Identification method of multi-feature weed based on multi-spectral images and data mining

Aimed to meet the identification accuracy requirements of variable spraying on weed, a new method using decision tree algorithm-C4.5 of data mining was developed to discriminate or classify crop and weeds by the multi-spectral images. The multi-spectral images of weeds and maize were captured by MS4100 Duncan Camera in the test field of Northwest Agriculture and Forestry University on May, 2012, and transformed from CIR color space to Lab systems, which can distinguish different quantized color and measure the Euclidean distance of different colors. Mathematical morphology was used to fill small holes among the extracted vegetation leaves, and connect the uncompleted contour line of the discontinuous edges which may be caused by noise, occlusion and other factors. The results showed that the average recognition rate of C4.5 algorithm was higher than that of the other two algorithms and it was an effective and feasible method to rapidly identify the weeds.