Abstract: A coherent (noise-free and fully connected) branch prediction for fruit trees is the prerequisite for 3D reconstruction and is essential to guide robots in avoiding a collision. In this work, we propose a novel one-step regression deep-learning model, HOB-CNNv2, to detect coherent branches for fruit trees under varying levels of occlusions. Furthermore, we develop a post-processing algorithm, Coherent Semantic Merging Algorithm (CSMA), to take advantage of HOB-CNNv2’s coherent guarantee and a state-of-the-art semantic model’s accurate boundary detection. CSMA predicts partially occluded trees guaranteeing coherent predictions while preserving the state-of-the-art performance. We apply HOB-CNNv2, CSMA, and a representative baseline on three types of planar trees. Experimental results show the output of CSMA has the best overall performance across all the tree species and occlusion conditions. Additionally, HOB-CNNv2 and CSMA can guarantee 100% coherent predictions.