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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1509656
This article is part of the Research Topic Optimizing Deep Learning for Effective Plant Species Recognition and Conservation View all 8 articles

LWheatNet: A Liteweight Convolutional Neural Network with Mixed Attention Mechanism for Wheat Seed Classification

Provisionally accepted
Xiaojuan Guo Xiaojuan Guo *Wang Jianping Wang Jianping Guohong Gao Guohong Gao Zihao Cheng Zihao Cheng Zongjie Qiao Zongjie Qiao Ranran Zhang Ranran Zhang Zhanpeng Ma Zhanpeng Ma Xing Wang Xing Wang
  • Henan Institute of Science and Technology, Xinxiang, China

The final, formatted version of the article will be published soon.

    With the advent of technologies such as deep learning in agriculture, a novel approach to classifying wheat seed varieties has emerged. However, some existing deep learning models encounter challenges, including long processing times, high computational demands, and low classification accuracy when analyzing wheat seed images, which can hinder their ability to meet real-time requirements. To address these challenges, we propose a lightweight wheat seed classification model called LWheatNet. This model integrates a mixed attention module with multiple stacked inverted residual convolutional networks. First, we introduce a mixed attention mechanism that combines channel attention and spatial attention in parallel. This approach enhances the feature representation of wheat seed images. Secondly, we design stacked inverted residual networks to extract features from wheat seed images. Each network consists of three core layers, with each core layer is comprising one downsampling unit and multiple basic units. To minimize model parameters and computational load without sacrificing performance, each unit utilizes depthwise separable convolutions, channel shuffle, and channel split techniques. To validate the effectiveness of the proposed model, we conducted comparative experiments with five classic network models: AlexNet, VGG16, MobileNet V2, MobileNet V3, and ShuffleNet V2. The results demonstrate that LWheatNet achieves the highest performance, with an accuracy of 98.59% on the test set and a model size of just 1.33 M. This model not only surpasses traditional CNN networks but also offers significant advantages for lightweight networks. The LWheatNet model proposed in this paper maintains high recognition accuracy while occupying minimal storage space. This makes it well-suited for real-time classification and recognition of wheat seed images on low-performance devices in the future.

    Keywords: wheat seed image, Wheat seed classification, mixed attention mechanism, Lightweight CNN, deep learning

    Received: 11 Oct 2024; Accepted: 12 Dec 2024.

    Copyright: © 2024 Guo, Jianping, Gao, Cheng, Qiao, Zhang, Ma and Wang. 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: Xiaojuan Guo, Henan Institute of Science and Technology, Xinxiang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.