Accurate classification of potato leaf diseases under uncontrolled conditions is challenging due to lighting variability, background clutter, and class imbalance. This study presents a lightweight and interpretable machine learning pipeline based on handcrafted feature engineering, specifically designed to address the challenges of uncontrolled imaging conditions in potato leaf disease classification. We systematically extract diverse feature types - including color statistics, color histograms, and BoVW-based texture descriptors (SIFT, KAZE) - and integrate them to form a comprehensive representation. To improve minority class recognition, Borderline-SMOTE is applied during training. Experimental results on a real-world potato leaf disease dataset demonstrate that the proposed approach achieves 81.03% accuracy using the LightGBM classifier, outperforming several state-of-the-art deep learning models. These results highlight the effectiveness of carefully engineered features and their integration for real-world plant disease classification.