Weed identification is a basic task in precision agriculture

as well as in the principle of variable spraying and accurate weeding. Field imaging spectrometer data

with both hyperspectral and high spatial resolutions

have potential applications in weed identification. Currently

methods for weed identification include considering shape features based on machine vision

which perform poorly when weeds and crops have similar shape features

while the use of hyperspectral features have low efficiency when it is identified in pixels. To overcome the limitations of these existing methods

a new weed identification method combining the strengths of both object-oriented and spectral feature matching approaches is proposed. The proposed method extracts and analyzes the shape features and the spectral curves of plant object samples

and builds a decision tree using shape feature rules and spectral angle mapper to identify the plant objects in the experimental field. The results show that the proposed method could identify different kinds of plant objects with similar shape features by using hyperspectral features

and could overcome the difficulties in identifying same objects with different spectra and different objects with the same spectrum by using shape feature rules. The identification accuracy of the described method is higher than both the spectral angle mapper method and the color and shape analysis.