Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull’s Nest

Intraspecific nest parasitism is a phenomenon that attracts the attention of biologists since it helps in saving the endangered species such as Slender Billed Gull. The problem comes from the fact that a parasite female lays its eggs in the nest of another female (host) of the same species which causes the abandon of the nest by the host. This behavior causes a significant reduction in future birds number and leads to the expansion of this specie. Thus, there has been an urgent necessity to clean the nest from parasitic eggs. So, our aim is to build an automatic parasitic egg identification system based on egg visual features information. Our system uses deep learning models which have proven their success for image classification. Indeed, our system conduct an egg image’s pre-processing phase followed by Fast Beta Wavelet Network (FBWN) to extract the most efficient descriptors (shape, texture, and color). Then, these features will be inputted to the Stacked AutoEncoder for egg classification. Our proposed system, has been evaluated on 91-egg dataset collected from 31 clutches of eggs in Sfax region, Tunisia. Our model has given a parasitic egg identification accuracy of 89.9% which has outperformed the state-of-the-art method and shows the efficiency and the robustness of our system.