Background: The most frequently cultivated edible legume crop in the world is Dry Beans (DB), which exhibit high genetic diversity. The impact of seed quality on crop production is undeniable. Seed classification is essential for production and marketing to provide building blocks for sustainable agricultural systems. With regard to big production numbers, traditional methods for classifying seed quality have flaws, including complicated processes, poor precision and sluggish inspection. Rapid and high throughput solutions are provided by automatic categorization algorithms based on machine learning and computer vision. Modern automatic classification models have made significant strides, yet there is still room for improvement by adding new methods. Since crop production is in the form of population rather than a single variation, the main goal of this study is to offer a technique for getting homogeneous dry bean variants. Although numerous intelligent models have been presented, most rely on a single classifier, which makes them unable to handle noisy and unbalanced data and can cause overfitting. Methods: To reduce bias and variance and avoid overfitting a single classifier-based model, this study provides an ensemble-based prediction model combining pertinent attributes and a simple stacking ensemble technique, Xtreme Stacking Prediction of Dry Beams (X-SPDB). The forecast is made using the proposed X-SPDB, which incorporates several assumptions. Result: Comparisons are made between the proposed X-SPDB’s performance and simple Decision Tree, SVM, Random Forest, Naive Bayes, SVM, Logistic Regression and SVM with XGBoost.