To　address　the　limitations　inherent　to　conventional　automated　harvesting　robots　specifically　their　suboptimal　success　rates　and　risk　of　crop　damage,　we　design　a　novel　bot　named　AHPPEBot　which　is　capable　of　autonomous　harvesting　based　on　crop　phenotyping　and　pose　estimation.　Specifically,　In　phenotyping,　the　detection,　association,　and　maturity　estimation　of　tomato　trusses　and　individual　fruits　are　accomplished　through　a　multi-task　YOLOv5　model　coupled　with　a　detection-based　adaptive　DBScan　clustering　algorithm.　In　pose　estimation,　we　employ　a　deep　learning　model　to　predict　seven　semantic　keypoints　on　the　pedicel.　These　keypoints　assist　in　the　robot＇s　path　planning,　minimize　target　contact,　and　facilitate　the　use　of　our　specialized　end　effector　for　harvesting.　In　autonomous　tomato　harvesting　experiments　conducted　in　commercial　greenhouses,　our　proposed　robot　achieved　a　harvesting　success　rate　of　86.67%,　with　an　average　successful　harvest　time　of　32.46　s,　showcasing　its　continuous　and　robust　harvesting　capabilities.　The　result　underscores　the　potential　of　harvesting　robots　to　bridge　the　labor　gap　in　agriculture.