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Inspection of Crop-Weed Image Database Using Kapur’s Entropy and Spider Monkey Optimization

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

Abstract

Image assessment measures are commonly employed in different domains to extract the helpful information to take essential decisions. This paper implements a soft-computing approach to examine the Benchmark Crop-Weed (BCW) images of Computer Vision Problems in Plant Phenotyping (CVPPP2014) challenge database. The proposed work executes a hybrid procedure based on Spider Monkey Optimization (SMO) algorithm and Kapur’s multi-thresholding and the Watershed Segmentation (WS) based extraction. After extracting the Crop-Weed regions of BCW pictures, the superiority of the proposed tool is then assessed by implementing a relative study among extracted segment and its related ground-truth. Additionally, the prominence of SMO is validated against the Bat-Algorithm (BA) and Firefly-Algorithm (FA). The outcome of this study authenticates that SMO-based technique is competent in examining the BCW pictures with significant accuracy and precision.

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Author information

Authors and Affiliations

  1. St. Joseph’s AI Group, St. Joseph’s College of Engineering, Chennai, 600119, Tamilnadu, India

    V. Rajinikanth

  2. Department of Information Technology, Techno India College of Technology, Kolkata, 700156, India

    Nilanjan Dey

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to Be University), Bhubaneswar, 751024, Odisha, India

    Suresh Chandra Satapathy

  4. Department of Instrumentation Engineering, M.I.T Campus, Anna University, Chennai, 600044, Tamilnadu, India

    K. Kamalanand

Authors
  1. V. Rajinikanth
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  2. Nilanjan Dey
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  3. Suresh Chandra Satapathy
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  4. K. Kamalanand
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Corresponding author

Correspondence to V. Rajinikanth .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

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

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Rajinikanth, V., Dey, N., Satapathy, S.C., Kamalanand, K. (2020). Inspection of Crop-Weed Image Database Using Kapur’s Entropy and Spider Monkey Optimization. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_32

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