Using　robots　for　tomato　truss　harvesting　represents　a　promising　approach　to　agricultural　production.　However,　incomplete　acquisition　of　perception　information　and　clumsy　operations　often　result　in　low　harvest　success　rates　or　crop　damage.　To　address　this　issue,　we　designed　a　new　method　for　tomato　truss　perception,　an　autonomous　harvesting　method,　and　a　novel　circular　rotary　cutting　end-effector.　The　robot　performs　object　detection　and　keypoint　detection　on　tomato　trusses　using　the　proposed　Top-down　Fusion　Network,　making　decisions　on　suitable　targets　for　harvesting　based　on　phenotyping　and　pose　estimation.　The　designed　end-effector　moves　gradually　from　the　bottom　up　to　wrap　around　the　tomato　truss,　cutting　the　peduncle　to　complete　the　harvest.　Experiments　conducted　in　real-world　scenarios　for　robotic　perception　and　autonomous　harvesting　of　tomato　trusses　show　that　the　proposed　method　increases　accuracy　by　up　to　11.42%　and　22.29%　for　complete　and　limited　dataset　conditions,　compared　to　baseline　models.　Furthermore,　we　have　implemented　an　automatic　tomato　harvesting　system　based　on　TDFNet,　which　reaches　an　average　harvest　success　rate　of　89.58%　in　the　greenhouse.　©　2004-2012　IEEE.