DOI QR코드

DOI QR Code

분광특성 분석에 의한 논 잡초 검출의 기초연구

A Fundamental Study on Detection of Weeds in Paddy Field using Spectrophotometric Analysis


초록

This is a fundamental study to develop a sensor to detect weeds in paddy field using machine vision adopted spectralphotometric technique in order to use the sensor to spread herbicide selectively. A set of spectral reflectance data was collected from dry and wet soil and leaves of rice and 6 kinds of weed to select desirable wavelengths to classify soil, rice and weeds. Stepwise variable selection method of discriminant analysis was applied to the data set and wavelengths of 680 and 802 m were selected to distinguish plants (including rice and weeds) from dry and wet soil, respectively. And wavelengths of 580 and 680 nm were selected to classify rice and weeds by the same method. Validity of the wavelengths to distinguish the plants from soil was tested by cross-validation test with built discriminant function to prove that all of soil and plants were classified correctly without any failure. Validity of the wavelengths for classification of rice and weeds was tested by the same method and the test resulted that 98% of rice and 83% of weeds were classified correctly. Feasibility of CCD color camera to detect weeds in paddy field was tested with the spectral reflectance data by the same statistical method as above. Central wavelengths of RGB frame of color camera were tried as tile effective wavelengths to distingush plants from soil and weeds from plants. The trial resulted that 100% and 94% of plants in dry soil and wet soil, respectively, were classified correctly by the central wavelength or R frame only, and 95% of rice and 85% of weeds were classified correctly by the central wavelengths of RGB frames. As a result, it was concluded that CCD color camera has good potential to be used to detect weeds in paddy field.

키워드

참고문헌

  1. Borregaard, T., H. Nielsen, L. Norgaard and H. Have. 2000. Crop-weed discrimination by line imaging spectroscopy. J. agric. Engng Res. 75: 389-400. https://doi.org/10.1006/jaer.1999.0519
  2. Cho, S. I., D. S. Lee and J. Y. Jeong. 2000. Weed detection by machine vision and artificial neural network. Proceedings of ICAME 2000. 2:270-278. KSAM.
  3. El-Faki, M. S., N. Zhang and D. E. Peterson. 2000. Weed detection using color machine vision. Trans. of the ASAE. 43(6):1969-1978. https://doi.org/10.13031/2013.3103
  4. Steward, B. L. and L. F. Tian. 1998. Real-time machine vision weed sensing. ASAE Paper No. 98-3033. ASAE.
  5. Suh, S. R., J. H. Sung and G. C. Chung. 2001. Comparison of nutrient deficient stress diagnoses of cucumber plant using non-destructive physiological instruments. Agric. and Biosystems Engineering. 2(1):1-6, KSAM.
  6. Tang, L., L. Tian and B. Steward. 2000. Development of a low-cost machine vision system for selective sprayer. ASAE Paper No. 003064. ASAE.
  7. Vrindts, E., J. De Baerdemaeker and H. Ramon. 2002. Weed detection using canopy reflection. Precision Agriculture, 3:63-80. https://doi.org/10.1023/A:1013326304427
  8. Wang, Ning, N. Zhang, D. E. Perterson and F. E. Dowell. 2000. Testing of a spectral-based weed sensor. ASAE Paper No. 003127. ASAE.
  9. Woebbecke, D. M., G. E. Meyer, K. Von Bargen and D. A. Mortensen. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. of the ASAE. 38(1):259-269. https://doi.org/10.13031/2013.27838