Abstract: In order to intelligently monitor the status of tobacco leaves during flue-curing process, image acquisition devices were used to collect image data of tobacco leaves, and an recognition model for the yellowing and drying status of tobacco leaves was developed by using manual annotation and an improved EfficientNet-GECA model based on EfficientNet-B0 to analyze the changes of tobacco leaves at key temperature points during 354 flue-curing processes in three tobacco producing areas including Nanyang, Sanmenxia, and Pingdingshan in Henan Province. The results showed that: 1) Compared with the classic neural network models such as MobileNetV2, MobileNetV3, VGG16, ShuffleNetV2, ResNet50 and EfficientNet-B0, the EfficientNet-GECA model increased the recognition accuracies of the test set for the yellowing and drying status of tobacco leaves by 1.81 to 32.36 percentage points and 1.98 to 25.84 percentage points, respectively, with recognition accuracies of 88.74% and 80.47%, respectively. 2) The main yellowing status of tobacco leaves from Nanyang, Sanmenxia and Pingdingshan production areas at the key temperature points of 38, 40, 42, 45, 48 and 54 ℃ during the flue-curing process were relatively consistent, while there were significant differences in the drying status of tobacco leaves among the different production areas at the key temperature points including 40, 42 and 54 ℃. The established recognition model for tobacco leaf yellowing and drying status based on EfficientNet-GECA model can be used for intelligent monitoring of tobacco leaf status during flue-curing process, providing a basis for developing customized regulation and control strategies during tobacco leaf curing process.