Benchmarking analysis of CNN models for bread wheat varieties

Most of the wheat produced and consumed worldwide is generally bread wheat and is used for bread making. Bread wheat varieties can affect the quality of bread. When comparing bread wheat to other varieties, there may be differences in taste, cost, and impact on human health. This study aims to classify bread wheat varieties using deep learning methods. Wheat cultivars used in this research ('Ayten Abla', 'Bayraktar 2000', 'Hamitbey', 'Sanli', and 'Tosunbey') were obtained from the Central Field Crop Research Institute, Ministry of Agriculture and Forestry, Republic of Turkiye. First, a dataset of 8354 images of these wheat varieties was created. Then, the images in this dataset were trained with tree different Convolutional Neural Networks (CNNs) using the transfer learning method. The CNN models used are Inception-V3, Mobilenet-V2, and Resnet18, and the classification accuracies obtained are 97.37%, 97.07%, and 97.67%, respectively. Finally, the images not used for training and validation of the CNN models were segmented using image processing techniques. The segmented images were classified as bread wheat and unidentified seeds in the Resnet18 CNN model.