Research on the Application of Image Recognition Technology in Nondestructive Testing of Tobacco Leaf Structure

In the current era of rapid technological development, as a key technology in the field of computer vision, image recognition technology is playing an increasingly important role in the tobacco industry, especially in the nondestructive testing of tobacco leaf structures, showing great potential. This paper comprehensively explores the application of image recognition technology in the structural analysis of tobacco leaves, from theory to practice, from image preprocessing to feature extraction, and then to the application of deep learning models. It systematically explains how this technology can revolutionize the quality control system of the tobacco industry. Through precise image preprocessing, background noise has been effectively removed, and the contrast of the leaf structure has been enhanced, creating favorable conditions for subsequent feature extraction. By adopting advanced edge detection technology and texture analysis, combined with the automatic feature learning capability of deep learning, we have successfully captured the subtle features of the leaves, including texture, shape, and vein details, significantly improving the accuracy and automation level of detection. This paper uses advanced edge detection technology and texture analysis, supplemented by the automatic feature learning ability of deep learning, to successfully capture the subtle features of leaves, including texture, shape, and vein details, significantly improving the accuracy and automation level of detection. Meanwhile, this paper delves into the impact of environmental factors, particularly temperature and humidity, on the spread of infection in tobacco leaves, providing a scientific basis for disease management and prevention strategies.