Agricultural　internet　of　things　(IOT)　and　sensor　technology　has　been　widely　used　in　the　informationalized　and　mechanized　orchard.　The　research　aimed　at　both　constructing　an　automatic-assistant　diagnosis　and　a　real-time　alerting　for　plant　disease　and　insect　pest.　The　purpose　also　covered　to　realize　an　unmanned　pest-disease　monitoring　and　to　release　some　human　interaction　in　making　a　diagnosis.　A　method　for　pathologic　image　recognition-diagnosis　based　on　deep　learning　neural　network　was　designed　and　an　innovative　method　for　updating　free　parameters　of　the　network　was　proposed　on　the　basis　of　analyzing　the　error　propagation　of　the　network,　so-called　the　gradient　descendent　with　flexible　momentum.　Then,　computer　recognizing　pathologic　images　of　fruit　sphere　was　researched　into　systematically,　where　the　apple　was　selected　as　a　subject.　Experiment　result　revealed　the　method　manifested　a　recall　rate　at　98.4%.　And　in　parallel　with　several　well-known　updating　schemes　based　momentum,　the　proposal　was　able　to　obviously　improve　the　accuracy　of　learning　network　with　a　flatter　converging　curve,　at　a　cost　of　short　converging　time.　The　test　upon　the　several　popular　benchmark　data-sets　also　demonstrated　it　could　perform　an　effective　recognition　on　the　image　pattern.　©,　2014,　Chinese　Society　of　Agricultural　Machinery.　All　right　reserved.