A predictive system for the classification of cereal species and varieties

Abstract

• Developed a method using Convolutional Neural Networks to automate the identification of cereal grains. • Collected 13.854 high-resolution grain images across four cereal species and six commercial varieties for model training and evaluation. • Evaluated three CNN architectures (VGG16, ResNet50, and Xception) to benchmark classification performance. • ResNet50 and Xception models achieved 97% test accuracy on variety level. Cereals are fundamental to global food supply, and their seeds are the raw materials for many food industries. Grain industry testing and certification workflows include classification of cereal species. Traditional identification relies on expert’s knowledge and is conducted through manual visual assessment, a time-consuming process which can often be subjective. Thi