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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39 For more details click here
National Academy of Agricultural Sciences (NAAS) : NAAS Score: *5.38 (2020) [Effective from January 1, 2020]For more details click here

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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(12): 589-597
DOI: https://doi.org/10.20546/ijcmas.2019.812.077


A Review on Recently Developed Technologies for Weed Recognition and Herbicide Application based on Digital Image Processing
Pankaj Malkani*, K. R. Asha, Atish Sagar, Abhinav Dubey,
AkshayKumar Singh and Prashant Singh
Department of Agricultural Engineering, ICAR-Indian Agricultural Research Institute,New Delhi-110012, India
*Corresponding author
Abstract:

Weed competes with a crop for nutrition, soil and water and, reduces its yields drastically. Conventional methods i.e. manual, mechanical and chemical mean have some limitations in controlling weeds. With the advancement in electronics and computers, Site-Specific Weed Management (SSWM) can provide a solution for precise weeds management. SSWM technologies with basic components and functions are described. SSWM consists of 3 basic processes i.e. image sensing, crop-weed discrimination, and chemical application. Digital image processing plays a crucial role in crop-weed discrimination and fascinates chemical applications.


Keywords: SSWM, Weed management, Crop-Weed discrimination, Image processing, smart herbicide applicator
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How to cite this article:

Pankaj Malkani, K. R. Asha, Atish Sagar, Abhinav Dubey, AkshayKumar Singh and Prashant Singh. 2019. A Review on Recently Developed Technologies for Weed Recognition and Herbicide Application based on Digital Image Processing.Int.J.Curr.Microbiol.App.Sci. 8(12): 589-597. doi: https://doi.org/10.20546/ijcmas.2019.812.077