Intelligent Plant Leaf Disease Detection Using Generative Adversarial Networks: a Case-study of Cassava Leaves

Abstract

Background Cassava leaf disease detection is a major problem since it is very difficult to identify the disease in naked-eye observation and even experts such as agricultural scientists can fail in this task. The approach we use in this paper has the potential to overcome this problem. Materials and Methods In this, we propose an approach based on artificial intelligence for leaf disease detection using deep learning with generative adversarial networks (GAN). Our experimental study used a dataset including 12880 cassava leaf pictures generated using CycleGAN showing five major disease classes. In order to avoid overfitting, a GAN architecture is proposed for data augmentation using two networks, i.e ., a Generator and a Discriminator. The generator is trained to generate similar data samples as the original data Results The proposed approach achieved an accuracy of 99.51% for the classification of healthy or unhealthy leaf images, which outperformed existing methods. Discussion The discriminator is trained to distinguish between the unique and generated sample records, as actual or fake. To classify cassava images into five categories of diseases, a combination of machine learning models has been trained on original and generated images. The proposed approach showed better accuracy compared to the existing methods. Conclusion The proposed deep learning-based method can be used as a tool for early disease diagnosis in cassava leaf disease detection and classification

Plant phenotyping relevance

カッサバ葉の画像から病害状態を推定・分類する深層学習手法の開発と評価が中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper uses the Kaggle cassava leaf disease dataset and CycleGAN-generated images, but provides no authors' public code, models, or dataset deposit; the data availability statement only points to the article itself.

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