Conventional AlexNet has the problems of slow training speed, single characteristic scale and low recognition accuracy. To solve these problems, a convolutional neural network identification model based on Inception module and dilated convolution is proposed in this study. The inception module combined with dilated convolution, could extract disease characteristics at different scales and increase the receptive field. By setting different parameters, six improved models were obtained. They were trained to identify 26 diseases of 14 different crops; then the authors selected optimal recognition model. On this basis, the segmented dataset and the grey-scaled dataset were trained as comparative experiments to explore the influence of background and colour features on the recognition results. After only two training epochs, the improved optimal model could achieve an accuracy of over 95%. Moreover, the final average identification accuracy reached 99.37%. Contrast experiments indicate that colour and background features may influence the recognition effect. The improved model can extract disease information from different scales in the feature map to identify diverse diseases of different crops. The proposed model has faster training speed and higher recognition accuracy than the traditional model, and thus it can provide a reference for crop disease identification in actual production.

中文翻译：

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使用改进的卷积神经网络识别农作物病害

传统的AlexNet存在训练速度慢，特征量表单一，识别精度低的问题。为解决这些问题，提出了一种基于Inception模块和扩张卷积的卷积神经网络识别模型。初始模块与膨胀卷积相结合，可以提取不同规模的疾病特征并增加接受范围。通过设置不同的参数，获得了六个改进的模型。他们经过培训可以识别14种不同农作物的26种病害；然后作者选择了最佳识别模型。在此基础上，将分割数据集和灰度数据集作为对比实验进行训练，以探索背景和颜色特征对识别结果的影响。经过两个训练时期 改进后的最优模型可以达到95％以上的精度。此外，最终的平均识别准确率达到了99.37％。对比实验表明颜色和背景特征可能会影响识别效果。改进的模型可以从特征图中的不同比例提取疾病信息，以识别不同作物的多种疾病。与传统模型相比，该模型具有更快的训练速度和更高的识别精度，因此可以为实际生产中的作物病害识别提供参考。改进的模型可以从特征图中的不同比例提取疾病信息，以识别不同作物的多种疾病。与传统模型相比，该模型具有更快的训练速度和更高的识别精度，因此可以为实际生产中的作物病害识别提供参考。改进的模型可以从特征图中的不同比例提取疾病信息，以识别不同作物的多种疾病。与传统模型相比，该模型具有更快的训练速度和更高的识别精度，因此可以为实际生产中的作物病害识别提供参考。