# Data Enhancement for Plant Disease Classification Using Generated Lesions
## 1 Introduction  
Plant diseases have led to a significant decline in the production and quantity of crops worldwide [1]. A series of plant diseases, such as citrus canker [2], have caused billions of dollars in losses each year. In more severe cases, it has even led to the extinction of species; for example, Panama disease led to the extinction of the Gros Michel banana [3]. Currently, many plant diseases cannot be cured and can only be dealt with when they are detected. Plant diseases usually produce corresponding lesions; however, due to the complexity and diversity of diseases, the lesions are often identified by experts. Great economic loss is caused because these diseases cannot be treated early. If these diseases can be accurately identified and treated early, the economic losses will be greatly reduced and ecological disasters caused by disease transmission can be avoided.  
Traditional computer vision techniques seek to program a model that can identify a series of traits empirically. However, many plant lesion structures are complex and similar, and their colors are diverse, which makes the diagnosis of plant lesions difficult. Recently, machine learning and especially convolutional neural network models have exhibited considerable strengths for image recognition applications. However, most deep learning algorithms are too complex in network structures and require a large training set. For many plant lesion classification applications, the training instances and related data are scarce. Novel models and algorithms are in high demand that can utilize the scarcity of training images to yield a good recognition accuracy.  
Since [PERSON] et al. [4] proposed the generative adversarial network (GAN), the generated image quality has greatly improved. When a GAN is used to generate plant lesion images, generatingthe complete lesion leaf images directly will generate images with a very poor quality (see Appendix A, Figure A1, Table A1) due to the complexity of the lesion structures. We cropped the lesion area of the leaf image and generated the lesion images with a higher subjective effect. However, many plant lesions have similar structures, such as plant cankers caused by _Pseudomonas_, like citrus canker [2] and pitaya canker [5] (Figure 1). When only the lesion information is used, the classification network cannot accurately identify the lesion. It is necessary to use the lesion information and the leaf information to accurately identify the corresponding disease types (see Appendix A, Table A2). In order to solve the problem, many plant diseases require the lesion and leaf information to be accurately identified, but a GAN cannot generate a complete plant leaf image with a good quality. We propose a binarization plant lesion generation method that uses a binarization generator network with image edge smoothing (ES-BGNet).  
Our approach includes multiple steps:  
1. We first input the binarized image and cropped lesion images into a GAN to generate plant lesions with a specific shape. Meanwhile, we also introduced the dropout layer of the network [6] to solve the problem of image overfitting and improve the training speed.
2. We used the image pyramid [7] and the image edge smoothing algorithm [8] to solve the problem regarding synthesizing a complete lesion leaf image where the synthetic edge pixels are different and the network output size is fixed, but the real lesion size is random.