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