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Leafminer is a vegetable insect pest. The visible spectral reflectance of the leafminer (Liriomyza sativae Blanchard) infected plant leaves was measured. Damaged degrees (DD) of leaves were worked out with the image processing technology, and the sensitive wavelengths related to them were also selected via spectroscopy analysis. Using the support vector machine (SVM) and the multi-spectral method, the spectral classifying experiments were done and the classifying models were set up to recognize the leaf spectra with different DD. The results that used the SVM and the multi-spectral methods were contrasted. The results showed that the classifying precisions of the SVM are 93.8% (using the polynomial-based kernel function) and 96.9% (using the RBF kernel function), which excelled to the multi-spectral method (whose classifying precision is 90%).