Unlike　conventional　hyperspectral　image　(HSI)　classification　in　general　scenes,　agricultural　HSI　classification　poses　greater　challenges　due　to　the　increased　occurrence　of　＂same　spectrum　different　object＂　and　＂different　spectrum　same　object＂　phenomena　caused　by　class　similarities.　Furthermore,　the　dense　spatial　distribution　of　land　cover　categories　in　agricultural　scenes　and　the　mixing　of　spatial-spectral　features　at　crop　boundaries　add　to　the　complexity　of　agricultural　HSIs.　To　tackle　these　issues,　we　propose　SANet,　a　network　designed　to　enhance　crop　classification.　SANet　integrates　spectral　and　contextual　information　while　emphasizing　self-correlation　within　the　HSIs.　It　combines　the　spatial-spectral　nonlocal　block　structure　and　the　multiscale　spectral　self-attention　(SSA)　structure,　allocating　more　attention　resources　to　spatial　and　spectral　dimensions　and　modeling　the　existing　correlations　within　the　spectral-spatial　domain.　Additionally,　we　introduce　a　two-branch　spatial-spectral　semantic　extraction　and　fusion　structure　that　can　adaptively　learn　results　from　both　branches.　Experimental　results　demonstrate　the　promising　performance　of　SANet　in　agricultural　HSI　classification　by　effectively　utilizing　spectral　data,　contextual　information,　and　self-attention　mechanisms.