Crop　disease　is　one　of　the　most　important　influencing　factors　for　agricultural　high　yield　and　high　quality.　Accurate　classification　of　diseases　is　a　key　and　basic　step　for　early　disease　monitoring,　diagnostics　and　prevention.　The　optimal　individual　classifier　design　is　currently　the　common　limitation　in　most　crop　disease　recognition　methods　based　images.　To　improve　the　accuracy　and　stability　of　disease　identification,　a　disease　recognition　method　of　cucumber　leaf　images　via　dynamic　ensemble　learning　was　proposed.　The　approach　consisted　of　three　major　stages.　Firstly,　totally　75-dimension　color　features　of　leaf　image　were　extracted　with　image　block　processing.　Secondly,　a　disagreement　approach　was　used　to　measure　the　diversity　among　10　classifiers　of　neural　networks　with　an　ensemble　technique,　where　the　classifiers　were　ordered　according　to　the　diversity.　Finally,　with　the　confidence　of　classifiers,　a　classifier　subset　was　dynamically　selected　and　integrated　to　identify　the　images　of　crop　leaf　diseases.　To　verify　the　effectiveness　of　the　proposed　method,　classification　experiments　were　performed　on　images　of　four　kinds　of　cucumber　leaf　tissues,　including　512　samples　composed　of　powdery　milder,　downy　mildew,　gray　mold　and　normal　leaf.　The　experimental　results　showed　that　the　recognition　error　rate　of　the　proposed　method　was　3.32%,　compared　with　those　of　BP　neural　network,　SVM,　Bagging　and　AdaBoost　methods,　it　was　reduced　by　1.37　percentage　point,　1.56　percentage　point,　1.76　percentage　point　and　0.78　percentage　point,　respectively.　The　proposed　method　identified　the　diseases　accurately　from　cucumber　leaf　images.　Moreover,　the　method　was　feasible　and　effective,　and　it　can　also　be　utilized　and　modified　for　the　classification　of　other　crop　diseases.　©　2017,　Chinese　Society　of　Agricultural　Machinery.　All　right　reserved.