Abstract
This contribution demonstrates an example of experimental automatic image analysis to detect spores prepared on microscope slides derived from trapping. The application is to monitor aerial spore counts of the entomopathogenic fungus Pandora neoaphidis which may serve as a biological control agent for aphids. Automatic detection of such spores can therefore play a role in plant protection. The present approach for such detection is a modification of traditional manual microscopy of prepared slides, where autonomous image recording precedes computerised image analysis. The purpose of the present image analysis is to support human visual inspection of imagery data – not to replace it. The workflow has three components:•Preparation of slides for microscopy.•Image recording.•Computerised image processing where the initial part is, as usual, segmentation depending on the actual data product. Then comes identification of blobs, calculation of principal axes of blobs, symmetry operations and projection on a three parameter egg shape space.
Highlights
This contribution demonstrates an example of experimental automatic image analysis to detect spores prepared on microscope slides derived from trapping
The present contribution attempts to aid identification of spores in images of microscope slides originally meant for manual processing
The present experiments include use of a P. neoaphidis isolate NCRI 393/13 obtained from its natural host the English grain aphid (Sitobion avenae) on wheat (Triticum aestivum) at Horten (WGS84: N59826.0830, E10824.1910), Norway, 8 August 2013
Summary
Monitoring spore counts in air using spore trap samplers traditionally includes laborious counting of spores under microscope before subsequent statistical treatment. Bonner et al [3] approached computerised measurement of production of [35_TD$IF]conidia from the aphid pathogenic fungus Erynia neoaphidis. They focused on data preparation to simplify the computerised part of the workflow. The present contribution attempts to aid identification of spores in images of microscope slides originally meant for manual processing. It may be regarded as a possible low cost, simple and intuitive extension of established manual and visual skill-based procedures. Such molecular detection methods exist for most of the entomopathogenic hypocrealean fungi, but there are only few for entomopathogenic fungi in the Entomophthoramycota
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