Abstract
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.
Highlights
For a long time, crop disease has been one of the most urgent problems in the field of agriculture
The remainder of the paper is arranged as follows: Chapter II describes the related literature; Chapter III introduces our IoT system and the residual network of the multidimensional feature compensation mechanism proposed in this article; Chapter IV analyzes the experimental results, and Chapter V describes the conclusions and recommendation for future research
Our model contains two ResNet-50 models and one ResNet34 model; we conclude that the compensation layer adds accuracy to our model is reasonable
Summary
Crop disease has been one of the most urgent problems in the field of agriculture. Hu et al.: MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases the basis of tradition and limited training with potentially high error rates They may not have access to latest information about crop disease treatments. They have achieved good recognition accuracy for diseases, they need more sophisticated recognition for the disease level To solve these limitations and contribute to agricultural production, we have combined deep learning with IoT technology to build an agrarian IoT system for crop disease identification. Our contributions are as follows: 1.) On the basis of deep learning and IoT technology, we build an end-to-end IoT system for crop disease identification This system can obtain crop disease information in time and feed it back to farmers. The remainder of the paper is arranged as follows: Chapter II describes the related literature; Chapter III introduces our IoT system and the residual network of the multidimensional feature compensation mechanism proposed in this article; Chapter IV analyzes the experimental results, and Chapter V describes the conclusions and recommendation for future research
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
