The invention belongs to the technical field of 
tobacco leaf air-curing, discloses a cigar 
tobacco leaf air-curing process stage identification method based on 
incremental learning, and provides a fusion model for identifying a 
tobacco leaf air-curing process stage by adopting an 
incremental learning mode based on an SGD logic classification 
algorithm. According to the model, data can be subjected to preprocessing and 
feature selection in combination with airing data characteristics collected by an airing room, and through an incremental training learning mode, the accuracy of judging the airing process stage is gradually improved, the tobacco airing process is optimized, the 
working pressure of tobacco growers is relieved, and the 
economic benefits of tobacco are improved. According to the method, a large amount of effective information is filled for tobacco leaf data features, the subsequent 
model prediction accuracy is improved, the problem of a large amount of 
noise in air-curing data is solved, and the model training efficiency is improved; the air-curing process stage is rapidly judged in real time, and meanwhile, the model learns again by utilizing subsequent data increment, so that the tobacco air-curing process is improved, and remote, intelligent and accurate air-curing is realized.