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Local Tetra Pattern-Based Fruit Grading Using Different Classifiers

  • Ramanpreet Kaur
  • Mukesh Kumar
  • Mamta Juneja
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

Agriculture is an integral part of economic development, and thus, it becomes essential to lift the impact factor of agriculture development. In past years, researchers had introduced many nondestructive image processing technique to grade the food products. These techniques ensure the quality of food products, are consistent, and save the labor time as well. Many dedicated systems or techniques are available for grading particular type of fruit; therefore, there is need to devise common technique to grade various type of fruits. This paper introduces the common feature extraction method which uses local tetra pattern to grade fruits. In this research, we graded guava fruit into four categories (unripe, ripe, overripe, and defected). The performance of the proposed method is evaluated and compared using ensemble classifiers and compared using accuracy and error rate. The experimental results showed the highest accuracy of 93.8% by Subspace Discriminant Ensemble classifier. The proposed method can be easily adapted for any other spherical fruit or vegetable.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.UIET, CSE DepartmentPanjab UniversityChandigarhIndia

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