Abstract: Abstract: Accurate segmentation of the potato buds is the premise of seed potatoes automatic cutting. In order to improve the accuracy of buds segmentation, the fast segmentation on potato buds with chaos optimization-based K -means algorithm was realized in this paper. K-means algorithm has the merits of effectiveness and easy to implement, while the problem of easily trapping into the local optima hinders its accuracy of clustering. Chaotic systems can carry out overall searches by chaotic variables at high speed with ergodicity and non-repetition. The main idea of the proposed algorithm was searching with chaotic variables by mapping them into the range of the variables in K-means algorithm and eventually achieved global optimization. Experimental results demonstrated that the proposed algorithm outperformed the state-of-the-art K-means and FCM (fuzzy C-means) algorithm, whether in segmentation accuracy or running time. The average running time for K-means and FCM algorithm to segment the buds in one image was 2.895 5 and 3.556 4 s, respectively, however, it only took 1.109 s with the proposed algorithm. And running time was less effected by clustering number when it was more than 3, namely, the proposed algorithm could provide a less running time when applied to other crops, which were suitable to be segmented into 3 or more clusters. Moreover, results also verified that the better selection of the clustering number for the samples in this paper was no more than 3. Finally, for the potatoes in buds segmentation precision experiment, the proposed algorithm could achieve a total segmentation precision of 98.87% and without any buds omission. Thereinto, the segmentation precision for normal potatoes was 100%, and that for special ones was 91.67%. Consequently, the fast segmentation of potato buds with the proposed algorithm could lay a solid foundation for future automatic cutting of seed potatoes.