An ASD Field Spec Pro Full Range spectrometer was used to acquire thespectral reflectance of healthy and diseased leaves infected by riceAphelenchoides besseyi Christie, which were cut from rice individuals in thepaddy field. Firstly, foliar pigment content was investigated. As compared withhealthy leaves, the total chlorophyll and carotene contents (mg?g-1) of diseasedleaves decreased 18% and 22%, respectively. The diseased foliar content ratio oftotal chlorophyll to carotene was nearly 82% of the healthy ones. Secondly, theresponse characteristics of hyperspectral reflectance of diseased leaves wereanalyzed. The spectral reflectance in the blue (450-520 nm), green (520-590 nm)and red (630-690 nm) regions were 2.5, 2 and 3.3 times the healthy onesrespectively due to the decrease in foliar pigment content, whereas in the nearinfrared (NIR, 770-890 nm) region was 71.7 of the healthy ones because of leaftwist, and 73.7% for shortwave infrared (SWIR, 1 500-2 400 nm) region, owing towater loss. Moreover, the hyperspectral feature parameters derived from the rawspectra and the first derivative spectra were analyzed. The red edge position(REP) and blue edge position (BEP) shifted about 8 and 10 nm toward the shortwavelengths respectively. The green peak position (GPP) and red trough position(RTP) shifted about 8.5 and 6 nm respectively toward the longer wavelengths.Finally, the area of the red edge peak (the sum of derivative spectra from 680 to740 nm) and red edge position (REP) as the input vectors entered into C-SVC,which was an soft nonlinear margin classification method of support vectormachine, to recognize the healthy and diseased leaves. The kernel function wasradial basis function (RBF) and the value of punishment coefficient (C) wasobtained from the classification model of training data sets (n=138). Theperformance of C-SVC was examined with the testing sample (n=126), and healthyand diseased leaves could be successfully differentiated without errors. Thisresearch demonstrated that the response feature of spectral reflectance wasobvious to disease stress in rice leaves, and it was feasible to discriminatediseased leaves from healthy ones based on C-SVC model and hyperspectralreflectance.Christie; Derivative spectrum; Support vector classification machine(SVC)