Visual　foundation　models　(VFMs)　pretrained　on　large-scale　training　datasets　show　robust　zero-shot　adaptability　across　many　vision　tasks.　However,　there　still　exist　limitations　in　remote　sensing　processing　tasks　due　to　the　variety　and　complexity　of　remote　sensing　images.　In　this　letter,　we　propose　a　two-flow　network　(TFNet)　based　on　multitask　VFM,　named　TFNet,　to　extract　croplands　with　well-delineated　boundaries　from　high-resolution　remote　sensing　images.　TFNet　consists　of　a　mask　flow　and　a　boundary　flow.　It　first　uses　a　VFM　as　visual　encoder　to　obtain　universal　semantic　features　regarding　croplands　and　then　aggregates　them　into　the　two　flows.　Next,　a　boundary　prior-guided　module　(BPM)　is　developed　to　incorporate　boundary　semantics　derived　from　the　boundary　flow　into　the　mask　flow,　to　refine　the　boundary　details　of　croplands.　We　also　develop　a　multibranch　parallel　fusion　module　(MPFM)　that　aggregates　multiscale　contextual　information　to　improve　the　identification　of　cropland　with　varied　sizes　and　shapes.　Finally,　a　semantic　consistency　loss　is　introduced　to　further　optimize　the　feature　learning　of　cropland　information.　We　conducted　extensive　experiments　on　Shandong　(SD)　and　Xinjiang　(XJ)　datasets　collected　from　Gaofen-2　(GF-2)　satellites　and　compared　our　method　with　five　existing　methods.　Experimental　results　show　that　the　croplands　extracted　by　our　method　have　the　fewest　omissions　and　errors,　achieving　the　highest　attribute　accuracy　(intersection　over　union　(IoU)　of　0.863　and　0.945)　and　lowest　geometric　errors　(global　total　classification　(GTC)　of　0.134　and　0.097)　than　other　methods　on　the　two　datasets.　Our　method　effectively　distinguished　croplands　of　varied　sizes,　shapes,　and　spectra,　even　in　scenarios　with　limited　samples.　Code　and　datasets　are　available　at　https://github.com/long123524/TFNet.