A workflow for low-cost automated image analysis of myxomycete spore numbers, size and shape

High Environmentally Induced Plasticity in Spore Size and Numbers of Nuclei per Spore in Physarum albescens (Myxomycetes)

Spore size enables dispersal in plasmodial slime molds (Myxomycetes) and is an important taxonomic character. We recorded size and the number of nuclei per spore for 39 specimens (colonies of 50-1000 sporocarps) of the nivicolous myxomycete Physarum albescens, a morphologically defined taxon with several biological species. For each colony, three sporocarps were analyzed from the same spore mount under brightfield and DAPI-fluorescence, recording ca. 14,000 spores per item. Diagrams for spore size distribution showed narrow peaks of mostly uninucleate spores. Size was highly variable within morphospecies (10.6-13.5 µm, 11-13%), biospecies (3-13%), even within spatially separated colonies of one clone (ca. 8%); but fairly constant for a colony (mean variation 0.4 µm, ca. 1.5%). ANOVA explains most of this variation by the factor locality (within all colonies: 32.7%; within a region: 21.4%), less by biospecies (13.5%), whereas the contribution of intra-colony variation was negligible (<0.1%). Two rare aberrations occur: 1) multinucleate spores and 2) oversized spores with a double or triple volume of normal spores. Both are not related to each other or limited to certain biospecies. Spore size shows high phenotypic plasticity, but the low variation within a colony points to a strong genetic background.

A Rapid Detection Method for Fungal Spores from Greenhouse Crops Based on CMOS Image Sensors and Diffraction Fingerprint Feature Processing

The detection and control of fungal spores in greenhouse crops are important for stabilizing and increasing crop yield. At present, the detection of fungal spores mainly adopts the method of combining portable volumetric spore traps and microscope image processing. This method is problematic as it is limited by the small field of view of the microscope and has low efficiency. This study proposes a rapid detection method for fungal spores from greenhouse crops based on CMOS image sensors and diffraction fingerprint feature processing. We built a diffraction fingerprint image acquisition system for fungal spores of greenhouse crops and collected diffraction fingerprint images of three kinds of fungal spores. A total of 13 diffraction fingerprint features were selected for the classification of fungal spores. These 13 characteristic values were divided into 3 categories, main bright fringe, main dark fringe, and center fringe. Then, these three features were calculated to obtain the Peak to Center ratio (PCR), Valley to Center ratio, and Peak to Valley ratio (PVR). Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results show that the SVM model has a better overall classification performance than the LR, KNN, and RF models. The average accuracy rate of the recognition of three kinds of fungal spores from greenhouse crops under the SVM model was 92.72%, while the accuracy rates of the LR, KNN, and RF models were 84.97%, 87.44%, and 88.72%, respectively. The F1-Score value of the SVM model was higher, and the overall average value reached 89.41%, which was 11.12%, 7.18%, and 5.57% higher than the LR, KNN, and RF models, respectively. Therefore, the method proposed in this study can be used for the remote identification of three fungal spores which can provide a reference for the identification of fungal spores in greenhouse crops and has the advantages of low cost and portability.