Abstract: Traditional wheat variety identification relies on manual recognition and microscopic observation, which is prone to subjective errors and low efficiency. With the increasing demand for efficient and precise management in modern agriculture, neural network technology has demonstrated remarkable potential in wheat variety identification due to its powerful feature extraction and autonomous learning capabilities. This review aims to systematically analyze the application progress of existing neural network algorithms, including traditional artificial neural networks (ANN), convolutional neural networks (CNN) and their variants to hybrid models-in wheat variety identification. The paper focuses on summarizing their advantages and disadvantages, identifying the challenges faced by existing technologies, and prospecting future research directions (such as further optimizing neural network models and introducing new algorithms), with the expectation of improving identification accuracy and promoting sustainable agricultural development.