Abstract
Deep learning has recently shown promising results in plant lesion recognition. However, a deep learning network requires a large amount of data for training, but because some plant lesion data is difficult to obtain and very similar in structure, we must generate complete plant lesion leaf images to augment the dataset. To solve this problem, this paper proposes a method to generate complete and scarce plant lesion leaf images to improve the recognition accuracy of the classification network. The advantages of our study include: (i) proposing a binary generator network to solve the problem of how a generative adversarial network (GAN) generates a lesion image with a specific shape and (ii) using the edge-smoothing and image pyramid algorithm to solve the problem that occurs when 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. Compared with the recognition accuracy of human experts and AlexNet, it was shown that our method can effectively expand the plant lesion dataset and improve the recognition accuracy of a classification network.
Highlights
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]
We proposed a method to generate a plant lesion leaf image with a specific shape synthesize a complete plant lesion leaf image to improve the recognition accuracy of the classification and synthesize a complete plant lesion leaf image to improve the recognition accuracy of the network
We put the binarized image into the generator network and obtained a specific shape classification network
Summary
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. Plant diseases usually produce corresponding lesions; due to the complexity and diversity of diseases, the lesions are often identified by experts. Machine learning and especially convolutional neural network models have exhibited considerable strengths for image recognition applications. Most deep learning algorithms are too complex in network structures and require a large training set. Novel models and algorithms are in high demand that can utilize the scarcity of training images to yield a good recognition accuracy. Since Goodfellow 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, generating. Sci. 2020, 10, x FOR PEER REVIEW the complete lesion leaf images directly will generate images with a very poor quality
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