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Existing nursery inventory methods require people hand-counting, which is very labor consuming and not efficient. Using unmanned aerial vehicle (UAV) to facilitate counting the number of nursery-grown plants automatically with high accuracy provides an alternative to inventory management. The segmentation of individual plants in UAV images is the crucial step to achieve the plants counting task, which is challenging because of variations in illumination changes under natural conditions, the size difference between individual plants, the complicated background of the ground weeds and overlapping of adjacent plants. A spruce image segmentation algorithm based on fully convolutional networks (FCN) was proposed. Images were collected by using DIJ PHANTOM 4 in Inner Mongolia, in which 470 labeled spruce images with 300 images as training set, 170 images as test set, and 90 Pinus sylvestris images labeled as additional test set for comparing test results. To design FCN for accurate spruces segmentation, VGG16 was chosen as a basic network with the shared weights and the decreasing learning rate to improve the accuracy under Tensorflow framework. The results on the test set showed that FCN algorithm achieved effective spruces segmentation in spite of illumination changes, the size difference between individuals, the complicated background and the overlapping problem, with pixel accuracy (PA) of 0.86, mean pixel accuracy (MPA) of 0.86, mean intersection over union (MIoU) of 0.75 and frequency weighted intersection over union (FWIoU) of 0.76 at an average speed of 85 millisecond per image. Compared with K-means clustering segmentation algorithm and HSV threshold segmentation algorithm, the MIoU value of FCN algorithm was 0.10 and 0.38 higher, respectively. All of the test results showed that the proposed FCN algorithm provided an effective pipeline for plants segmentation.