Smart Dry Bean Classification: Unleashing AI-Powered Image Analysis for Superior Precision

Dry beans are a widely consumed crop with distinct species, each possessing unique characteristics. Accurate classification is essential for quality control and efficient crop management. This study explores the multiclass classification of dry beans using various machine learning techniques, focusing on the impact of preprocessing methods—MinMax Scaler, Standard Scaler, and Robust Scaler—on model performance. Extensive experiments were conducted, with particular emphasis on the Light Gradient Boosting Machine (LGBM) classifier. Results indicate that LGBM consistently outperforms alternative models, including Multilayer Perceptron (MLP), Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Decision Tree, and Extra Tree. When utilizing the MinMax Scaler, the LGBM classifier achieved an accuracy of 96%, precision of 96%, recall of 95.80%, and an F1-score of 95.57%. These findings highlight LGBM’s effectiveness in accurately classifying dry beans while demonstrating the critical role of preprocessing techniques in optimizing model performance. Among the tested scalers, the MinMax Scaler consistently produced the highest-performing models, whereas the Standard Scaler exhibited slightly reduced performance on specific metrics. The Robust Scaler showed comparable results to the MinMax Scaler, reinforcing its suitability for handling outliers. These insights emphasize the importance of selecting an appropriate preprocessing technique based on dataset characteristics. The integration of the LGBM classifier with optimized preprocessing methods presents a powerful approach for dry bean classification, enabling precise quality assessment and informed crop management. These findings contribute to advancing machine learning applications in agriculture, offering practical guidance for researchers and industry professionals in optimizing classification models for agricultural data analysis.

Smart Dry Bean Classification: Unleashing AI-Powered Image Analysis for Superior Precision

Mohamed Reda Shoeib, Jun Zhao, Nanyang Technological University