Local Binary Patterns for Classifying Pistachio Species

To maintain the economic value of pistachio nuts, which is vital in the agricultural economy, efficient post-harvest industrial processes are essential. Different pistachio species cater to distinct markets, increasing the need for accurate classification methods. This study introduces an alternative feature extraction approach using Local Binary Pattern (LBP) to enhance the classification of pistachio species. We apply this method to a dataset comprising 2,148 images of two pistachio species, Kirmizi and Siirt, each with unique market-relevant characteristics. Various machine learning models are evaluated, with Multilayer Perceptron (MLP) emerging as the most effective, achieving a classification accuracy of 96.74%. The findings demonstrate that machine learning methods, combined with LBP, significantly improve the performance and efficiency of pistachio variety classification. These results hold promising applications in precise pistachio selection and intelligent farming techniques.