Recognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70-100% are obtained for field recordings with varying sound intensities and background signals.

No abstract

No abstract

No abstract

Abstract-This paper describes research into the automation of the identification of harlequin and other ladybird species using color images. The automation process involves image processing and the use of an artificial neural network as a classifier. The ultimate aim is to reduce the number of color images to be examined by an expert by pre-sorting the images into correct, questionable and incorrect species. The ladybirds are 3-dimensional and the images have variable resolution. CIELAB has been useful as the color space in this research, as it provides good separation of chroma components from luminance on a color plane. Two major sets of features have been extracted from ladybird images: color and geometrical measurements. The system combination consisted of J48 decision trees which were used to filter out unnecessary features, and multilayer perceptron which was used for classification. Trials using ladybird images showed 92% class match for the species Harmonia axyridis f. spectabilis against Exochomus 4-pustulatus. The identification results are rotation and translation invariant. The methods allow quantitative data on both intra-species and inter-species variation for biodiversity studies.