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    朱伟, 马立新, 张平, 等. 基于GoogLeNet和无人机图像的水稻秧苗形态识别[J]. 华南农业大学学报, 2022, 43(3): 99-106. DOI: 10.7671/j.issn.1001-411X.202107041
    引用本文: 朱伟, 马立新, 张平, 等. 基于GoogLeNet和无人机图像的水稻秧苗形态识别[J]. 华南农业大学学报, 2022, 43(3): 99-106. DOI: 10.7671/j.issn.1001-411X.202107041
    shu
    ZHU Wei, MA Lixin, ZHANG Ping, et al. Morphological recognition of rice seedlings based on GoogLeNet and UAV image[J]. Journal of South China Agricultural University, 2022, 43(3): 99-106. DOI: 10.7671/j.issn.1001-411X.202107041
    Citation: ZHU Wei, MA Lixin, ZHANG Ping, et al. Morphological recognition of rice seedlings based on GoogLeNet and UAV image[J]. Journal of South China Agricultural University, 2022, 43(3): 99-106. DOI: 10.7671/j.issn.1001-411X.202107041
    shu

    基于GoogLeNet和无人机图像的水稻秧苗形态识别

    Morphological recognition of rice seedlings based on GoogLeNet and UAV image

      摘要
    • 摘要:
      目的  针对目前国内评价插秧质量主要以人工观察和随机抽样的现状，提出一种基于卷积神经网络GoogLeNet 对水稻秧苗图像进行形态识别的方法。
      方法  首先，利用无人机超低空航拍获取清晰、完整的稻田秧苗图像，通过裁剪标记制作漂秧、伤秧和合格秧苗数据集；然后，基于GoogLeNet结构训练数据，得到最佳网络识别模型；最后，对单穴秧苗图像进行分类试验，并与传统图像分类算法(SVM、BP神经网络)进行对比。
      结果  在相同样本的条件下，基于GoogLeNet的秧苗形态识别方法更快、更准确地完成了判断分类，秧苗形态识别的平均正确率为91.17%，平均耗时0.27 s；与SVM和BP神经网络相比，分类平均精度分别提高了21和13个百分点，检测时间分别缩短了1.09 和0.58 s。
      结论  本研究可为水稻插秧质量评价提供相关支持。

       

      Abstract:
      Objective  In view of the current situation that the quality of transplanting is mainly based on manual observation and random sampling in China, it is proposed to use the convolutional neural network GoogLeNet to recognize the morphology of rice seedlings.
      Method  Firstly, clear and intact images of rice seedlings were obtained by UAV aerial photography at low altitude. Data sets of floating seedlings, damaged seedlings and qualified seedlings were made by cutting and marking. Then, based on the GoogLeNet structure training data, the optimal network recognition model was obtained. Finally, the image classification experiment of seedlings per hole was carried out, and compared with traditional image classification algorithms (SVM, BP neural network).
      Result  Under the condition of using the same samples, the seedling morphology recognition method based on GoogLeNet completed the judgment and classification was faster and more accurately. The average accuracy of seedling morphology recognition was 91.17%, and the average detection time was 0.27 s. Compared with SVM and BP neural network, the average classification accuracy increased by 21 and 13 percentage points respectively, and the detection time was shortened by 1.09 and 0.58 s respectively.
      Conclusion  This study can provide the relevant support for evaluation of rice transplanting quality.

       

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