In　each　petrochemical　plant　around　the　world,　the　flare　stack　as　a　requisite　facility　produces　a　large　amount　of　soot　due　to　the　incomplete　combustion　of　flare　gas,　and　this　strongly　endangers　air　quality　and　human　health.　Despite　severe　damages,　the　abovementioned　abnormal　conditions　rarely　occur,　and,　thus,　only　few-shot　samples　are　available.　To　address　such　difficulty,　in　this　article,　we　design　an　image-based　flare　soot　density　recognition　network　(FSDR-Net)　via　a　new　ensemble　meta-learning　technology.　More　particularly,　we　first　train　a　deep　convolutional　neural　network　(CNN)　by　applying　the　model-agnostic　meta-learning　algorithm　on　a　variety　of　learning　tasks　that　are　relevant　to　the　flare　soot　recognition　so　as　to　obtain　the　general-purpose　optimized　initial　parameters　(GOIP).　Second,　for　the　new　task　of　recognizing　the　flare　soot　density　via　only　few-shot　instances,　a　new　ensemble　is　developed　to　selectively　aggregate　several　predictions　that　are　generated　based　on　a　wide　range　of　learning　rates　and　a　small　number　of　gradient　steps.　Results　of　experiments　conducted　on　the　density　recognition　of　flare　soot　corroborate　the　superiority　of　our　proposed　FSDR-Net　as　compared　with　the　popular　and　state-of-the-art　deep　CNNs.