面向边缘计算的轻量级植物病害识别模型

  目的  传统深度学习模型因参数和计算量过大不适用于边缘部署，在网络边缘的植物病害自动识别是实现长时间大范围低成本作物监测的迫切需求。  方法  联合使用多种模型压缩方法，得到可部署于算力有限的嵌入式系统的轻量级深度卷积神经网络，在边缘节点实现植物病害智能识别。模型压缩分2个阶段:第1阶段利用基于L₁范数的通道剪枝方法，压缩MobileNet模型；第2阶段将模拟学习与量化相结合，在模型量化的同时恢复识别精度，得到高精度轻量级的端模型。  结果  在PlantVillage数据集58类植物病害的实验结果表明:通道剪枝将MobileNet压缩了3.6~14.3倍，量化又将模型的参数精度由32 bit降低至8 bit。整体压缩率达到了14.4~57.2倍，识别准确率仅降低0.24%~1.65%。与通道剪枝后无模拟学习训练、通道剪枝结合量化后无模拟学习训练这2种压缩方法相比，具有更高的模型压缩率和识别准确率。  结论  联合使用多种模型压缩方法可以少量的精度损失深度压缩人工智能模型，可为农林业提供面向边缘计算的植物病害识别模型。图3表2参23

Lightweight plant disease recognition model for edge computing

  Objective  The traditional deep learning model is not suitable for edge deployment because of too many parameters and too much calculation. Automatic identification of plant diseases on the edge of the network is urgently needed to realize long-term and large-scale low-cost crop monitoring.  Method  By using multiple model compression methods, a light weight deep convolution neural network was obtained, which could be deployed in the embedded system with limited computing power to realize intelligent identification of plant diseases at edge nodes. The model compression was divided into two stages. The first stage used the channel pruning method based on L₁ norm to compress the MobileNet model. In the second stage, simulation learning and quantization were combined to restore the recognition accuracy while the model was quantized, and a high-precision lightweight end model was obtained.  Result  Experimental results of 58 kinds of plant diseases in PlantVillage dataset showed that channel pruning compressed MobileNet by 3.6?14.3 times, and quantization reduced the parameter accuracy of the model from 32 bit to 8 bit. The overall compress rate reached 14.4?57.2 times, and the recognition accuracy was only reduced by 0.24% to 1.65%. Compared with the pruning method trained by common learning, and pruning with quantization trained by common learning, this method achieved higher compression rate and recognition accuracy.  Conclusion  The combination of multiple model compression methods can compress the artificial intelligence models in depth with only tiny loss of accuracy, and provide plant disease recognition models for agriculture and forestry based on edge computing. Ch, 3 fig. 2 tab. 23 ref.]