Xylosandrus germanus (Blandford) has caused increasing damage in high-density New York apple orchards since 2013, resulting in tree decline and death. We documented their occurrence and timing in > 50 orchards using ethanol-baited traps from 2014 to 2016. First captures ranged from 48 to 83 degree days (base 10 °C) from 1 January. Captures were numerically higher at the orchard-woods interface than within the orchard interior, but differences were not significant in locations with lower populations. Control using insecticide trunk sprays was tested in potted, waterlogged apple trees placed in orchards and nurseries, and inside wooded areas adjacent to orchards. A verbenone repellent was used in combination with trunk sprays to improve control. Overall, insecticide sprays were inconsistent and marginal in preventing new infestations. Chlorpyrifos significantly reduced infestations versus lambda-cyhalothrin and untreated trees at one location in the 2015 orchard trials, and versus untreated trees at one location in the 2016 nursery trials, but otherwise performed no better than other treatments. The addition of verbenone to either the check or permethrin treatments resulted in significantly fewer attack sites containing brood at one orchard site in 2016. Chlorpyrifos, lambda-cyhalothrin, and permethrin significantly reduced the number of attack sites containing adults compared with untreated trees at one nursery trial location in 2016, but were otherwise ineffective in reducing numbers of trees in other locations and infestation categories. We found several fungal and bacterial species associated with X. germanus and its infestation of apples. These microbes likely play a minimal role in apple decline.

Investigating the chemical ecology of agricultural systems continues to be a salient part of integrated pest management programs. Apple maggot fly, a key pest of apple in eastern North America, is a visual specialist with attraction to host fruit-mimicking cues. These cues have been incorporated into red spherical traps used for both monitoring and behaviorally based management. Incorporating generalist or specialist olfactory cues can potentially increase the overall success of this management system. The primary aim of this study was to evaluate the attractiveness of a generalist olfactory cue, ammonium carbonate, and the specialist olfactory cue, a five-component apple volatile blend, when included as a component of a red attracticidal sphere system. Secondly, we assessed how critical it was to maintain minimal deviation from the optimal, full-round specialist visual stimulus provided by red spheres. Finally, attracticidal spheres were deployed with specialist olfactory cues in commercial apple orchards to evaluate their potential for effective management of apple maggot. Ammonium carbonate did not increase residency, feeding time, or mortality in the laboratory-based trials. Field deployment of specialist olfactory cues increased apple maggot captures on red spheres, while the generalist cue did not. Apple maggot tolerated some deviation from the optimal visual stimulus without reducing captures on red spheres. Attracticidal spheres hung in perimeter trees in orchards resulted in acceptable and statistically identical levels of control compared with standard insecticide programs used by growers. Overall, our study contributes valuable information for developing a reliable attract-and-kill system for apple maggot.

No abstract

No abstract

Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs.