Automated carrot disease recognition: a computer vision approach

  • Authors

    • Anup Majumder Daffodil International Universuty
    • Md. Tarek Habib Daffodil International Universuty
    • Papiya Hossain Lima Daffodil International Universuty
    • Saifuddin Sourav Daffodil International Universuty
    • Rabindra Nath Nandi Khulna University of Engineering and Technology
    2019-04-21
    https://doi.org/10.14419/ijet.v7i4.27019
  • Carrot Disease, Agro-Medical Expert System, Computer Vision, k-means Clustering, Support Vector Machine, Performance Metric.

  • Abstract

    To ensure the freshness of fruits and vegetables modern image processing tools can help a lot. Experts can detect the defected fruits and vegetables by watching them with their eyes but the process is too long and not suitable for all the stores, farms, supermarkets or the exporters all around. There comes the blessings of new computer vision technologies with image processing techniques that can do a lot of works in a second. In this paper an automated approach is developed to detect defects of fruits and vegetables and recognize diseases by using machine vision based image processing techniques. There are many algorithms that can detect defects of fruits and vegetables hence, we separated the defected parts of the carrots using k-means clustering and then classified it with Multiclass Support Vector Machine. Here, a supervised machine learning concept is implemented to recognize various carrot diseases. As the domain of this research model, carrot diseases are classified and 96% of accuracy is achieved which can certainly help in our agricultural science along with proper maintenance.

     

     

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  • How to Cite

    Majumder, A., Tarek Habib, M., Hossain Lima, P., Sourav, S., & Nath Nandi, R. (2019). Automated carrot disease recognition: a computer vision approach. International Journal of Engineering & Technology, 7(4), 5790-5797. https://doi.org/10.14419/ijet.v7i4.27019

    Received date: 2019-02-02

    Accepted date: 2019-02-25

    Published date: 2019-04-21