Early detection and rapid response are crucial to avoid severe epidemics of exotic pathogens. However, most detection methods (molecular, serological, chemical) are logistically limited for large-scale survey of outbreaks due to intrinsic sampling issues and laboratory throughput. Evaluation of 10 canines trained for detection of a severe exotic phytobacterial arboreal pathogen, Candidatus Liberibacter asiaticus ( C Las), demonstrated 0.9905 accuracy, 0.8579 sensitivity, and 0.9961 specificity. In a longitudinal study, cryptic C Las infections that remained subclinical visually were detected within 2 wk postinfection compared with 1 to 32 mo for qPCR. When allowed to interrogate a diverse range of in vivo pathogens infecting an international citrus pathogen collection, canines only reacted to Liberibacter pathogens of citrus and not to other bacterial, viral, or spiroplasma pathogens. Canines trained to detect C Las-infected citrus also alerted on C Las-infected tobacco and periwinkle, C Las-bearing psyllid insect vectors, and C Las cocultured with other bacteria but at C Las titers below the level of molecular detection. All of these observations suggest that canines can detect C Las directly rather than only host volatiles produced by the infection. Detection in orchards and residential properties was real time, ∼2 s per tree. Spatiotemporal epidemic simulations demonstrated that control of pathogen prevalence was possible and economically sustainable when canine detection was followed by intervention (i.e., culling infected individuals), whereas current methods of molecular (qPCR) and visual detection failed to contribute to the suppression of an exponential trajectory of infection.

For millennia humans have benefitted from application of the acute canine sense of smell to hunt, track and find targets of importance. In this report, canines were evaluated for their ability to detect the severe exotic phytobacterial arboreal pathogen Xanthomonas citri pv. citri (Xcc), which is the causal agent of Asiatic citrus canker (Acc). Since Xcc causes only local lesions, infections are non-systemic, limiting the use of serological and molecular diagnostic tools for field-level detection. This necessitates reliance on human visual surveys for Acc symptoms, which is highly inefficient at low disease incidence, and thus for early detection. In simulated orchards the overall combined performance metrics for a pair of canines were 0.9856, 0.9974, 0.9257 and 0.9970, for sensitivity, specificity, precision, and accuracy, respectively, with 1–2 s/tree detection time. Detection of trace Xcc infections on commercial packinghouse fruit resulted in 0.7313, 0.9947, 0.8750, and 0.9821 for the same performance metrics across a range of cartons with 0–10% Xcc-infected fruit despite the noisy, hot and potentially distracting environment. In orchards, the sensitivity of canines increased with lesion incidence, whereas the specificity and overall accuracy was >0.99 across all incidence levels; i.e., false positive rates were uniformly low. Canines also alerted to a range of 1–12-week-old infections with equal accuracy. When trained to either Xcc-infected trees or Xcc axenic cultures, canines inherently detected the homologous and heterologous targets, suggesting they can detect Xcc directly rather than only volatiles produced by the host following infection. Canines were able to detect the Xcc scent signature at very low concentrations (10,000× less than 1 bacterial cell per sample), which implies that the scent signature is composed of bacterial cell volatile organic compound constituents or exudates that occur at concentrations many fold that of the bacterial cells. The results imply that canines can be trained as viable early detectors of Xcc and deployed across citrus orchards, packinghouses, and nurseries.

Identification and separation of poultry carcasses contaminated by feces and/or crop ingesta are very important to protect the consumer from a potential source of food poisoning. A transportable hyperspectral imaging system was developed to detect fecal and ingesta contamination on the surface of poultry carcasses. Detection algorithms used with the imaging system were developed from visible/near infrared monochromator spectra and with contaminates from birds fed a corn/soybean meal diet. The objectives of this study were to investigate using regions of interest reflectance spectra from hyperspectral images to determine optimal wavelengths for fecal detection algorithms from images of birds fed corn, wheat and milo diets. Spectral and spatial data between 400 and 900 nm with a 1.0 nm spectral resolution were acquired from uncontaminated and fecal and ingesta contaminated poultry carcasses. Regions of interest (ROIs) were defined for fecal and ingesta contaminated and uncontaminated skin (i.e. breast, thigh, and wing). Average reflectance spectra of the ROIs were extracted for analysis. Reflectance spectra of contaminants and uncontaminated skin differed. Spectral data pre-processing treatments with a single-term, linear regression program to select wavelengths for optimum calibration coefficients to detect contamination were developed. Fecal and ingesta detection models, specifically a quotient of 2 and/or 3-wavelengths was 100% successful in classification of contaminates.

Species of the Colletotrichum acutatum complex cause postbloom fruit drop of citrus and anthracnose fruit rot of strawberries. C. acutatum produces acervuli in diseased citrus flowers and strawberry fruit, surviving asymptomatically on vegetative tissues. Previous studies have suggested that dispersal mechanisms other than windblown rain may be involved in the dispersal of conidia of C. acutatum sensu lato. Our hypothesis is that wind alone may play a role in dispersal, especially for inocula surviving on vegetative tissue. Our objectives were to study the dispersal pattern of C. acutatum sensu lato from strawberry fruit and leaves and citrus flowers and leaves by placing semiselective media at known distances away from inoculum sources in laminar flow and turbulent wind tunnels. The number of C. acutatum sensu lato colony-forming units on each plate was correlated with the distance from the inoculum sources. Conidia were dispersed up to 15 m in wind speeds greater than 10 m/s, but observed dispersal patterns were not described by the models we tested. A negative exponential model adequately described the dispersal gradient of the inoculum downwind, particularly for the dispersal gradient in turbulent wind. Dispersal in rain splash and wind was limited to short distances. Our data describe how conidia spread from inoculum sources and for the first time describe how the dispersal of C. acutatum sensu lato secondary conidia occurs from citrus and strawberry leaves. Knowledge of the dispersal of conidia of C. acutatum sensu lato can provide valuable information on epidemic development and, thus, approaches for disease management.