# Buckwheat Disease Recognition Based on Convolution Neural Network
## 1 Introduction
### Disease Recognition Based on Deep Learning in Agriculture
This method has good robustness, generalization, and accuracy for the segmentation of pests and diseases in agricultural disease image recognition [19]. [PERSON] et al. proposed a generative adversarial network with dual-attention and topology-fusion mechanisms (DATFGAN), which can effectively transform unclear images into clear and high-resolution images. The weight sharing scheme in DATFGAN can significantly reduce the number of parameters. For recognizing the processed image, the result shows that DATFGAN is superior to other methods and has sufficient robustness [20]. In the field of deep learning, many learning frameworks have been developed for image feature extraction and applied in agricultural pest and disease detection. [PERSON] et al. proposed a detection method for rice pests based on deep learning [21]. They used VGG16 and Inception V3 structures and fine-tuned them to detect and identify rice pests and diseases. [PERSON], [PERSON] et al. used a migration learning mechanism to obtain the DenseNet network through pre-training on the ImageNet database and Inception module, which is used for the identification of rice pests and diseases. Compared with other methods, this method has better recognition performance and lower training costs [22]. [PERSON] et al. proposed a video detection architecture based on deep learning to detect plant diseases and insect pests by video [23]. In addition, this paper proposes a set of video detection evaluation indexes based on machine learning classifier, which can effectively evaluate the quality of video detection. Compared with VGG16, ResNet-50, ResNet-101, and YOLOv3, this network structure system is more suitable for detecting diseases and pests in untrained rice videos. In the literature [24], CNN is also used to identify maize leaf disease. It can identify three major maize diseases in southern Africa; maize leaf blight, common rust (sorghum rust), and gray spot (brown spot). [PERSON] et al. used VGG16 as the eighth convolution layer feature extractor and multi-class support vector machine (MSVM) to classify diseases and pests in eggplant, and achieved good results [25]. On the basis of analyzing state-of-the-art convolution neural networks, AlexNet, GoogleNet, Inception V3, ResNet18, and ResNet 50, [PERSON] et al. used the improved GoogleNet model to identify tomato pests and diseases [26]. Furthermore, [PERSON] et al. combined visual object recognition and language generation models to generate detailed information about plant abnormal symptoms and scene interactions. In the identification task of tomato pests and disease, the method achieved 92.5% accuracy in the dataset of tomato plant anomaly [27]. [PERSON] et al. used the Aprioro algorithm to find the association rules between climatic factors and the occurrence of cotton pests and diseases, the prediction of pests and diseases was attributed to time series prediction, and a prediction method based on LSTM (long short-term memory) was presented. The results show that suitable temperature, humidity, low rainfall, low wind speed, suitable sunshine time, and low evaporation are the main factors causing cotton pests and diseases [28]. [PERSON], [PERSON] et al. used Capsule Net to classify potato diseases and achieved good results [29]. At present, the detection of crop diseases is mostly carried out in the standard crop sample database. The standard sample database often has obvious disease characteristics through expert screening, and the imaging conditions and image quality are reliable. The existing deep learning methods for identifying crop diseases and insect pests do not consider the complexity of disease images collected in the field, such as leaf overlap, uneven lighting, shadow coverage, etc. These challenges greatly affect the recognition effect. Therefore, how to adapt to a complex imaging environment and accurately extract key features is an important direction of crop disease recognition research.