Identification of Weeds using Hsv Color Spaces and Labelling with Machine Learning Algorithms
S. Jeba Priya1, G. Naveen Sundar2, D. Narmadha3, Shamila Ebenezer4
1S. Jebapriya, Department of Computer Science Engineering, Karunya Institute of Technology and Sciences/ Karunya nagar, Coimbatore, India.
2Dr. G. Naveen Sundar, Department of Computer Science Engineering, Karunya Institute of Technology and Sciences/ Karunya nagar, Coimbatore, India.
3D. Narmadha, Department of Computer Science Engineering, Karunya Institute of Technology and Sciences/ Karunya nagar, Coimbatore, India.
4Dr. Shamila Ebenezer, Department of Computer Science Engineering, Karunya Institute of Technology and Sciences/ Karunya nagar, Coimbatore, India.
Manuscript received on 01 April 2019 | Revised Manuscript received on 07 May 2019 | Manuscript published on 30 May 2019 | PP: 1781-1786 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1191058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The goal of this project is to detect the weeds in the farmland, for proper distribution of sparing of herbicides in the farm. The crops are separated from the weeds with their color and feature of their appearance. In that cases the features of the weeds are extracted with HSV color space method, it produces higher accuracy comparing to RGB color space model. The extracted feature is compared with the trained data in Neural Networks for more accurate results comparing to SVM or BP methods. NN is used to divide the images into pixel for more accurate value. It can produce maximum of 95% accuracy comparing to other methods.
Keyword: Weed Identification, HSV, Feature Extraction, CNN, Image Separation.
Scope of the Article: Machine Learning