An intelligent approach to identify parasitic eggs from a slender-billed’s nest

Nhidi Wiem; Ridha Ejbali; Dahman Hassen

doi:10.1117/12.2558685

31 January 2020 An intelligent approach to identify parasitic eggs from a slender-billed’s nest

Nhidi Wiem, Ridha Ejbali, Dahman Hassen

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143309 (2020) https://doi.org/10.1117/12.2558685
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

The intraspecific nest parasitism is a phenomenon that attracts the attention of biologists. There are bird species like the Slender-billed which contains at most 3 eggs, but their nests can contain four or five eggs. In fact, a genetic study made on a set of nests has shown that one or two of the eggs belong to a second female named by biologists “a parasitic egg”. As the Gull Mockers are protected by the Law, researchers found it difficult to identify parasite eggs without genetic test. Many studies have been done in order to identify the parasitic egg, based on the morphological parameters and the characteristic of the egg’s shell, but these studies haven’t led to good results. Recent Advances in Artificial Intelligence (AI) and particularly Deep Learning (DL) techniques has increased motivation to use this method to quantify parasitic eggs. In this work, we present a new method to quantify a parasitic egg from a dataset of egg’s image. One of the most used techniques is Convolutional Neural Network (CNN). The technique is a supervised learning method used to classify images. We used this technique to extract features from image to characterize any egg. To evaluate our approach, we use 31 lays of eggs form the 92 eggs dataset to test the performance of our proposed method.

Citation Download Citation

Nhidi Wiem, Ridha Ejbali, and Dahman Hassen "An intelligent approach to identify parasitic eggs from a slender-billed’s nest", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143309 (31 January 2020); https://doi.org/10.1117/12.2558685

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