Analysis and Testing of Weed Real-time Identification Based on Neural Network

  • Yan Shi
  • Haibo Yuan
  • Anbo Liang
  • Chunmei Zhang
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 259)

Contrasting the two green strength genes of soil, wheat, corn, and the weed, the paper designed a system to identify the weed from the crop. It used the 2G-R-B and BP neural network, with the help of pixel-position-histogram diagram, to calculate the area and position of weeds. The result showed that it could identify the weed from the field and crop with an accuracy of 93%. The program gave the result that running time of identifying weed in wheat field was 273.31ms. As far as the corn was concerned, the time was 321.94ms, In a word, the system can satisfy the request of real-time.

Keywords

variable spray weed identify picture disposal visual c++ 

References

  1. Blackmore B.S. Developing the principles of precision farming. Proceeding of Agrotech, Barretos. Brazil, 1999, 11(6):15-19Google Scholar
  2. Burks T. F., Shearer S. A., Gates R. S. Back propagation neural network design and evaluation for classifying weed species using color image texture. Transactions of the ASAE, 2000, 43 (4):1029-1037CrossRefGoogle Scholar
  3. Guyer D.E., G.E. Miles, L.D. Gaultney, and M.M. Schreiber. 1993. Application of machine vision to shape analysis in leaf and plant identification. Transactions of ASAE 36(1):163-171CrossRefGoogle Scholar
  4. Ji Shouwen, Wang Rongben, Chen Yajuan. Research on Recognizing Weed from Corn Seedling by Using Computer Image Processing Technology, Transactions of the Chinese Society of Agricultural Engineering, 2001(02) (in Chinese)Google Scholar
  5. Shi Yan, Study on the System of Spraying Rate Varied by Pressure of Liquid Chemical Application, Chinese Agriculture University, 2004 (in Chinese).Google Scholar
  6. Shi Yan, Qi Lijun, Fu Zetian. Model development and simulation of variable rate of pressure spray. Translations of the CSAE, 2004 (in Chinese)Google Scholar
  7. Thornson J.F., Stafford J.V., Potention for automatic weed detection and selective herbicide application. Corp Protection 1991, 10:254-259CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Yan Shi
    • 1
  • Haibo Yuan
    • 2
  • Anbo Liang
    • 3
  • Chunmei Zhang
    • 2
  1. 1.Architecture and engineering collegeQingdao Agricultural UniversityChina
  2. 2.College of Engine & Electronic EngineeringQingdao Agricultural UniversityChina
  3. 3.International Intercommunion SchoolQingdao Agricultural UniversityChina

Personalised recommendations