LBP Feature Based Pest Identification in Rice Crop

Authors

  • Priyadarshi Kanungo Department of Electronics and Telecommunication Engineering, C. V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Safwan Ghanem Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Shakti Kumari Department of Electronics and Telecommunication Engineering, C. V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Rubeena Naaz Department of Electronics and Telecommunication Engineering, C. V. Raman Global University, Bhubaneswar, Odisha-752054, India
  • Rashmi Prava Nayak Department of Electronics and Telecommunication Engineering, C. V. Raman Global University, Bhubaneswar, Odisha-752054, India

DOI:

https://doi.org/10.46977/apjmt.2020.v01i01.006

Keywords:

LBP; ROI; CIELAB; Pest Identification.

Abstract

Food is the basic necessity of every living being, unfortunately, vegetables and fruits became harmful because of the overuse of pesticides. Toxins are unsuccessfully spread in farms for increasing the harvest amounts and quantities, needless to say, that causes serious health problems. Therefore, more reliable information regarding pests should be provided to farmers in a user-friendly way. In this work, a novel method for automatic pest identification using local binary pattern (LBP) feature is proposed, implemented, then tested using real-life images. Firstly, the image is resized and the a* plane of the L*a*b* color space is used to extract the region of interest (ROI). Secondly, the histogram of the LBP of the ROI is calculated. Finally, a correlation matching is performed to measure the similarity between input ROI and predefined trained templates of different pests. The model output includes the pest name and symptoms. Experiments conducted on the variety of pests of rice plant infected leaves showed a 92.4% true identification rate which makes this method reliable comparing with the reported results from other works. The detection time also is 10 ms/ frame which fulfills one of the real-time application requirements.

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References

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Published

2020-06-12

How to Cite

Kanungo, P., Ghanem, S., Kumari, S., Naaz, R., & Nayak, R. P. (2020). LBP Feature Based Pest Identification in Rice Crop. Advancement in Management and Technology (AMT) , 1(1), 30-35. https://doi.org/10.46977/apjmt.2020.v01i01.006

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