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Computer Vision-Based Fruit Disease Detection and Classification

  • Abhay Agarwal
  • Adrija SarkarEmail author
  • Ashwani Kumar Dubey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 851)

Abstract

Indian economy is mainly dependant on agriculture as it gives employment to 60% of the population and contributes around 17% to the GDP, therefore, disease identification and management is extremely essential for the farmers in order to harvest a higher percentage of utilizable fruits, which are fit for consumption. Diseases in fruits are the cause of huge agricultural losses. Manual monitoring of fruits is not very reliable as it totally depends on the perception of the naked eye and also not feasible to have specialists in the remote areas where the fruits grow. Therefore, an automatic disease detection system for apples using image processing techniques has been proposed so that the extent of the disease can be known and loss of yield can be controlled. For image segmentation, k-means clustering has been used. Thirteen features have been extracted from the segmented image using gray-level co-occurrence matrix (GLCM). Multi-class support vector machine (SVM) is used for disease identification and classification. The results are experimentally validated and classification accuracy is achieved up to 98.387% as compared to other existing algorithms.

Keywords

K-means clustering Scab Canker GLCM SVM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhay Agarwal
    • 1
  • Adrija Sarkar
    • 1
    Email author
  • Ashwani Kumar Dubey
    • 1
  1. 1.Department of Electronics and Communication Engineering, Amity School of Engineering and TechnologyAmity University Uttar PradeshNoidaIndia

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