This　paper　presents　a　new　multi-task　neural　network,　called　BsiNet,　to　delineate　agricultural　fields　from　high-resolution　satellite　images.　BsiNet　is　modified　from　a　Psi-Net　by　structuring　three　parallel　decoders　into　a　sin-gle　encoder　to　improve　computational　efficiency.　BsiNet　learns　three　tasks:　a　core　task　for　agricultural　field　identification　and　two　auxiliary　tasks　for　field　boundary　prediction　and　distance　estimation,　corresponding　to　mask,　boundary,　and　distance　tasks,　respectively.　A　spatial　group-wise　enhancement　module　is　incorporated　to　improve　the　identification　of　small　fields.　We　conducted　experiments　on　a　GaoFen1　and　three　GaoFen2　satellite　images　collected　in　Xinjiang,　Fujian,　Shandong,　and　Sichuan　provinces　in　China,　and　compared　BsiNet　with　13　different　neural　networks.　Our　results　show　that　the　agricultural　fields　extracted　by　BsiNet　have　the　lowest　global　over-classification　(GOC)　of　0.062,　global　under-classification　(GUC)　of　0.042,　and　global　total　errors　(GTC)　of　0.062　for　the　Xinjiang　dataset.　For　the　Fujian　dataset　with　irregular　and　complex　fields,　BsiNet　outperformed　the　second-best　method　from　the　Xinjiang　dataset　analysis,　yielding　the　lowest　GTC　of　0.291.　It　also　produced　satisfactory　results　on　the　Shandong　and　Sichuan　datasets.　Moreover,　BsiNet　has　fewer　parameters　and　faster　computation　than　existing　multi-task　models　(i.e.,　Psi-Net　and　ResUNet-a　D7).　We　conclude　that　BsiNet　can　be　used　successfully　in　extracting　agricultural　fields　from　high-resolution　satellite　images　and　can　be　applied　to　different　field　settings.