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Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry

  • Published:2019-07 Issue: Volume:162 Page:1-13
  • ISSN:0168-1699
  • Container-title:Computers and Electronics in Agriculture
  • language:en
  • Short-container-title:Computers and Electronics in Agriculture

Author:

Rehman Tanzeel U.,Zaman Qamar U.,Chang Young K.,Schumann Arnold W.,Corscadden Kenneth W.

Funder

Nova Scotia Research and Innovation Graduate Scholarship Program

New Brunswick Department of Agriculture

Aquaculture and Fisheries

Doug Bragg Enterprises

Wild Blueberry Producers Association of Nova Scotia

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference47 articles.

1. Automated weed classification with local pattern-based texture descriptors;Ahmed;Int. Arab J. Inform. Technol.,2014

2. An ultrasonic system for weed detection in cereal crops;Andújar;Sensors,2012

3. Crop segmentation from images by morphology modeling in the CIE L* a* b* color space;Bai;Comput. Electron. Agric.,2013

4. Weed segmentation using texture features extracted from wavelet sub-images;Bakhshipour;Biosyst. Eng.,2017

5. Evaluation of support vector machine and artificial neural networks in weed detection using shape features;Bakhshipour;Comput. Electron. Agric.,2018

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