Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC–ResNet) model for fine-grained disease identification in the system. MDFC–ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC–ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.

中文翻译：

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MDFC–ResNet：一种准确识别作物病害的农业物联网系统

作物病害诊断是作物病害治疗的重要步骤，是农业研究的热点问题。但是，在农业生产中，即使是同一种病害，不同等级的处理方法也不尽相同，仅识别农作物的粗粒病害是不够的。不恰当的处理不仅不能有效治疗疾病，还会影响作物产量和食品安全。我们将物联网技术与深度学习相结合，构建了用于作物细粒度病害识别的物联网系统。该系统可以自动检测作物病害并将诊断结果发送给农民。我们提出了一种多维特征补偿残差神经网络（MDFC​​-ResNet）模型，用于系统中的细粒度疾病识别。MDFC-ResNet 从三个维度进行识别，即物种、粗粒病害、和细粒度疾病，并建立一个补偿层，使用补偿算法融合多维识别结果。实验表明，与其他流行的深度学习模型相比，MDFC-ResNet 神经网络具有更好的识别效果，在实际农业生产活动中更具指导意义。