Insect　pest　is　one　of　the　main　causes　affecting　agricultural　crop　yield　and　quality　all　over　the　world.　Rapid　and　reliable　insect　pest　monitoring　plays　a　crucial　role　in　population　prediction　and　control　actions.　The　great　breakthrough　of　deep　learning　(DL)　technology　has　resulted　in　its　successful　applications　in　various　fields,　including　automatic　insect　pest　monitoring.　DL　creates　both　new　strengths　and　a　series　of　challenges　for　data　processing　in　smart　pest　monitoring　(SPM).　This　review　outlines　the　technical　methods　of　DL　frameworks　and　their　applications　in　SPM　with　emphasis　on　insect　pest　classification　and　detection　using　field　images.　The　methodologies　and　technical　details　evolved　in　insect　pest　classification　and　detection　using　DL　are　summarized　and　distilled　during　different　processing　stages:　image　acquisition,　data　preprocessing　and　modeling　techniques.　Finally,　a　general　framework　is　provided　to　facilitate　the　smart　insect　monitoring　and　future　challenges　and　trends　are　highlighted.　In　a　word,　our　purpose　is　to　provide　researchers　and　technicians　with　a　better　understanding　of　DL　techniques　and　their　state-of-art　achievements　in　SPM,　which　can　promote　the　implement　of　various　SPM　applications.