Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Smart Automated Agriculture System for Weed Detection

Author : Madhu M Nayak 1 Shilpashree J B 2 Parashiva Murthy B M 3

Date of Publication :7th June 2016

Abstract: Weed management is one of the costliest input to the agriculture and it is one of the un-mechanized area. To bring mechanization in this area the most important step is the detection of weed in agricultural field. Weed can be detected by using machine vision techniques. Machine vision uses special image processing techniques. Weeds in agricultural field can be detected by its properties such as Size, Shape, Spectral Reflectance, Texture features. In this paper we are demonstrating weed detection by its Size features. After the image acquisition Excessive green algorithm is developed to remove soil and other unnecessary objects from the image. Image enhancement techniques are used to remove Noise from the images, By using Labeling algorithm each components in the Image were extracted, then size based features like Area, Perimeter, longest chord and longest perpendicular chord are calculated for each label and by selecting appropriate threshold value Weed and Crop segmentation is done . Result of all features is compared to get the best result.

Reference :

    1. D.C. Slaughter, D.K. Giles, D. Downey Autonomous robotic weed control systems: A review. computers and electronics in agriculture 6 1 ( 2 0 0 8 ) 63–78
    2. Hong Y. Jeon ,, Lei F. Tian and Heping Zhu, Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination, Sensors 2011, 11, 6270-6283; doi:10.3390/s110606270
    3. S. Kiani, and A. Jafari, Crop Detection and Positioning in the Field Using Discriminant Analysis and Neural Networks Based on Shape Features, J. Agr. Sci. Tech. (2012) Vol. 14: 755-765
    4. Kamal N. Agrawal, Karan Singh, Ganesh C. Bora and Dongqing Lin, Weed Recognition Using ImageProcessing Technique Based on Leaf Parameters, Journal of Agricultural Science and Technology B 2 (2012) 899-908
    5. Xavier P. Burgos-Artizzu , Angela Ribeiro , Alberto Tellaeche , Gonzalo Pajares , Cesar FernándezQuintanilla Analysis of natural images processing for the extraction of agricultural elements. Image and Vision Computing 28 (2010) 138–149
    6. J. Romeo, G. Pajares, M.Montalvo, J.M. Guerrero, M. Guijarro, and A. Ribeiro, Crop Row Detection inMaize Fields Inspired on the Human Visual Perception, The ScientificWorld Journal Volume 2012, Article ID 484390
    7. Muhammad Asif, Samreen Amir, Amber Israr and Muhammad Faraz A Vision System for Autonomous Weed Detection Robot, International Journal of Computer and Electrical Engineering, Vol. 2, No. 3, June, 2010 1793- 8163 International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 826 Vol. 7, Issue 3, pp. 818-826
    8. Xavier P. Burgos-Artizzu,, Angela Ribeiroa, Alberto Tellaecheb, Gonzalo Pajaresc, Cesar FernándezQuintanilladImproving weed pressure assessment using digital images from an experience-based reasoning approach. computers and electronics in agriculture 6 5 ( 2 0 0 9 ) 176–185
    9. J. Bossua, Ch. Géea,∗, G. Jones, F. TruchetetbWavelet transform to discriminate between crop and weed in perspective agronomic images. computers and electronics in agriculture 6 5 ( 2 0 0 9 ) 133–143
    10. Sajad Kiani Crop-Weed Discrimination Via WaveletBased Texture Analysis Internatıonal Journal of Natural and Engineering Sciences 6 (2) : 7-11 , 2012
    11. Alberto Tellaeche, Gonzalo Pajares A computer vision approach for weeds identification through Support Vector Machines Applied Soft Computing 11 (2011) 908–915
    12. Lanlan Wu, Youxian Wen, Xiaoyan Deng and Hui Peng Identification of weed/corn using BP network based on wavelet features and fractal dimension. Scientific Research and Essay Vol.4 (11), pp. 1194-1200, November, 2009
    13. Anup Vibhute, S K Bodhe Applications of Image Processing in Agriculture: A Survey. International Journal of Computer Applications (0975 – 8887)
    14. Imran Ahmed, Awais Adnan, Muhammad Islam,Salim Gul, Edge based Real-Time Weed Recognition System for Selective Herbicides. IMECS 2008, 19-21 March, 2008, Hong Kong
    15. T. K. Das, Weeds and their Control Methods Division of Agronomy, Indian Agricultural Research Institute, New Delhi – 110 012
    16. Dr. A.R. Sharma, vision 2050 ,the Director, Directorate of Weed Science Research Jabalpur-482 004 (M.P.), India.

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