Novel Technologies for the detection of Fusarium head blight disease and airborne inoculum

Abstract

Many pathogens are dispersed by airborne spores, which can vary in space and time. We can use air sampling integrated with suitable diagnostic methods to give a rapid warning of inoculum presence to improve the timing of control options, such as fungicides. Air sampling can also be used to monitor changes in genetic traits of pathogen populations such as the race structure or frequency of fungicide resistance. Although some image-analysis methods are possible to identify spores, in many cases, species-specific identification can only be achieved by DNA-based methods such as qPCR and LAMP and in some cases by antibody-based methods (lateral flow devices) and biomarker-based methods ('electronic noses' and electro-chemical biosensors). Many of these methods also offer the prospect of rapid on-site detection to direct disease control decisions. Thresholds of spore concentrations that correspond to a disease risk depend on the sampler (spore-trap) location (whether just above the crop canopy, on a UAV or drone, or on a tall building) and also need to be considered with weather-based infection models. Where disease control by spore detection is not possible, some diseases can be detected at early stages using optical sensing methods, especially chlorophyll fluorescence. In the case of Fusarium infections on wheat, it is possible to map locations of severe infections, using optical sensing methods, to segregate harvesting of severely affected areas of fields to avoid toxins entering the food chain. This is most useful where variable crop growth or microclimates within fields generate spatially variable infection, i.e. parts of fields that develop disease, while other areas have escaped infection and do not develop any disease.

Wheat blast: from its origins in South America to its emergence as a global threat

“…Similar predictive models should be developed for other countries facing wheat blast outbreaks, including Bolivia, Paraguay, Bangladesh and India. The major advantage of using a model like Sisalert based on the automated collection of weather data is to provide a warning of an imminent risk of wheat blast infection (West et al ., ; West and Kimber, ). This alert enables real‐time decision‐making for fungicide applications.…”

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

“…However, multispectral and RGB imagery are used to detect only infected ears with typical symptoms via sophisticated analysis of the image under uniform illumination conditions. Spectroscopy and imaging platforms for tractors, UAVs, aircraft and satellites are current innovative technologies for mapping disease in wild fields [16].…”

A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

“…RDVI and OSAVI were both established to improve the sensitivity of NDVI to soil backgrounds [20]. The good performances of these indices indicated that even if FHB occurred at the flowering stage (Feekes 10.5) through to the milk-ripening stage (Feekes 11.1) [50], soil background still had an effect on the canopy spectral reflectance. The red-edge VIs, i.e., NREDI1, NREDI2, and NREDI3, were modifications of NDVI with red-edge1, red-edge2, and red-edge3, respectively, and the R 2 of models developed from these indices were 0.46, 0.42, and 0.21 respectively.…”