Discrimination of rapeseed and weeds under actual field conditions based on principal component analysis and artificial neural network by VIS/NIR spectroscopy

Min Huang; Yidan Bao; Yong He

doi:10.1117/12.751317

15 November 2007 Discrimination of rapeseed and weeds under actual field conditions based on principal component analysis and artificial neural network by VIS/NIR spectroscopy

Min Huang, Yidan Bao, Yong He

Author Affiliations +

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67882S (2007) https://doi.org/10.1117/12.751317
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The study documented successful discrimination between five weed species and rapeseed plants under actual field conditions using visible and near infrared (Vis/NIR) spectroscopy. A hybrid recognition model, BP artificial neural networks (BP-ANN) combined with principal component analysis (PCA), had been established for discrimination of weeds in rapeseed field. Spectra tests were performed on the rapeseed and five-weed species canopy of 180 samples in the field using a spectrophotometer (325-1075 nm). 6 optimal PCs were selected as the input of BP neural networks to build the prediction model. Rapeseed samples were marked as 1, while the five weed species marked as 2, 3, 4, 5, 6, which were used as output set of BP-ANN. 120 samples were randomly selected as the training set, and the remainder as prediction set. It showed excellent predictions with the correlation value of 0.9745, and the relative standard deviation (RSD) was under 5% thus 100% of prediction accuracy was achieved. The results are promising for further work in real-time identification of weed patches in rapeseed fields for precision weed management.

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Min Huang, Yidan Bao, and Yong He "Discrimination of rapeseed and weeds under actual field conditions based on principal component analysis and artificial neural network by VIS/NIR spectroscopy", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882S (15 November 2007); https://doi.org/10.1117/12.751317

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