Low-Cost Classification of Close and Open Shell Antep Pistachio Nuts based on Image Analysis and Machine Learning

Abstract

The effectiveness of post-harvest industrial processes is critical to maintaining the economic worth of pistachio nuts, which play an essential role in the agricultural economy. To achieve this level of efficiency, updated applications and technology for pistachio separation and categorization are required. Different pistachio species target different markets, highlighting the need for pistachio species classification. This work aims to develop a classification model that is distinct from existing separation approaches, based on image processing and machine learning, and can provide the required categorization. A computer vision application was done to identify between three types of pistachios. A high-resolution camera was used to capture 385 images of these pistachios. The photos of the pistachio samples were processed using image processing techniques like segmentation and feature extraction. On the given dataset, an advanced classifier based on Decision Tree and Random Forest predictions was constructed, as well as a simple and successful classifier. In the research, an application with feature extraction based on the dimension and pixel measurement is proposed. The proposed approach attained a classification success rate of 100% at 70% train and 30% test, and also, 80% train and 20% test data rate with Random Forest prediction, according to the experimental data. The provided high-performance classification model fills an important demand for the separation of pistachio types while increasing the economic worth of the species.

Keywords

• Image Processing
• Image Analysis
• Classification
• Gaziantep Pistachio
• Decision Tree
• Random Forest.

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