Municipal　solid　waste　(MSW)　incineration　processes　often　experience　fluctuations　in　the　physical　and　chemical　properties　of　waste　materials,　leading　to　unstable　combustion　and　increased　flue-gas　emissions.　This　hinders　the　development　of　greener　waste-treatment　methods.　Currently,　experts　rely　on　their　experience　in　observing　flames　during　field　operations　to　determine　the　combustion　state.　However,　this　process　is　energy　consuming,　subjective,　and　not　optimal　for　controlling　MSW　incineration　(MSWI).　To　address　this　issue,　this　study　proposes　a　dataset　of　MSWI　flame　images　for　identifying　combustion　states　using　a　vision-transformer-improved　deep　forest　classification　(ViT-IDFC)　algorithm.　The　ViT-IDFC　algorithm　extracts　multilayer　visual　transformation　features　from　flame　images　based　on　a　pre-trained　ViT　model＇s　transformer　coding　layer.　The　experience　of　domain　experts　was　used　to　select　the　depth　features.　The　algorithm　then　constructed　an　IDFC　model　by　combining　the　selected　ViT　visual　transformation　features　and　the　original　flame　image　as　inputs　for　the　cascade　forest.　The　proposed　method　was　evaluated　using　a　dataset　of　flame　images,　and　the　results　demonstrated　accuracies　of　98.15%　and　96.43%　in　identifying　the　left　and　right　grate　flame　images,　respectively,　which　are　acceptable　in　the　industry.　©　2023　Elsevier　Ltd