Large　scale　pest　recognition　is　one　of　crucial　components　in　pest　management　in　outdoor　conditions,　which　is　much　more　difficult　than　common　object　recognition　because　of　the　variational　image　acquisition　direction,　location,　pest　size　and　complex　image　background.　To　overcome　the　challenges,　this　study　proposes　a　CNN　model　by　combining　spatial　attention　mechanism　and　channel　attention　mechanism　to　realize　accurate　pest　location　and　recognition　in　field　images.　The　proposed　model　consists　of　two　major　parts.　Firstly,　the　module　Spatial　Transformer　Networks　(STN)　is　incorporated　into　a　Convolutional　Neural　Network　(CNN)　architecture　to　provide　image　cropping　out　and　scale-normalization　of　the　appropriate　region,　which　can　simplify　the　subsequent　classification　task.　The　second　one　is　called　Improved　Split-Attention　Networks　that　is　used　to　enable　feature-map　attention　across　feature-map　groups.　The　proposed　model　is　evaluated　on　three　different　datasets:　Li＇s　dataset　(10　species),　proposed　dataset　(58　species)　and　IP102　dataset　(102　species),　achieving　the　classification　accuracies　of　96.78%,　96.50%　and　73.29%,　respectively.　Comparisons　with　five　traditional　CNN　models　and　three　attention-related　state-of-the-art　deep　learning　models　show　that　the　current　method　outperforms　these　previous　models.　Besides,　to　verify　the　robustness　of　this　proposed　model　on　different　image　resolutions,　six　datasets　with　different　image　resolutions　are　constructed　and　all　accuracies　exceed　92%　with　the　image　resolution　of　400　x　267　pixels　reaching　the　optimal　performance.　All　results　show　that　the　proposed　method　provides　a　reliable　solution　to　recognize　insect　pest　in　field　and　support　precision　plant　protection　in　agriculture　production.