Discrimination of Crop and Weeds on Visible and Visible/Near-Infrared Spectrums Using Support Vector Machine, Artificial Neural Network and Decision Tree

Weeds are regarded as farmers' natural enemy. In order to avoid excessive pesticide residues, the destruction of ecological environment, and to guarantee the quality and safety of agricultural products, it is urgent to develop highly-efficient weed management methods. Amongst, weed discrimination is the key part. There have been a lot of researches on weed detection/discrimination using spectral measurement on plant leaf/canopy. However, as reported so far the spectral ranges from the researches were not consistent and no research was reported to determine more efficient wavelength range for weed classification. Some researchers adopted visible spectrum, some adopted near-infrared spectrum, the others adopted both visible and near-infrared spectrum. The purpose of this study was to compare the classifications of the spectral reflectance in range of 350 ~ 760 nm and in 350 ~ 2500 nm for crop/weed discrimination. Through spectral analysis of these data respectively using three kinds of modeling methods of Support Vector Machines (SVMs), Artificial Neural Network (ANN), and Decision Tree (DT), the results showed that the three classifiers could differentiate crop and weeds better in 350 ~ 760 nm wavelength range than in 350 ~ 2500 nm. Therefore, the visible wavelength range could be good enough to meet the requirement for crop/weed spectral discrimination, which might reduce the cost of weed detect sensors. Copyright © 2014 IFSA Publishing, S. L.