Aiming at the problems of low target recognition rate and poor localization effect of cucumber in complex near color background,a cyclic fusion feature enhanced based on SSD(CFFE-SSD)target detection model is proposed.First, cyclic feature fusion is performed on the first four effective feature layers of SSD,so that the information of low-level feature layers and high-level feature layers can be effectively used.Secondly,in the view of special aspect ratio and over lapping phenomenon of the cucumber target,the K-means algorithm is used to improve the default size and aspect ratio of the a priori frame,and meanwhile,DIoU-NMS is proposed to replace ordinary NMS.Finally,the efficient channel attention module is introduced into the cyclic feature fusion module to enhance the feature extraction ability of the network.The ex-perimental results show that the AP@0.5 of the improved CFFE-SSD model proposed in this paper reaches 96.63%,an increase of 4.61%,and the AP@0.75 reaches 89.02%,an in-crease of 7.14%.The detection speed reaches 144 fps,and the frame regression accuracy is higher.Effectively meet the needs of automatic cucumber picking.