Leveraging generative adversarial networks and SE-UNet for high-precision tobacco leaf image segmentation and blend uniformity detection.

The uniformity of tobacco blend mixing is a critical factor influencing cigarette quality. However, due to the dynamic nature of the blending process and the complex characteristics of the materials involved, current detection methods primarily rely on manual sampling or offline analysis. These approaches suffer from poor real-time performance and lack representativeness. To address these limitations, this study explores methods for generating tobacco-leaf image data and proposes a real-time semantic segmentation technique for tobacco-leaf images based on U-Net. First, a small set of tobacco leaf images was acquired to establish a raw dataset of composite tobacco leaf images. Three generative adversarial networks-CycleGAN, WGAN, and DCGAN-were employed to generate tobacco leaf images from this dataset. Based on image quality, the optimal image generation network was selected. Experimental results showed that CycleGAN produced the highest-quality tobacco leaf images. Next, the Squeeze Excitation Attention (SE) mechanism was integrated into the VGG16 backbone network. This enhancement improved the network's ability to extract features from various types of tobacco leaf images while suppressing interference from irrelevant pixels. The results demonstrated that, compared to mainstream models such as Segformer, DeepLabV3, PSPNet, and the unmodified U-Net, the proposed SE-UNet model achieved superior segmentation accuracy, excellent real-time performance, and overall optimal segmentation capabilities. Compared to Segformer, DeepLabV3, PSPNet, and the original model, the MIOU in the evaluation metrics improved by 17.46, 8.7, 13.39, and 2.77 percentage points, respectively. Finally, the pixel areas of different tobacco leaf types were extracted and calculated from the segmented images to determine the proportion of each leaf type within the images. This research offers a novel approach for practical tobacco production and quality inspection, while also providing a new pathway for real-time online detection of other agricultural products.