Tea sprouts segmentation via machine vision is the core technology of tea automatic picking. A novel method for Tea Sprouts Segmentation based on improved deep convolutional encoder-decoder Network (TS-SegNet) is proposed in this paper. In order to increase the segmentation accuracy and stability, the improvement is carried out by a contrastive-center loss function and skip connections. Therefore, the intra-class compactness and inter-class separability are comprehensively utilized, and the TS-SegNet can obtain more discriminative tea sprouts features. The experimental results indicate that the proposed method leads to good segmentation results, and the segmented tea sprouts are almost coincident with the ground truth.

The segmentation performance of fresh tea sprouts is inadequate due to the uncontrollable posture. A novel method for Fresh Tea Sprouts Segmentation based on Capsule Network (FTS-SegCaps) is proposed in this paper. The spatial relationship between local parts and whole tea sprout is retained and effectively utilized by a deep encoder-decoder capsule network, which can reduce the effect of tea sprouts with uncontrollable posture. Meanwhile, a patch-based local dynamic routing algorithm is also proposed to solve the parameter explosion problem. The experimental results indicate that the segmented tea sprouts via FTS-SegCaps are almost coincident with the ground truth, and also show that the proposed method has a better performance than the state-of-the-art methods.