Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection
Agricultural production is a cornerstone of national economies, and the prevalence of plant diseases poses a significant threat to crop yields. Timely disease detection is essential to mitigate these risks. However, manual plant observation methods are labor-intensive and time-consuming, necessitati...
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IEEE
2023-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10323084/ |
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| author | Aisha Ahmed AlArfaj Abdulaziz Altamimi Turki Aljrees Shakila Basheer Muhammad Umer Md. Abdus Samad Shtwai Alsubai Imran Ashraf |
| author_facet | Aisha Ahmed AlArfaj Abdulaziz Altamimi Turki Aljrees Shakila Basheer Muhammad Umer Md. Abdus Samad Shtwai Alsubai Imran Ashraf |
| author_sort | Aisha Ahmed AlArfaj |
| collection | DOAJ |
| description | Agricultural production is a cornerstone of national economies, and the prevalence of plant diseases poses a significant threat to crop yields. Timely disease detection is essential to mitigate these risks. However, manual plant observation methods are labor-intensive and time-consuming, necessitating a shift toward automated solutions. This study addresses the pressing problem of plant disease identification by leveraging advanced image processing techniques. This research begins with a comprehensive analysis of the pepper bell leaf disease dataset. Through a series of meticulously designed image processing steps, the dataset is normalized, enhancing its quality and consistency. Building upon this preprocessing, the UNET segmentation technique in conjunction with the InceptionV3 transfer learning model is employed. This novel approach yields exceptional results, with 99.48% accuracy, 99.97% precision, 99.99% recall, and 99.98% F1 scores. To objectively assess the significance of the proposed model, the performance is benchmarked against existing state-of-the-art models. The findings demonstrate the superiority of the proposed approach in the domain of plant disease identification. By automating the detection process, this research not only enhances efficiency but also enables early disease detection, thereby potentially contributing to the agricultural sector to identify crop disease and manage it efficiently. |
| format | Article |
| id | doaj-art-b6cb60e020b0446d875133a9998a84df |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b6cb60e020b0446d875133a9998a84df2025-08-20T03:42:19ZengIEEEIEEE Access2169-35362023-01-011113225413226710.1109/ACCESS.2023.333442810323084Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease DetectionAisha Ahmed AlArfaj0Abdulaziz Altamimi1Turki Aljrees2https://orcid.org/0000-0002-7473-7115Shakila Basheer3https://orcid.org/0000-0001-9032-9560Muhammad Umer4https://orcid.org/0000-0002-6015-9326Md. Abdus Samad5https://orcid.org/0000-0002-1990-6924Shtwai Alsubai6https://orcid.org/0000-0002-6584-7400Imran Ashraf7https://orcid.org/0000-0002-8271-6496Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi ArabiaDepartment of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaAgricultural production is a cornerstone of national economies, and the prevalence of plant diseases poses a significant threat to crop yields. Timely disease detection is essential to mitigate these risks. However, manual plant observation methods are labor-intensive and time-consuming, necessitating a shift toward automated solutions. This study addresses the pressing problem of plant disease identification by leveraging advanced image processing techniques. This research begins with a comprehensive analysis of the pepper bell leaf disease dataset. Through a series of meticulously designed image processing steps, the dataset is normalized, enhancing its quality and consistency. Building upon this preprocessing, the UNET segmentation technique in conjunction with the InceptionV3 transfer learning model is employed. This novel approach yields exceptional results, with 99.48% accuracy, 99.97% precision, 99.99% recall, and 99.98% F1 scores. To objectively assess the significance of the proposed model, the performance is benchmarked against existing state-of-the-art models. The findings demonstrate the superiority of the proposed approach in the domain of plant disease identification. By automating the detection process, this research not only enhances efficiency but also enables early disease detection, thereby potentially contributing to the agricultural sector to identify crop disease and manage it efficiently.https://ieeexplore.ieee.org/document/10323084/Pepper bell leaf diseaseimage processingtransfer learningInceptionV3 |
| spellingShingle | Aisha Ahmed AlArfaj Abdulaziz Altamimi Turki Aljrees Shakila Basheer Muhammad Umer Md. Abdus Samad Shtwai Alsubai Imran Ashraf Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection IEEE Access Pepper bell leaf disease image processing transfer learning InceptionV3 |
| title | Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection |
| title_full | Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection |
| title_fullStr | Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection |
| title_full_unstemmed | Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection |
| title_short | Multi-Step Preprocessing With UNet Segmentation and Transfer Learning Model for Pepper Bell Leaf Disease Detection |
| title_sort | multi step preprocessing with unet segmentation and transfer learning model for pepper bell leaf disease detection |
| topic | Pepper bell leaf disease image processing transfer learning InceptionV3 |
| url | https://ieeexplore.ieee.org/document/10323084/ |
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