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An Efficient Bag-of-Features for Diseased Plant Identification

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Computer Vision and Machine Learning in Agriculture

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Agriculture is the main source of human habitation and its redemption from disease is a primary concern for any economy. For the same, computer vision techniques have been proven to be quite useful. However, the diseased plant identification is still a challenging task due to the disparity in the leaf images. To alleviate the same, this chapter proposes a new bag-of-features-based diseased plant identification method. In the proposed method, the efficient visual words are generated using gray relational analysis-based clustering method, and for image encoding, two-dimensional vector quantization method is used. The proposed method is evaluated on a publicly available leaf images dataset, i.e., PlantVillage. Moreover, the performance is compared against the state-of-the-art classification methods in terms of accuracy, precision, recall, sensitivity, and specificity. Experiments validate that the proposed method is efficient than the compared methods image classification.

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Authors and Affiliations

  1. Jaypee Institute of Information Technology, Noida, India

    Raju Pal, Himanshu Mittal, Avinash Pandey & Mukesh Saraswat

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  1. Raju Pal
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  2. Himanshu Mittal
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  3. Avinash Pandey
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  4. Mukesh Saraswat
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Correspondence to Mukesh Saraswat .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

  2. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Pal, R., Mittal, H., Pandey, A., Saraswat, M. (2021). An Efficient Bag-of-Features for Diseased Plant Identification. In: Uddin, M.S., Bansal, J.C. (eds) Computer Vision and Machine Learning in Agriculture. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-6424-0_11

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