Multi-spectral imaging technique based on texture analysis and machine learning was proposed to discriminate alien
invasive weeds with similar outline but different categories. The objectives of this study were to investigate the
feasibility of using Multi-spectral imaging, especially the near-infrared (NIR) channel (800 nm±10 nm) to find the
weeds' fingerprints, and validate the performance with specific eigenvalues by co-occurrence matrix. Veronica polita
Pries, Veronica persica Poir, longtube ground ivy, Laminum amplexicaule Linn. were selected in this study, which
perform different effect in field, and are alien invasive species in China. 307 weed leaves' images were randomly
selected for the calibration set, while the remaining 207 samples for the prediction set. All images were pretreated by
Wallis filter to adjust the noise by uneven lighting. Gray level co-occurrence matrix was applied to extract the texture
character, which shows density, randomness correlation, contrast and homogeneity of texture with different algorithms.
Three channels (green channel by 550 nm±10 nm, red channel by 650 nm±10 nm and NIR channel by 800 nm±10 nm)
were respectively calculated to get the eigenvalues.Least-squares support vector machines (LS-SVM) was applied to
discriminate the categories of weeds by the eigenvalues from co-occurrence matrix. Finally, recognition ratio of 83.35%
by NIR channel was obtained, better than the results by green channel (76.67%) and red channel (69.46%). The
prediction results of 81.35% indicated that the selected eigenvalues reflected the main characteristics of weeds'
fingerprint based on multi-spectral (especially by NIR channel) and LS-SVM model.