This paper presents a case study in smart agriculture using Explainable AI (XAI) to analyze and assess the behavior of AI models. The study focuses on a multiclass classification problem using the Crop Recommendation dataset, where the goal is to predict the most suitable crop based on seven features. Two popular XAI methods, SHAP and LIME, are used to generate explanations and interpretations of the model's predictions. These methods provide visualizations that help users understand model behavior without needing to delve into complex mathematical details. While there are criticisms of these approaches, they remain widely used and considered key references in XAI research. The study highlights the importance of interpretability in smart agriculture, where farmers and agronomists need to trust AI predictions. XAI allows for the investigation of the knowledge learned by machine learning models, enabling non-experts to understand why a model predicts a particular crop. The paper demonstrates how SHAP and LIME can be used to visualize model behavior, revealing patterns and insights into the decision-making process. For example, SHAP summary plots show the most important features for predicting certain crops, while LIME provides local explanations that help interpret model predictions. The paper also discusses the limitations and strengths of SHAP and LIME. While both methods are effective, they have challenges such as computational cost and potential inaccuracies in explanations. Despite these issues, they remain valuable tools in XAI, especially in agricultural contexts where interpretability is crucial. The study emphasizes the need for XAI in smart agriculture to ensure that models are not only accurate but also trustworthy and understandable to users. Future work includes exploring different XAI approaches and visualization techniques to enhance the practical application of these methods in various fields.This paper presents a case study in smart agriculture using Explainable AI (XAI) to analyze and assess the behavior of AI models. The study focuses on a multiclass classification problem using the Crop Recommendation dataset, where the goal is to predict the most suitable crop based on seven features. Two popular XAI methods, SHAP and LIME, are used to generate explanations and interpretations of the model's predictions. These methods provide visualizations that help users understand model behavior without needing to delve into complex mathematical details. While there are criticisms of these approaches, they remain widely used and considered key references in XAI research. The study highlights the importance of interpretability in smart agriculture, where farmers and agronomists need to trust AI predictions. XAI allows for the investigation of the knowledge learned by machine learning models, enabling non-experts to understand why a model predicts a particular crop. The paper demonstrates how SHAP and LIME can be used to visualize model behavior, revealing patterns and insights into the decision-making process. For example, SHAP summary plots show the most important features for predicting certain crops, while LIME provides local explanations that help interpret model predictions. The paper also discusses the limitations and strengths of SHAP and LIME. While both methods are effective, they have challenges such as computational cost and potential inaccuracies in explanations. Despite these issues, they remain valuable tools in XAI, especially in agricultural contexts where interpretability is crucial. The study emphasizes the need for XAI in smart agriculture to ensure that models are not only accurate but also trustworthy and understandable to users. Future work includes exploring different XAI approaches and visualization techniques to enhance the practical application of these methods in various fields.