Automated identification of live moths (Macrolepidoptera) using digital automated identification System (DAISY)

Abstract Two hundred and thirty‐seven species of Macrolepidoptera were light trapped at Treborth Botanical Garden, Gwynedd, UK. Live adults were digitally imaged using a simple, inexpensive method suitable for field use, then released. Inconsistent lighting, variation in resting posture and inclusion of worn individuals produced image sets high in intraspecific variation. Thirty‐five common species were selected to provide training images for the Digital Automated Identification SYstem (DAISY). Twenty individuals per species were pre‐processed to standardize size and posture and to enhance features. The right forewing of each was highlighted manually and the pattern rendered polar and greyscale for DAISY analysis. Despite poor quality of some images, 83% of unknown species were identified correctly. The best species had 100% correct identification and the worst 35%. The most poorly identified images were those of moths that had lost scales or been unevenly illuminated. The precision with which the forewing was highlighted affected performance. When highlighted carefully, Laothoe populi was identified correctly twice as successfully as when the same image was highlighted poorly. Size of the training set was also important. Sets of 5, 10, 15 and 20 training images, plotted against performance produced a curve of diminishing returns. Colourimages and inclusion of size should improve accuracy.