Guido Spina
XAI tools to predict biological invasiveness: a case study in plants.
Rel. Daniele Apiletti, Simone Monaco. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2025
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Abstract
Invasive non-native species are plants, animals, fungi or microorganisms that have been introduced (intentionally or accidentally) into an area where they are not originally present, and can have a negative impact on environment, economy or health by spreading quickly and without control. Their identification is important because it allows humans to either eradicate them if the spreading process has already begun, or to avoid their import into a new area altogether. To this moment, there is no method to identify what morphological traits make a species of plants potentially invasive and what makes it non-invasive based exclusively on image data, relying instead on categorical or numerical traits that are not always available.
In this work we propose a pipeline to identify, within a family of plants, which species have the potential to be invasive and which ones have not, using the "Lythrum" genus as a case study
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