This research focuses on optimizing crop selection and understandability of the soil behavior with the help of different XAI methodologies to leverage and explore the extent of its benefits in the agricultural aspects. This paper leverages and compares several machine learning algorithms, including Random Forests, Naïve Bayes, and Support Vector Machines (SVM), to develop a robust classifier. To enhance usability, this paper incorporates Shapley Additive Explanations (SHAP) to identify influential features. Additionally, to help visualize the impact of soil characteristics using Partial Dependency Plots (PDPs). By combining predictive accuracy, explainability, and visualization, the approach adopted in this paper empowers farmers to make informed decisions, bridging the socio-economic divide within the agricultural sector.