Video　sensors　and　agricultural　IoT　(internet　of　things)　have　been　widely　used　in　the　informationalized　orchards.　In　order　to　realize　intelligent-unattended　early　warning　for　disease-pest,　this　paper　presents　convolutional　neural　network　(CNN)　early　warning　for　apple　skin　lesion　image,　which　is　real-time　acquired　by　infrared　video　sensor.　More　specifically,　as　to　skin　lesion　image,　a　suite　of　processing　methods　is　devised　to　simulate　the　disturbance　of　variable　orientation　and　light　condition　which　occurs　in　orchards.　It　designs　a　method　to　recognize　apple　pathologic　images　based　on　CNN,　and　formulates　a　self-adaptive　momentum　rule　to　update　CNN　parameters.　For　example,　a　series　of　experiments　are　carried　out　on　the　recognition　of　fruit　lesion　image　of　apple　trees　for　early　warning.　The　results　demonstrate　that　compared　with　the　shallow　learning　algorithms　and　other　involved,　well-known　deep　learning　methods,　the　recognition　accuracy　of　the　proposal　is　up　to　96.08%,　with　a　fairly　quick　convergence,　and　it　also　presents　satisfying　smoothness　and　stableness　after　convergence.　In　addition,　statistics　on　different　benchmark　datasets　prove　that　it　is　fairly　effective　to　other　image　patterns　concerned.　Copyright　©　by　HIGH　TECHNOLOGY　LETTERS　PRESS.