Recently,　deep　learning-based　fruit　detection　applications　have　been　widely　used　in　the　modern　fruit　industry;　however,　the　training　data　labeling　process　remains　a　time-consuming　and　labor-intensive　process.　Auto　labeling　can　provide　a　convenient　and　efficient　data　source　for　constructing　smart　orchards　based　on　deep-learning　technology.　In　our　previous　study,　based　on　a　labeled　source　domain　fruit　dataset,　we　used　a　generative　adversarial　network　and　a　fruit　detection　model　to　achieve　auto　labeling　of　unlabeled　target　domain　fruit　images.　However,　since　the　current　method　uses　one　species　source　domain　fruit　to　label　multiple　species　target　domain　fruits,　there　is　a　problem　of　the　domain　gap　in　both　the　foreground　and　the　background　between　the　training　data　(retaining　the　source　domain　fruit　label　information)　and　the　application　data　(target　domain　fruit　images)　of　the　fruit　detection　model.　Therefore,　we　propose　a　domain-adaptive　anchor-free　fruit　detection　model,　DomAda-FruitDet,　and　apply　it　to　the　previously　proposed　fruit　labeling　method　to　further　improve　the　accuracy.　It　consists　of　2　design　aspects:　(a)　With　a　foreground　domain-adaptive　structure　based　on　double　prediction　layers,　an　anchor-free　method　with　multiscale　detection　capability　is　constructed　to　generate　adaptive　bounding　boxes　that　overcome　the　foreground　domain　gap;　(b)　with　a　background　domain-adaptive　strategy　based　on　sample　allocation,　we　enhance　the　ability　of　the　model　to　extract　foreground　object　features　to　overcome　the　background　domain　gap.　As　a　result,　the　proposed　method　can　label　actual　apple,　tomato,　pitaya,　and　mango　datasets,　with　an　average　precision　of　90.9%,　90.8%,　88.3%,　and　94.0%,　respectively.　In　conclusion,　the　proposed　DomAdaFruitDet　effectively　addressed　the　problem　of　the　domain　gap　and　improved　effective　auto　labeling　for　fruit　detection　tasks.