting in a robust method for estimating plant phenotypes. We test this implementation in three settings: rice fields, a plant nursery, and row crops of grain sorghum and soybeans. We find that unsupervised subpopulation description facilitates accurate plant phenotype estimation without requiring supervised classification approaches such as construction of reference data subsets using geographic positioning systems. Specifically, we apply finite mixture modeling to discern component probability distributions within mixtures, where components correspond to spatial references (for example, the ground) and measurement targets (plants). Major benefits of this approach are its robustness against ground elevational variations at either large or small scale and its proficiency in efficiently returning estimates without requiring in-field operations other than the vehicle overflight. Applications in plant pathosystems where metrics of interest are spectral instead of spatial are a promising future direction.

Fusarium wilt of cotton, caused by the soilborne fungal pathogen Fusarium oxysporum f. sp. vasinfectum (FOV), occurs in regions of the United States where cotton (Gossypium spp.) is grown. Race 4 of this pathogen (FOV4) is especially aggressive, and does not require the co-occurrence of the root knot nematode (Meloidogyne incognita) to infect cotton. Its sudden appearance in far-west Texas in 2016 after many years of being restricted to California is of great concern, as is the threat of its continued spread through the cotton-producing regions of the United States. The aim of this research was to analyze the spatial variability of FOV4 inoculum density in the location where FOV4 is locally emerging, using quantitative and droplet digital PCR methods. Soil samples collected from a field with known FOV4 incidence in Fabens, Texas, were analyzed. Appreciable variation in inoculum density was found to occur at spatial scales smaller than the size of plots involved in cultivar trial research, and was spatially autocorrelated (Moran’s I, Z = 17.73, P < 0.0001). These findings indicate that, for cultivar trials, accounting for the spatial distribution of inoculum, either by directly quantifying it or through the use of densely distributed calibration checks, is important to the interpretation of results.

Management of disease affecting peanut in the southeastern United States has benefited from extensive field research identifying disease-associated risk factors since the 1990s. An assessment of risk factors associated with tomato spotted wilt (TSW), caused by tomato spotted wilt virus and spread exclusively by thrips, is available to growers through Peanut Rx, a tool developed to inform peanut management decisions. Peanut Rx provides an assessment of relative TSW risk as an index. The assessment provides information about the relative degree to which a field characterized by a specified suite of practices is at risk of crop loss caused by TSW. Loss results when infection occurs, and infection rates are determined, in part, by factors outside a grower’s control, primarily the abundance of dispersing, viruliferous thrips. In this study, we incorporated meteorological variables useful for predicting thrips dispersal, increasing the robustness of the Peanut Rx framework in relation to variation in the weather. We used data from field experiments and a large grower survey to estimate the relationships between weather and TSW risk mediated by thrips vectors, and developed an addition to Peanut Rx that proved informative and easy to implement. The expected temporal occurrence of major thrips flights, as a function of heat and precipitation, was translated into the existing risk-point system of Peanut Rx. Results from the grower survey further demonstrated the validity of Peanut Rx for guiding growers’ decisions to minimize risk of TSW.