# Buckwheat Disease Recognition Based on Convolution Neural Network
## 1 Introduction
### Disease Recognition Based on Deep Learning in Agriculture  
Conventional disease recognition has been limited by slow speed, strong subjectivity, high misjudgment rate, and inefficiency, therefore, it no longer meets the needs of modern agricultural production. Buckwheat mostly grows in mountainous areas, so there is a shortage of agricultural technicians, often leading to missed optimal periods of disease control [5]. Fortunately, with the development of machine learning and pattern recognition, it is feasible to classify and identify buckwheat pests and diseases in agriculture. In recent years, many new methods of pattern recognition have emerged for image classification, detection, and recognition. These technologies have been beneficial in improving the identification efficiency, reducing the cost, increasing the identification accuracy, and easing the burden of experts [6; 7; 8]. Researchers have developed the classifiers of pests and diseases in agricultural crops by support vector machine [9], K-means clustering [10], radial basis function [11], genetic algorithm [12], Bayesian classification [13], integrated learning [14], filter segmentation [15], and so on, and have achieved good results. With the rise of deep learning, deep structure learning technologies, such as convolutional neural networks and recurrent neural networks have made constant progress. Much attention has been paid to carrying out the automatic identification of pests and diseases by using deep learning techniques. In the above research, the recognition of crop diseases mainly focuses on the field crops. Field crops are planted extensively and it is relatively easy to collect and select samples; the sample database established is standard. In the image samples, morbid and healthy crops are relatively clear and the image quality is good; therefore, the current deep learning framework can achieve a better recognition effect on these field crops. However, as a multigrain crop, buckwheat has no standard image database and is mostly planted in mountainous areas; there exist many limitations for sampling, including that the illumination of samples is uneven, and the leaves are overlapping, etc. As a consequence, the image of buckwheat is low quality, containing serious noise. In this paper, a two-level inception structure is inserted into the basic framework of the convolutional neural network to accurately extract the features of buckwheat images and improve the accuracy of classification as well. The proposed approach is capable of dealing with overlapping leaves and low-quality images. Meanwhile, in order to reduce the influence of light in the sampling process, convolution based on cosine similarity is adopted instead of traditional convolution operation, so that better feature extraction can be carried out for samples with uneven light, and finally, accurate recognition of buckwheat disease can be realized. In agricultural production, automatic classification has been widely applied to crop disease images, which is a key technology for pesticide selection and spraying in precision agriculture [16]. Feature extraction is an important technique for identifying crop diseases. However, it is not flexible to traditional classification algorithms based on artificial feature extraction, as it requires high requirements of professional knowledge, high time complexity, and difficulty in extracting high-quality features [17]. Deep learning can obtain the multi-scale feature of crop diseases, realize the characteristic expression of different diseases more accurately, and it is beneficial to the accurate identification of crop diseases [18]. [PERSON] et al. presented a deblurring method for local blurred images based on deep learning. By training convolution neural network models with different structures, a normalized segmentation algorithm based on spectral theory was used to segment the image of plant diseases and insect pests.