The laser induced breakdown spectroscopy (LIBS) combined with chemometrics was used to qualitatively detect Huanglongbing in citrus leaves. The results showed that the LIBS signal intensities of elements P(Ⅱ),Mn(Ⅰ),Si(Ⅰ) and Fe(Ⅰ) in citrus leaves were directly related to the healthiness of citrus leaves,where the intensity of the characteristic peaks of P(Ⅱ),Mn(Ⅰ),Si(Ⅰ) and Fe(Ⅰ) in healthy,moderately and severely infected citrus leaves had a decreasing trend. Five characteristic spectrum were analyzed and fused together by spectral fusion to establish a partial least squares discriminant analysis (partial least square,PLS) model,in which the root mean square error (RMSEC) of the modeling set for Fe(Ⅰ) was 0.394,the correlation coefficient (Rc) of the modeling set was 0.871,and the total false positive rate was 23.1%. The prediction set mean square error (RMSEP) was 0.454,and the prediction set correlation coefficient (Rp) was 0.841,with an overall misspecification rate of 26.6%.The RMSEC for spectral fusion was 0.341 and Rc was 0.905,with an overall false positive rate of 15.5%,and the RMSEP was 0.395 and Rp was 0.867,with an overall false positive rate of 22.7%. Meanwhile,four pre-processing methods including normalization,multiplicative scatter correction (MSC),standard normal variate (SNV) and orthogonal signal correction (OSC) were used to reduce the effects of noise and errors on the spectra,and to establish the PLS model. Results of the LIBS technique combined with orthogonal signal correction (OSC) spectral pre-processing and partial least squares (PLS) modeling methods showed that the RMSEC was 0.027 and Rc was 0.994 〖JP2〗with a total false positive rate of 0. The RMSEP was 0.023 and Rp was 0.995 with a total false positive rate of 0. The three categories of citrus leaves were better classified. It is indicated that the feasibility of using LIBS technology to detect nutrients in citrus leaves. It will provide a method for rapidly detecting Huanglongbing in citrus leaf.