ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1603073
This article is part of the Research TopicMachine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume IIView all 30 articles
Corn variety identification based on improved EfficientNet lightweight neural network
Provisionally accepted
- Qingdao Agricultural University, Qingdao, China
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The authenticity of corn seeds is critical to yields and their market value. The screening of corn ears is an important step in the processing of corn seeds. In order to protect the intellectual property rights of corn varieties and realize intelligent ear screening, this article proposes an improved EfficientNet lightweight model, which uses deep learning technology to classify and identify corn ear images. First, 6529 RGB images of corn ears of five varieties were collected to construct a data set. Secondly, the number of MBConv modules in the EfficientNetB0 model was reduced, and the CBAM attention mechanism and dalition convolution were introduced to enhance the feature extraction capability. Finally, the Swish activation function was used to improve the stability of gradient transfer, and the SCD_EFTNet model was proposed. Experiments show that the proposed model has obvious advantages compared with mainstream models in indicators such as Recall, Precision, mAP, and inference time, and its mAP reaches 98.11%. The phenotypic characteristics of corn ears can be used to better classify and identify different varieties of corn, providing a reference for intelligent sorting of corn ears.
Keywords: Corn ear, Variety identification, Classification, EfficientNetB0, CBAM, Dilated Convolution
Received: 31 Mar 2025; Accepted: 29 May 2025.
Copyright: © 2025 Xu, Lan, Lü and Ma. 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: Jinhao Lan, Qingdao Agricultural University, Qingdao, China
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