We analyze a case study in the field of smart agriculture exploiting Explainable AI (XAI) approach. The study regards a multiclass classification problem on the Crop Recommendation dataset. The original task is the prediction of the most adequate crop according to seven features. In addition to the predictions, two of the most well-known XAI approaches have been used in order to obtain explanations and interpretations of the behaviour of the models: SHAP (Shapley Additive ExPlanations), and LIME (Local Interpretable Model-Agnostic Explanations).
EXplainable AI for Smart Agriculture
Pilato;
2022
Abstract
We analyze a case study in the field of smart agriculture exploiting Explainable AI (XAI) approach. The study regards a multiclass classification problem on the Crop Recommendation dataset. The original task is the prediction of the most adequate crop according to seven features. In addition to the predictions, two of the most well-known XAI approaches have been used in order to obtain explanations and interpretations of the behaviour of the models: SHAP (Shapley Additive ExPlanations), and LIME (Local Interpretable Model-Agnostic Explanations).File in questo prodotto:
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