Deep Learning Approach for Practical Plant Disease Diagnosis

• 資料種別: 博士論文
• タイトル: Deep Learning Approach for Practical Plant Disease Diagnosis
• 著者・編者: HUU QUAN, Cap
• 著者標目: HUU QUAN, Cap
• 出版年月日等: 2021-09-15
• 出版年（W3CDTF）: 2021-09-15
• 授与機関名: 法政大学 (Hosei University)
• 授与年月日: 2021-09-15
• 授与年月日（W3CDTF）: 2021-09-15
• 報告番号: 甲第525号
• 学位: 博士(工学)
• 博論授与番号: 甲第525号
• 本文の言語コード: eng
• 件名標目: Automated plant disease diagnosis; cucumber diseases; deep learning; computer vision
• 対象利用者: 一般
• 一般注記: type:Thesis; With the breakthrough of deep learning techniques, many excellent applications for the automated diagnosis of plant disease have been proposed. However, there are several open issues for developing practical plant disease diagnosis systems in real cultivation. Firstly, most conventional methodologies only accept narrow range images, typically one or quite a limited number of targets are in their inputs. Applying these models to wide-angle images in large farms would be very time-consuming, since many targets (e.g., leaves) need to be diagnosed. In this work, we propose a two-stage system which has independent leaf detection and leaf diagnosis stages for wide-angle disease diagnosis. We show that our proposal attains a promising disease diagnostic performance that is more than six times higher than end-to-end systems (state-of-the-art detection methods like Faster R-CNN or SSD) with F1-score of 33.4 - 38.9% compared to 4.4 - 6.2% on an unseen target dataset.Secondly, the lack of image resolution (i.e., diagnosing from low-quality input images such as low-resolution, blur, poor camera focus, etc.) could significantly reduce the diagnostic performance in practice. Also, high-resolution data is very difficult to obtain and are not always available in practice. Deep learning-based techniques, and particularly generative adversarial networks (GANs), can be applied to generate high-quality super-resolution images, but these methods often produce unexpected artifacts that can lower the diagnostic performance. In this paper, we propose a novel artifact-suppression super-resolution method that is specifically designed for diagnosing leaf disease, called LASSR. Our LASSR can detect and suppress artifacts to a considerable extent. Thus, generating much more pleasing, high-quality images from low-resolution inputs. Experiments show that training with data generated by our proposal significantly boosts the performance on an unseen test dataset by over 21% compared with the baseline.; Thirdly, collecting and labeling training disease data for these diagnosis systems requires solid biological knowledge and is very labor-intensive. Limited amount of disease training data leads to the fourth problem of model overfitting. The performance of disease diagnostic models are drastically decreased when used on test data sets from new environments. Meanwhile, we observe that healthy images are easier to collect. Based on this, we propose LeafGAN, a novel image-to-image translation system. LeafGAN generates countless diverse and high-quality diseased data via transformation from healthy images, as a data augmentation tool for improving the performance of plant disease diagnosis. Our model can transform only relevant areas from images with a variety of backgrounds, thus enriching the versatility of the training images. Experiments show that data augmentation with LeafGAN help to improve the generalization, boosting the diagnostic performance on unseen data by 7.4% from baseline.In summary, we show that our approaches significantly improve the diagnostic performance under practical settings, confirming to be efficient and reliable methods for real cultivation scenarios.
• DOI: 10.15002/00024532; https://doi.org/10.15002/00024532
• 国立国会図書館永続的識別子: info:ndljp/pid/11976872; https://dl.ndl.go.jp/pid/11976872
• コレクション（共通）: 障害者向け資料
• コレクション（障害者向け資料：レベル1）: テキストデータ
• コレクション（個別）: 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• 収集根拠: 博士論文（自動収集）
• 受理日（W3CDTF）: 2022-01-10T16:22:37+09:00
• 作成日（W3CDTF）: 2021-11-22
• 記録形式（IMT）: PDF; application/pdf
• オンライン閲覧公開範囲: 国立国会図書館内限定公開
• デジタル化資料送信: 図書館・個人送信対象外
• 遠隔複写可否（NDL）: 可
• 掲載誌（URI）: https://doi.org/10.15002/00024532; http://hdl.handle.net/10114/00024532
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

目次

2024-02-02 再収集

2024-02-02 再収集