Harnessing deep learning for wheat variety classification: a convolutional neural network and transfer learning approach.

ABSTRACT:

Background

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.

Results

The proposed CNN model, DenseNet201, MobileNet and InceptionV3 models achieved cl

SUBMITTER: Mengstu MT 

PROVIDER: S-EPMC12355334 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature