This research proposes a saliency detecting approach based on an improved manifold ranking algorithm, aiming at the problem that green citrus has similar color features to the background in the natural environment, making the citrus difficult to be recognized. First, to avoid the increasing difficulty of recognizing caused by the uneven brightness of the green citrus images, the brightness improvement approach based on fuzzy set theory was employed to preprocess the orange images. Second, to resolve the issue that the traditional graph-based manifold ranking saliency detection algorithm relies on the boundary background to obtain the foreground seeds, resulting in the unsatisfactory effect of the saliency map, an approach combining relative total variation and local complexity was employed to extract more accurate foreground seeds. Finally, to sort the manifolds, the extracted foreground seeds were combined with the a priori saliency map of the boundary background without foreground seeds and the final saliency map was obtained. Experimental findings indicate that the proposed algorithm can recognize the green citrus region more effectively, and the segmentation accuracy, false-positive rate, and false-negative rate are 94%, 3.19%, and 1.64%, respectively.