No abstract

While insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role in increased acceptance and application of IPM in practice. In this study, we tested the differentiation of two whitefly species and their natural enemies trapped on yellow sticky traps (YSTs) via image processing approaches under practical conditions. Using the bag of visual words (BoVW) algorithm, accurate differentiation between both natural enemies and the Trialeurodes vaporariorum and Bemisia tabaci species was possible, whereas the procedure for B. tabaci could not be used to differentiate this species from T. vaporariorum. The decay of species was considered using fresh and aged catches of all the species on the YSTs, and different pooling scenarios were applied to enhance model performance. The best performance was reached when fresh and aged individuals were used together and the whitefly species were pooled into one category for model training. With an independent dataset consisting of photos from the YSTs that were placed in greenhouses and consequently with a naturally occurring species mixture as the background, a differentiation rate of more than 85% was reached for natural enemies and whiteflies.

A bait test with leaves of Rhododendron ‘Cunningham's White’ and Rhododendron ‘Catawbiense Grandiflorum’ was validated for the detection of Phytophthora ramorum according to EPPO Standard PM 7/98 (2) Specific requirements for laboratories preparing accreditation for plant pest diagnostic activities (EPPO, 2014). The bait test was validated with zoospores and agar cultures as inocula for the parameters ‘frequency of leaves with necrosis’, ‘size of necrosis’ and ‘isolation rate’. In addition, the influence of the month of collection of bait leaves on the parameters was studied.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.