The classification of pistachio varieties is crucial to the agricultural economy, with distinct types catering to specific market demands. This reality necessitates the development of highly accurate and automated classification methods. In this chapter, we propose a novel approach that enhances pistachio classification by fusing two powerful feature extraction techniques: local binary patterns (LBP) and the gray-level co-occurrence matrix (GLCM). This combination creates a comprehensive feature representation that significantly improves classification capabilities. Experiments on a real-world dataset demonstrated the superiority of the proposed method, with a support vector machine (SVM) classifier achieving an impressive accuracy of 98.45% using the fused features. These results present significant practical implications for intelligent agricultural processing and underscore the promise of this technique for smart farming applications.