<p>Intraspecific parasitism is a serious problem, especially for endangered bird species such as the Slender-Billed Gull. Indeed, once a parasitic egg is detected in its nest, the Slender-Billed Gull female abandons the nest, which results in a loss of a significant number of eggs and contributes to this species extinction. As a result, cleaning the nest through getting rid of the parasitic eggs from the nest is becoming an urgent need. In this research work, we propose a parasitic egg identification method for the Slender-Billed Gull species using an ensemble machine learning model based on visual egg information. In fact, after preprocessing the egg image, our proposed system extracts the most pertinent visual features by means of the Fast Beta Wavelet Network (FBWN) and SpotEgg tools. Consequently, several local features such as spottiness, color, and size, are obtained by SpotEgg and global egg-specific visual features like shape, color, and texture are be obtained by FBWN. The combination of these visual features offers a comprehensive egg feature representation. Then, an ensemble machine learning model is used to classify these features. Actually, several single models and various ensemble learning strategies have been evaluated on a 31-nest dataset. After an exhaustive experimental study, a bagging-based SVM model with different polynomial kernel degrees has given the best identification accuracy of 95.1%. This ensemble learning method applied on an aggregation of two egg-specific visual features (FBWN and SpotEgg) enhances classification by leveraging multiple SVMs to improve robustness and generalization, especially suitable for small datasets. The proposed method has surpassed the state of the art parasitic egg identification methods.</p>