The Design of the Weeds Classification System Based on BP Neural Network
Author:
Publisher
Springer Berlin Heidelberg
Link
http://link.springer.com/content/pdf/10.1007/978-3-642-30223-7_99
Reference5 articles.
1. Marchant, J.A., Andersen, H.J., Onyango, C.M.: Evaluation of an imaging sensor for detecting vegetation using different waveband combinations. Computers and Electronics in Agriculture 32(2), 101–117 (2001)
2. Goel, P.K., Prasher, S.O., Patel, R.M., et al.: Use of airborne multi-spectral imagery for weed detection in field crops. Transactions of the ASAE 45(2), 443–449 (2002)
3. Alchanatis, V., Ridel, L., Hetzroni, A., et al.: Weed detection in multi-spectral images of cotton fields. Computers and Electronics in Agriculture 47(3), 243–260 (2005)
4. Karimi, Y., Prasher, S.O., Patel, R.M., et al.: Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture 51(2), 99–109 (2006)
5. Neto, J.-C., Meyer, G.E., Jones, D.D., et al.: Plant species identification using Elliptic Fourier leaf shape analysis. Computers and Electronics in Agriculture 50(2), 121–134 (2006)
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