Pluckable-zone segmentation and plucking-point localization for multicultivar tea shoots using an improved lightweight DeepLabV3+ and DBSCAN–assisted 3D clustering

Accurate segmentation of pluckable zones and localization of plucking points are essential for intelligent tea harvesting. However, cultivar-specific traits, such as elongated internodes and low color contrast among buds, leaves, and stems, together with tea-shoot overlap and occlusion, hinder these tasks. To address these challenges, this study defines a pluckable zone for tea shoots and proposes a lightweight two-stage segmentation–localization pipeline. Stage I: an improved DeepLabV3+ model, termed DeepLabV3 + –MSEF, integrates a MobileNetV2 backbone, strip pooling–enhanced atrous spatial pyramid pooling, efficient channel attention, and focal loss for pluckable-zone segmentation. Stage II: two-dimensional (2D) plucking points are obtained from segmentation masks using a minimum bounding rectangle algorithm, and density-based spatial clustering of applications with noise (DBSCAN) is applied to the aligned three-dimensional (3D) point clouds to localize plucking points amid shoot overlap and occlusion. On an eight-cultivar test set, DeepLabV3 + -MSEF features 6.04 M parameters, runs at 60.14 frames per second (FPS), and achieves a mean intersection over union (mIoU) of 81.77% and an F1-score of 89.12%. Compared with the original DeepLabV3+ with an Xception backbone, it improves mIoU by 10.78 percentage points, reduces the parameter count from 54.71 M to 6.04 M, and nearly doubles inference speed from 30.36 FPS to 60.14 FPS, while achieving a favorable accuracy–efficiency tradeoff among benchmark models. Cultivar-wise testing demonstrates stable segmentation performance, with the mIoU ranging from 75.36% to 85.26% and F1-scores from 84.71% to 91.62%; residual errors mainly stem from variable illumination, occlusion by mature or fish leaves, and low color contrast among buds, leaves, and stems. The 2D localization stage achieves a cultivar-weighted success rate of 85.17% on 400 field images, while DBSCAN-based 3D point-cloud processing yields 92.00% plucking-point accuracy in occluded and overlapping shoot scenarios. These results demonstrate the potential of the proposed method for real-time, cross-cultivar intelligent tea harvesting.