Shape-based Fruit Recognition and Classification

  • Susovan JanaEmail author
  • Ranjan Parekh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 776)


Classification of fruits is traditionally done using manual resources due to which the time and economic involvements increase adversely with number of fruit types and items per class. In recent times computer based automated techniques have been used to alleviate this problem to a certain extent. These techniques utilize image analysis and pattern recognition methodologies to automatically classify fruits based on their visual features like color, texture, and shape. However, challenges of such techniques include the fact that fruit appearances differ due to natural environments, geographical locations, stages of growth, size, orientations and imaging equipments. In this paper, a shape based fruit recognition approach has been proposed which is independent of many of these factors. It involves a pre-processing step to normalize a fruit image with respect to variations in translation, rotation, scaling and utilizes features which do not change due to varying distances, growth stages and surface appearances of fruits. The proposed method has been applied to 210 images of 7 fruit classes. The overall recognition accuracy ranges from 88–95%.


Fruit classification Geometrical transformation Morphological operation Convex polygon Naïve bayes classifier 


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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of Education TechnologyJadavpur UniversityKolkataIndia

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