Automated　recognition　of　insect　category,　which　currently　is　performed　mainly　by　agriculture　experts,　is　a　challenging　problem　that　has　received　increasing　attention　in　recent　years.　The　goal　of　the　present　research　is　to　develop　an　intelligent　mobile-terminal　recognition　system　based　on　deep　neural　networks　to　recognize　garden　insects　in　a　device　that　can　be　conveniently　deployed　in　mobile　terminals.　State-of-the-art　lightweight　convolutional　neural　networks　(such　as　SqueezeNet　and　ShuffleNet)　have　the　same　accuracy　as　classical　convolutional　neural　networks　such　as　AlexNet　but　fewer　parameters,　thereby　not　only　requiring　communication　across　servers　during　distributed　training　but　also　being　more　feasible　to　deploy　on　mobile　terminals　and　other　hardware　with　limited　memory.　In　this　research,　we　connect　with　the　rich　details　of　the　low-level　network　features　and　the　rich　semantic　information　of　the　high-level　network　features　to　construct　more　rich　semantic　information　feature　maps　which　can　effectively　improve　SqueezeNet　model　with　a　small　computational　cost.　In　addition,　we　developed　an　off-line　insect　recognition　software　that　can　be　deployed　on　the　mobile　terminal　to　solve　no　network　and　the　time-delay　problems　in　the　field.　Experiments　demonstrate　that　the　proposed　method　is　promising　for　recognition　while　remaining　within　a　limited　computational　budget　and　delivers　a　much　higher　recognition　accuracy　of　91.64%　with　less　training　time　relative　to　other　classical　convolutional　neural　networks.　We　have　also　verified　the　results　that　the　improved　SqueezeNet　model　has　a　2.3%　higher　than　of　the　original　model　in　the　open　insect　data　IP102.　©　2021　China　Agricultural　University