Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

由于实际环境的复杂性，直接利用农业物联网系统的原位图像进行植物叶片疾病的识别建模和诊断具有挑战性。为了克服这一缺点，提出了一种基于小样本和深度卷积神经网络的方法，用于在田间条件下进行黄瓜叶病的识别。提出了一种两阶段分割方法，通过从黄瓜叶片中提取病斑来获取病灶图像。随后，在执行旋转和平移之后，将病变图像输入到激活重建生成对抗网络中以进行数据增强，以生成新的训练样本。最后，为了提高黄瓜叶病的识别准确性，我们提出了使用生成的训练样本进行训练的扩张和初始卷积神经网络。实验结果表明，该方法在炭疽病，霜霉病和白粉病三种病害的病原和原始田间病叶图像数据集上实现时，平均识别准确率分别为96.11％和90.67％。同行，表明它为服务农业物联网的现场应用提供了良好的潜力。