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

  • V. RajinikanthEmail author
  • Nilanjan Dey
  • Suresh Chandra Satapathy
  • K. Kamalanand
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)


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.


Crop-Weed image Condition monitoring Spider Monkey Optimization Image assessment Validation 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • V. Rajinikanth
    • 1
    Email author
  • Nilanjan Dey
    • 2
  • Suresh Chandra Satapathy
    • 3
  • K. Kamalanand
    • 4
  1. 1.St. Joseph’s AI GroupSt. Joseph’s College of EngineeringChennaiIndia
  2. 2.Department of Information TechnologyTechno India College of TechnologyKolkataIndia
  3. 3.School of Computer EngineeringKalinga Institute of Industrial Technology (Deemed to Be University)BhubaneswarIndia
  4. 4.Department of Instrumentation EngineeringM.I.T Campus, Anna UniversityChennaiIndia

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