Harnessing Deep Learning for Wheat Variety Classification: A Convolutional Neural Network and Transfer Learning Approach

Abstract

BACKGROUNDComputer vision and the use of image-based solutions are gaining traction as non-destructive food assessment methods because of the low costs of computational equipment. Research conducted on the development of wheat classification models has been based on limited data and a smaller number of classes compared to the availability of wheat varieties. To assess the applicability of convolutional neural network (CNN) models, the present study prepared multi-view images of 124 wheat varieties. Using deep learning (DL) methods, a four-layered CNN model was developed from scratch, and popular architectures, DenseNet201, MobileNet and InceptionV3 were trained using transfer learning.RESULTSThe proposed CNN model, DenseNet201, MobileNet and InceptionV3 models achieved classification accuracies of 95.40%, 92.41%, 90.54% and 83.47%, respectively, and they were found to be both promising and successful. Despite the challenges related to high computational resource demands, the newly proposed CNN model outperformed the pretrained models. It can be inferred that the multi-view, large-image dataset contributed significantly to the model's success in achieving promising accuracy in the challenging task of classifying 124 wheat varieties.CONCLUSIONThe present study recommends further fine-tuning of hyperparameters to improve the accuracy of the proposed CNN model and to identify better configurations. Besides, other popular models should be evaluated. Moreover, by freezing specific early layers, fine-tuning should be performed to maximize accuracy. Additionally, the image datasets used will be publicly available to allow researchers to discover new methodologies to classify wheat varieties. (c) 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.