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With the rapid development of rice phenotype research, rice disease research has also made significant progress as an essential part of rice phenotype research. Bacterial blight disease is one of the three major diseases of rice. Still, there is a lack of an effective spectral index for monitoring whether rice leaves are infected with bacterial blight. Taking rice leaves at the tillering stage as the research object, totally 200 pieces of rice leaves inoculated with Xanthomonas oryzae, and control group were collected respectively. A hyperspectral imaging device was used to obtain the spectral data of rice leaves in the band of 373~1033nm, and eventually, the band of 450~900nm was selected. The hyperspectral data of rice leaves in the wave band was used as a sample. The region of interest (ROI) was selected from each sample and the average spectrum was calculated. After applying Savtzky-Golay smoothing, the average spectrum curve was obtained. In order to quantitatively describe whether the rice leaves were infected or not, the spectral fractal dimension (FD) as a monitoring spectral index for quantitatively describing rice bacterial leaf blight disease was used. By analyzing the spectral index (SI) and FD, the multivariate linear relationship between SI and FD was established, and the effectiveness of FD and other commonly used monitoring indexes for bacterial blight monitoring were compared. The results showed that the response of rice bacterial leaf blight in the green peak (510~560nm) and red valley (650~690nm) spectrum was more sensitive;for healthy and susceptible leaves, there was a good relationship between FD and SI. The multivariate linear relationship of FD indicated that FD had a good corresponding relationship with the spectral curve, which can be used as a spectral index to quantitatively describe the health of leaves;compared with the commonly used monitoring index, the proposed disease monitoring index had a high correlation with whether rice was infected or not. The correlation coefficient reached 0.9840, and the distribution was more stable. The results indicated that the fractal dimension based on the spectral reflectance curve was feasible for judging whether rice leaves were infected with bacterial blight and provided a method for early monitoring of rice bacterial blight.