Mengstu Mahtem Teweldemedhin, Taner Alper
Ondokuz Mayıs University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Samsun, Turkey.
Department of Agricultural Engineering, Hamelmalo Agricultural College|, Keren, Eritrea.
J Sci Food Agric. 2025 Sep;105(12):6692-6705. doi: 10.1002/jsfa.14378. Epub 2025 May 24.
Computer 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.
The 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.
The 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. © 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.
由于计算设备成本较低，计算机视觉和基于图像的解决方案作为非破坏性食品评估方法正越来越受到关注。与小麦品种的实际数量相比，已开展的小麦分类模型开发研究是基于有限的数据和较少的类别。为评估卷积神经网络（CNN）模型的适用性，本研究准备了124个小麦品种的多视图图像。使用深度学习（DL）方法，从头开发了一个四层CNN模型，并使用迁移学习对流行架构DenseNet201、MobileNet和InceptionV3进行了训练。
所提出的CNN模型、DenseNet201、MobileNet和InceptionV3模型的分类准确率分别达到了95.40%、92.41%、90.54%和83.47%，结果表明它们既具有前景又很成功。尽管存在与高计算资源需求相关的挑战，但新提出的CNN模型优于预训练模型。可以推断，多视图、大图像数据集对该模型在对124个小麦品种进行具有挑战性的分类任务中成功实现有前景的准确率做出了重大贡献。
本研究建议进一步微调超参数以提高所提出的CNN模型的准确率并确定更好的配置。此外，应评估其他流行模型。此外，通过冻结特定的早期层，应进行微调以最大化准确率。此外，所使用的图像数据集将公开提供，以便研究人员发现对小麦品种进行分类的新方法。© 2025作者。《食品与农业科学杂志》由约翰·威利父子有限公司代表化学工业协会出版。
