ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1490384
THz image recognition of moldy wheat based on multi-scale context and feature pyramid
Provisionally accepted
- Henan University of Technology, Zhengzhou, China
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In order to achieve high-precision recognition and class classification of wheat with different degrees of mold, a multi-scale context and feature pyramid based moldy wheat recognition network (MSCFP-Net) is proposed. Firstly, the network uses the residual network ResNeXt as the baseline network, and incorporates a multi-scale contextual feature extraction module, which is more helpful to determine the important discriminative regions in the whole image to extract more image detail features. In addition, a coordinated attention mechanism module is introduced to perform global average pooling from both directions to learn the importance of different regions in the input features in a dynamically weighted manner.Moreover, a bidirectional feature pyramid network is embedded into the baseline model, so that certain coarse-grained features and fine-grained features are retained in the processed output features at the same time to improve the network recognition accuracy. Compared with the baseline network, the four evaluation indexes of Accuracy, Precision, Recall and F1-Score of MSCFP-Net are improved by 1.08%, 1.25%, 0.53% and 0.91%, respectively. In addition, a series of comparison experiments and ablation experiments show that the classification network constructed in this paper has the best fine-grained classification performance for moldy wheat THz images.
Keywords: Terahertz, identification of moldy wheat, spectral image, image classification, deep learning
Received: 03 Sep 2024; Accepted: 16 May 2025.
Copyright: © 2025 Jiang, Chen, Ge, Wen, Jiang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yuying Jiang, Henan University of Technology, Zhengzhou, China
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