In　the　complex　environment　of　greenhouses,　it　is　important　to　provide　the　picking　robot　with　accurate　information.　For　this　purpose,　this　paper　improves　the　recognition　and　detection　method　based　on　you　only　look　once　v5　(YOLO　v5).　Firstly,　adding　data　enhancement　boosts　the　network　generalizability.　On　the　input　end,　the　k-means　clustering　(KMC)　was　utilized　to　obtain　more　suitable　anchors,　aiming　to　increase　detection　accuracy.　Secondly,　it　enhanced　multi-scale　feature　extraction　by　improving　the　spatial　pyramid　pooling　(SPP).　Finally,　non-maximum　suppression　(NMS)　was　optimized　to　improve　the　accuracy　of　the　network.　Experimental　results　show　that　the　improved　YOLO　v5　achieved　a　mean　average　precision　(mAP)　of　97.3%,　a　recall　of　90.5%,　and　an　F1-score　of　92.0%,　while　the　original　YOLO　v5　had　a　mAP　of　95.9%　and　a　recall　of　85.6%;　the　improved　YOLO　v5　took　57ms　to　identify　and　detect　each　image.　The　recognition　accuracy　and　speed　of　the　improved　YOLOv5　are　much　better　than　those　of　faster　region-based　convolutional　neural　network　(Faster　R-CNN)　and　YOLO　v3.　After　that,　the　improved　network　was　applied　to　identify　and　detect　images　take　in　unstructured　environments　with　different　illumination,　branch/leave　occlusions,　and　overlapping　fruits.　The　results　show　that　the　improved　network　has　a　good　robustness,　providing　stable　and　reliable　information　for　the　operation　of　tomato　picking　robots.