Invasive non-native species spread rapidly outside their natural range and can disrupt ecosystems, damage economies, and threaten health. Early identification is therefore critical, yet current ecological practice remains largely manual and existing deep learning pipelines provide little insight into the morphological traits that define invasiveness. This thesis addresses this gap by developing an explainability-driven deep learning pipeline that links morphological traits to a model's classification prediction of invasiveness and exposes why the model fails on specific images. The approach trains a classifier on image embeddings extracted with BioCLIP-2 and adopts an imageomics perspective, treating images as high-dimensional phenotypes. Saliency-guided region extraction (Integrated Gradients) identifies the image pixels most critical to the model's predictions.