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Significance Exotic infectious pathogens, like citrus huanglongbing (HLB), are increasingly introduced into agrosystems. Early detection is the key to mitigating their destructive effects. Human visual assessment is insufficiently sensitive to detect new plant infections in a responsive timeframe, and molecular assays are expensive and not easily deployable over large crop landscapes. We turned to detector dogs, an ancient technology, which can rapidly survey large plantings without laborious sample collection or laboratory processing. Dogs detected infections (>99% accuracy) weeks to years prior to visual survey and molecular methods and were highly specific, accurately discriminating target pathogens from other pathogens. Epidemiological models indicated that dogs were more effective and economical than current early detection methods for sustainable disease control., 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 (CLas), demonstrated 0.9905 accuracy, 0.8579 sensitivity, and 0.9961 specificity. In a longitudinal study, cryptic CLas 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 CLas-infected citrus also alerted on CLas-infected tobacco and periwinkle, CLas-bearing psyllid insect vectors, and CLas cocultured with other bacteria but at CLas titers below the level of molecular detection. All of these observations suggest that canines can detect CLas 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.

Asiatic citrus canker (ACC, caused by Xanthomonas citri subsp. citri, [Xcc]) is a serious disease of citrus, reducing yield and impacting marketability of fresh fruit. ACC has phytosanitary implications for fresh fruit trade. Lesions on fruit, leaves, and shoots in an orchard of ACC-susceptible grapefruit were monitored for production of Xcc from June to the following January in 2009 and 2010. Lesions on fruit post-harvest either packingline-treated or not treated were also monitored for production of Xcc on fruit harvested in November 2013 and January 2014. Shoot lesions generally produced fewer bacteria and had a smaller proportion of active lesions compared to leaf and fruit lesions. As the season progressed the proportion of active lesions on fruit and shoots gradually declined. Bacterial flux density (BFD) in surviving lesions was >103 on all organs at all sample times. A window-pane analysis revealed weather variables associated with bacterial survival within the lesion. The packingline treatment had no effect in reducing the proportion of active lesions, and had only a small, transient effect reducing BFD (~1.5 log units). Lesion size affected survival – small lesions (

Studies were conducted to evaluate treatments that reduce survival and attachment of Diaphorina citri nymphs on infested curry leaves (Bergera koenigii). Decontamination of curry leaves infested with D. citri in relation to disinfectant (none or Pro-San), temperature (0, 40, and 50°C), and treatment duration (0, 5, 10, and 20 min) was examined using a split-split plot design. Experiments were performed three times. Treatment duration did not significantly affect D. citri nymph survival or removal (P > 0.2). Temperature and disinfectant each significantly affected D. citri nymph survival and removal (P < 0.031). The interaction of temperature and disinfectant was significant with respect to nymph survival (P < 0.0001) but did not significantly affect removal (P = 0.4589). Tissue damage was significantly affected by temperature (P = 0.0056), duration (P = 0.0023), the interaction of temperature and duration (P = 0.0320), and the interaction of disinfectant, temperature, and duration (P = 0.0410). Of the treatments resulting in 100% D. citri nymph mortality on infested curry leaves, 40°C for 5 min with Pro-San was accompanied with the least proportion of curry leaf tissue damage (0.14 greater than untreated control, P = 0.25). Results from these studies may be useful in formulation of future regulatory policies regarding trade of citrus foliage, especially those used as condiments.

Studies were conducted to evaluate treatments that reduce recovery of Xanthomonas citri subsp. citri (Xcc) in infected grapefruit leaves. To investigate effects of temperature (0, 10, 40, and 50 °C), disinfectant (none or Pro-San), and treatment duration (0, 2, 10, and 20 min) on survival of Xcc in vitro, a split–split plot experimental design was utilized. Recovery of Xcc in vitro in the absence of Pro-San decreased with increasing treatment duration at 50 °C but not at temperatures

Fourier transform infrared (FTIR) spectroscopy has the ability to quickly identify the presence of specific carbohydrates in plant materials. The presence of the disease huanglongbing (HLB) in the leaves of infected citrus plants has a distinctive spectrum that can be used to distinguish an infected plant from a healthy plant. However, many citrus diseases display similar visible symptoms and are of concern to citrus growers. In this study several citrus diseases (citrus leaf rugose virus, citrus tristeza virus, citrus psorosis virus, and Xanthomonas axonopodis ) and nutrient deficiencies (iron, copper, zinc, manganese, and magnesium) were compared with HLB using FTIR spectroscopy to determine if the spectra alone can be used to identify plants that are infected with HLB instead of another disease. The results indicate that the spectra of some diseases and deficiencies more closely resemble those of apparently healthy plants and some share the carbohydrate transformation that has been seen in the spectra of HLB-infected plants.

Reliable, precise and accurate estimates of disease severity are important for predicting yield loss, monitoring and forecasting epidemics, for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including co-evolution. Disease assessments that are inaccurate and/or imprecise might lead to faulty conclusions being drawn from the data, which in turn can lead to incorrect actions being taken in disease management decisions. Plant disease can be quantified in several different ways. This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes our current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted. The review also considers how these can be identified using various statistical tools. Indeed, great strides have been made in the last thirty years in identifying the sources of assessment error inherent to visual rating, and this review highlights ways that assessment errors can be reduced—particularly by training raters or using assessment aids. Lesion number in relation to area infected is known to influence accuracy and precision of visual estimates—the greater the number of lesions for a given area infected results in more overestimation. Furthermore, there is a widespread tendency to overestimate disease severity at low severities (

Citrus Huanglongbing (HLB, also known as citrus greening disease) was discovered in Florida in 2005 and is spreading rapidly amongst the citrus growing regions of the state. Detection via visual symptoms of the disease is not a long-term viable option. New techniques are being developed to test for the disease in its earlier presymptomatic stages. Fourier transform infrared-attenuated total reflection (FT-IR-ATR) spectroscopy is a candidate for rapid, inexpensive, early detection of the disease. The mid-infrared region of the spectrum reveals dramatic changes that take place in the infected leaves when compared to healthy non-infected leaves. The carbohydrates that give rise to peaks in the 900–1180 cm−1 range are reliable in distinguishing leaves from infected plants versus non-infected plants. A model based on chemometrics was developed using the spectra from 179 plants of known disease status. This model then correctly predicted the status of >95% of the plants tested.

The U. S. Department of Agriculture, Agricultural Research Service has been developing a method and system to detect fecal contamination on processed poultry carcasses with hyperspectral and multispectral imaging systems. The patented method utilizes a three step approach to contaminant detection. Spectra of homogenous samples of feces, ingesta (undigested food particles), and skin were first collected. Then those spectra were evaluated with multivariate analysis techniques to identify significant wavelength regions for further analysis. Hyperspectral data were then collected on contaminated poultry carcasses and information learned from the spectroscopic data was used to aide in hyperspectral data analysis. Finally, the results of the hyperspectral data were used to identify a few optimum wavelengths for use in a real-time multispectral imaging system. In this work, two techniques for developing spectral datasets and algorithms for classifying surface contaminants on poultry carcasses were explored. The first consisted of a scanning monochrometer that measured the average spectra of uncontaminated breast skin and fecal and ingesta contaminants. The second technique used regions of interest (ROI) from a hyperspectral image to collect spatially averaged spectra. Comparison of the spectra from each instrument showed variations in the spectra collected from similar samples. There was an offset of absorption values between the two instruments and the hyperspectral imaging system had better resolution at higher absorption wavelengths. Although both systems were calibrated prior to measuring, there was also a slight shift in absorption peaks between the two systems. Both techniques were able to classify contaminated skin from uncontaminated skin in a full cross-validated test set with better than 99% accuracy. However, when the classification model developed from the monochrometer spectra was applied to whole-carcass hyperspectral images, numerous common carcass features, such as exposed meat and wing-shadowed skin, were wrongly identified as false positives. Since spectra of entire poultry carcasses were available in the original hyperspectral dataset, the hyperspectral ROI technique allowed researchers to easily add the spectra of these false positives to the calibration dataset. New partial least squares regression models with meat and skin shadow spectra resulted in different principal component loadings and improved classification models. The classification model with the combined ROI spectra from skin, feces, ingesta, meat, and skin shadows gave a classification accuracy of 99.5%. When this model was compared to the original model developed from the monochrometer dataset on a few hyperspectral images of contaminated carcasses, fewer false positives were classified with the hyperspectral ROI model without sacrificing the accuracy of contaminant detection. Further research must be done to fully characterize the accuracy of the model.

A method to calibrate a pushbroom hyperspectral imaging system for "near-field" applications in agricultural and food safety has been demonstrated. The method consists of a modified geometric control point correction applied to a focal plane array to remove smile and keystone distortion from the system. Once a FPA correction was applied, single wavelength and distance calibrations were used to describe all points on the FPA. Finally, a percent reflectance calibration, applied on a pixel-by-pixel basis, was used for accurate measurements for the hyperspectral imaging system. The method was demonstrated with a stationary prism-grating-prism, pushbroom hyperspectral imaging system. For the system described, wavelength and distance calibrations were used to reduce the wavelength errors to

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.