Abstract
This paper describes recent work in the UK to automate the identification of Harlequin ladybird species (Harmonia axyridis) using color images. The automation process involves image processing and the use of probabilistic neural network (PNN) as classifier, with an aim to reduce the number of color images to be examined by entomologists through pre-sorting the images into correct, questionable and incorrect species. Two major sets of features have been extracted: color and geometrical measurements. Experimental results revealed more than 75% class match for the identification of taxa with similar-colored spots.
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Ayob, M.Z., Chesmore, E.D. (2013). Probabilistic Neural Network for the Automated Identification of the Harlequin Ladybird (Harmonia Axyridis). In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_3
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DOI: https://doi.org/10.1007/978-3-642-44949-9_3
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