Weeds remain one of the most important factors affecting the yield and quality of corn in modern agricultural production. To use deep convolutional neural networks to accurately, efficiently, and losslessly identify weeds in corn fields, a new corn weed identification model, SE-VGG16, is proposed. The SE-VGG16 model uses VGG16 as the basis and adds the SE attention mechanism to realize that the network automatically focuses on useful parts and allocates limited information processing resources to important parts. Then the 3 × 3 convolutional kernels in the first block are reduced to 1 × 1 convolutional kernels, and the ReLU activation function is replaced by Leaky ReLU to perform feature extraction while dimensionality reduction. Finally, it is replaced by a global average pooling layer for the fully connected layer of VGG16, and the output is performed by softmax. The experimental results verify that the SE-VGG16 model classifies corn weeds superiorly to other classical and advanced multiscale models with an average accuracy of 99.67%, which is more than the 97.75% of the original VGG16 model. Based on the three evaluation indices of precision rate, recall rate, and F1, it was concluded that SE-VGG16 has good robustness, high stability, and a high recognition rate, and the network model can be used to accurately identify weeds in corn fields, which can provide an effective solution for weed control in corn fields in practical applications.

中文翻译：

---

基于改进VGG16的玉米杂草识别新模型

杂草仍然是现代农业生产中影响玉米产量和品质的最重要因素之一。为了利用深度卷积神经网络准确、高效、无损地识别玉米田杂草，提出了一种新的玉米杂草识别模型SE-VGG16。SE-VGG16模型以VGG16为基础，加入SE注意力机制，实现网络自动聚焦于有用的部分，并将有限的信息处理资源分配给重要的部分。然后将第一块中的3×3卷积核缩减为1×1卷积核，并将ReLU激活函数替换为Leaky ReLU，在降维的同时进行特征提取。最后对于VGG16的全连接层替换为全局平均池化层，并通过softmax进行输出。实验结果验证SE-VGG16模型对玉米杂草的分类优于其他经典和先进的多尺度模型，平均准确率达到99.67%，超过了原始VGG16模型的97.75%。综合查准率、召回率和F1三个评价指标，得出SE-VGG16鲁棒性好、稳定性高、识别率高，网络模型可用于玉米田杂草的准确识别，在实际应用中可为玉米田杂草防除提供有效的解决方案。