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A Method for Obtaining the Number of Maize Seedlings Based on the Improved YOLOv4 Lightweight Neural Network

文献类型： 外文期刊

作者： Gao, Jiaxin 1 ; Tan, Feng 1 ; Cui, Jiapeng 2 ; Ma, Bo 4 ;

作者机构： 1.Heilongjiang Bayi Agr Univ, Coll Elect & Informat, Daqing 163000, Peoples R China

2.Heilongjiang Bayi Agr Univ, Coll Agr Engn, Daqing 163000, Peoples R China

3.Heilongjiang Acad Agr Mechanizat Sci, Branch Suihua, Suihua 152054, Peoples R China

4.Heilongjiang Acad Agr Sci, Qiqihar Branch, Qiqihar 161006, Peoples R China

关键词： maize seedlings; detection; YOLOv4; improved Ghostnet; k-means clustering; attention mechanism

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 10 期

页码：

收录情况： SCI

摘要： Obtaining the number of plants is the key to evaluating the effect of maize mechanical sowing, and is also a reference for subsequent statistics on the number of missing seedlings. When the existing model is used for plant number detection, the recognition accuracy is low, the model parameters are large, and the single recognition area is small. This study proposes a method for detecting the number of maize seedlings based on an improved You Only Look Once version 4 (YOLOv4) lightweight neural network. First, the method uses the improved Ghostnet as the model feature extraction network, and successively introduces the attention mechanism and k-means clustering algorithm into the model, thereby improving the detection accuracy of the number of maize seedlings. Second, using depthwise separable convolutions instead of ordinary convolutions makes the network more lightweight. Finally, the multi-scale feature fusion network structure is improved to further reduce the total number of model parameters, pre-training with transfer learning to obtain the optimal model for prediction on the test set. The experimental results show that the harmonic mean, recall rate, average precision and accuracy rate of the model on all test sets are 0.95%, 94.02%, 97.03% and 96.25%, respectively, the model network parameters are 18.793 M, the model size is 71.690 MB, and frames per second (FPS) is 22.92. The research results show that the model has high recognition accuracy, fast recognition speed, and low model complexity, which can provide technical support for corn management at the seedling stage.

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