eprintid: 627 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/00/06/27 datestamp: 2023-11-09 15:48:46 lastmod: 2023-11-09 15:48:46 status_changed: 2023-11-09 15:22:50 type: article metadata_visibility: show creators_name: Siddiqi, M.H. creators_name: Sulaiman, S. creators_name: Faye, I. creators_name: Ahmad, I. title: A Real time specific weed discrimination system using multi-Level wavelet decomposition ispublished: pub keywords: Euclidia note: cited By 17 abstract: The developed algorithm was used for the real time specific weed discrimination employing multi-level wavelet decomposition. This algorithm used four different types of wavelets i.e., Daubechies (bd4), Symlets (sym4), Biorthogonal (bior3.3) and Reverse Biorthogonal (rbio3.3) up to four levels of decomposition to classify images into broad and narrow class for real-time selective herbicide application using the Euclidian distance method. The lab, which have shown that the system to be very effective in weed identification, segmentation and discrimination. The test and analysis show that 97.26 classification accuracy over 350 sample images (broad & narrow) with 175 samples from each category of weeds and the proposed algorithm takes 29 ms as average time for the classification of the specific weeds. date: 2009 official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-76149088419&partnerID=40&md5=fb99d53e61775c2e3d08774d36df8643 full_text_status: none publication: International Journal of Agriculture and Biology volume: 11 number: 5 pagerange: 559-565 refereed: TRUE issn: 15608530 citation: Siddiqi, M.H. and Sulaiman, S. and Faye, I. and Ahmad, I. (2009) A Real time specific weed discrimination system using multi-Level wavelet decomposition. International Journal of Agriculture and Biology, 11 (5). pp. 559-565. ISSN 15608530