An adaptive enhancement algorithm based on nonlinear global brightness correction is proposed for leaf disease images with uneven illumination. First, the original image is preprocessed by Gaussian filtering and adaptive equalization, and the color space is transferred to HSV. The multi-scale Retinex algorithm is used to estimate the light component of the V component. Combining the optimal segmentation threshold of the bright and dark areas calculated by the maximum between class variance segmentation method (OTSU) and the constructed nonlinear brightness correction function, the brightness of the bright and dark areas is adaptively adjusted, and then the corrected V component is obtained by merging with the original V component. Gamma correction is performed on the S component of HSV space, and the reconstructed image is restored to RGB image. The experimental results show that the algorithm can effectively reduce the impact of uneven lighting on the image, guarantee the adaptive enhancement of bright areas while enhancing dark areas, and improve the image clarity and brightness uniformity. Compared with the contrast limited adaptive histogram equalization algorithm (CLAHE), nonlinear correction algorithm and color restoration multi-scale Retinex algorithm (MSRCR), it has better performance in terms of average gradient, information entropy, peak signal-to-noise ratio, and structure similarity.