In　recent　years,　the　escalating　labor　costs　in　agricultural　production　have　emerged　as　a　major　concern.　The　use　of　inspection　robots　to　achieve　automated　inspection　of　fruit　and　fruit　bunches　for　ripeness　not　only　enhances　production　efficiency　and　cost　savings,　but　also　simplifies　the　tasks　for　workers.　To　address　this　issue,　an　improved　YOLOv7-based　multi-task　deep　convolutional　neural　network　(DCNN)　detection　model,　called　MTDYOLOv7,　is　proposed　in　this　paper.　Initially,　the　dataset　labels　were　expanded　to　meet　the　requirements　of　multi-task　classification.　Two　additional　decoders　were　then　added　on　the　basis　of　YOLOv7　to　detect　tomato　fruit　clusters,　fruit　maturity　and　cluster　maturity.　Subsequently,　the　loss　function　was　designed　based　on　the　characteristics　of　multi-task　and　the　Scale-Sensitive　Intersection　over　Union　(SIoU)　was　used　instead　of　Complete　Intersection　over　Union　(CIoU)　to　improve　the　model＇s　recognition　accuracy.　Finally,　to　verify　the　effectiveness　of　the　algorithm,　tests　were　conducted　on　the　cherry　tomato　dataset,　and　comparisons　were　made　with　common　target　detection　algorithms,　classification　models,　and　cascade　models.　The　experimental　findings　reveal　that　MTD-YOLOv7　achieved　an　overall　score　of　86.6%　in　multi-task　learning,　with　an　average　inference　time　of　4.9　ms　(RTX3080).　It　excels　in　simultaneous　detection　of　cherry　tomato　fruits　and　bunches,　fruit　maturity,　and　bunch　maturity,　offering　exceptional　precision,　rapid　detection,　and　robust　generalization　capabilities.　Its　suitability　extends　to　various　applications,　notably　in　inspection　tasks.