Tobacco Leaf Vein Segmentation Method Based on Hyperspectral Imaging and GAN - SA - UNet Algorithm

As an important feature of plants, leaf veins contain physiological and genetic Information. Aiming at the problems of blurred edge segmentation and low segmentation aceuracy of small veins in complex leaf texture State, a GAN — SA — UNet vein segmentation algorithm was proposed with tobacco leaves as the research object. The spectral Information of veins and leaves was obtained by hyperspectral imaging teehnology, and the principal component analysis (PCA) was used to reduce the dimension and obtain the composite map. On this basis, the spatial attention mechanismwas introduced to capture the key spatial features and improve the segmentation aceuracy. At the same time, the adversarial network was introduced to optimize the generated results and improve the robustness of vein segmentation. The results showed that the Interpretation rate of the first three principal components of PCA of leaf vein and leaf surface spectrum was 95. 71%, and the spectral characteristics of the two after dimension reduction showed obvious separability. The first three principal components composite map could highlight the difference between leaf surface and leaf vein, and highlight the characteristics of leaf vein. The GAN — SA — UNet segmentation algorithm can capture the vein features in complex leaf texture images. The segmentation aceuracy and intersection over union were 98. 93% and 66. 23%, respectively. Compared with the original model, they were increased by 0. 18 percentage points and 4.21 percentage points, respectively. The inference time of single image was 4 ms. It showed strong generalization ability and efficient and accurate recognition ability in the verification test of different producing areas, parts, grades and types of tobacco leaves. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.