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Machine Learning-Based Classification of Good and Rotten Apple

  • Shiksha Singh
  • Nagendra Pratap Singh
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)

Abstract

An apple is one of the most cultivated and consumed fruits in the world and continuously being praised as a delicious and miracle food. It is a rich source of Vitamin A, Vitamin B1, Vitamin B2, Vitamin B6, Vitamin C, and folic acid etc, whereas the rotten fruits affect the health of human being as well as cause big economical loss in agriculture sectors and industries. Therefore, identification of rotten fruits has become a prominent research area. This paper focuses on the classification of rotten and good apple. For classification, first extract the texture features of apples such as discrete wavelet feature, histogram of oriented gradients (HOG), Law’s Texture Energy (LTE), Gray level co-occurrence matrix (GLCM) and Tamura features. After that, classify the rotten and good apples by applying various classifiers such as SVM, k-NN, logistic regression, and Linear Discriminant. The performance of proposed approach by using SVM classifier is 98.9%, which is found better with respect to the other classifiers.

Keywords

Apple images Texture features Machine learning Classification 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMMM University of TechnologyGorakhpurIndia

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