A Wheat Grain Variety Recognition Based on Deep Learning

This study introduces a deep learning-based approach to wheat grain variety recognition, a critical task for enhancing wheat quality and yield. It details the creation of a wheat kernel image dataset, capturing images from multiple angles of 22 varieties, followed by preprocessing steps to standardize and prepare the data for analysis. The study leverages convolutional neural networks (CNNs) and transfer learning to develop a model capable of identifying wheat varieties with high accuracy, addressing the limitations of traditional machine learning methods. The results demonstrate the superiority of deep learning in wheat kernel identification, with the optimized moachieving a remarkable recognition rate of 99.3%. This high performance is attributed to the ability of CNN s to automatically extract features and learn from the data, thus eliminating the need for manual feature engineering.