Crop-saving with AI: latest trends in deep learning techniques for plant pathology
Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous v...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1224709/full |
| _version_ | 1827888436253556736 |
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| author | Zafar Salman Abdullah Muhammad Md Jalil Piran Dongil Han |
| author_facet | Zafar Salman Abdullah Muhammad Md Jalil Piran Dongil Han |
| author_sort | Zafar Salman |
| collection | DOAJ |
| description | Plant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management. |
| first_indexed | 2024-03-12T20:33:42Z |
| format | Article |
| id | doaj.art-df95d5895657412ea13c986c3b58f226 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-12T20:33:42Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-df95d5895657412ea13c986c3b58f2262023-08-01T17:45:42ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-08-011410.3389/fpls.2023.12247091224709Crop-saving with AI: latest trends in deep learning techniques for plant pathologyZafar SalmanAbdullah MuhammadMd Jalil PiranDongil HanPlant diseases pose a major threat to agricultural production and the food supply chain, as they expose plants to potentially disruptive pathogens that can affect the lives of those who are associated with it. Deep learning has been applied in a range of fields such as object detection, autonomous vehicles, fraud detection etc. Several researchers have tried to implement deep learning techniques in precision agriculture. However, there are pros and cons to the approaches they have opted for disease detection and identification. In this survey, we have made an attempt to capture the significant advancements in machine-learning based disease detection. We have discussed prevalent datasets and techniques that have been employed as well as highlighted emerging approaches being used for plant disease detection. By exploring these advancements, we aim to present a comprehensive overview of the prominent approaches in precision agriculture, along with their associated challenges and potential improvements. This paper delves into the challenges associated with the implementation and briefly discusses the future trends. Overall, this paper presents a bird’s eye view of plant disease datasets, deep learning techniques, their accuracies and the challenges associated with them. Our insights will serve as a valuable resource for researchers and practitioners in the field. We hope that this survey will inform and inspire future research efforts, ultimately leading to improved precision agriculture practices and enhanced crop health management.https://www.frontiersin.org/articles/10.3389/fpls.2023.1224709/fulldeep learningdisease detectioncomputer visionmachine learningplant diseasevision transformers |
| spellingShingle | Zafar Salman Abdullah Muhammad Md Jalil Piran Dongil Han Crop-saving with AI: latest trends in deep learning techniques for plant pathology Frontiers in Plant Science deep learning disease detection computer vision machine learning plant disease vision transformers |
| title | Crop-saving with AI: latest trends in deep learning techniques for plant pathology |
| title_full | Crop-saving with AI: latest trends in deep learning techniques for plant pathology |
| title_fullStr | Crop-saving with AI: latest trends in deep learning techniques for plant pathology |
| title_full_unstemmed | Crop-saving with AI: latest trends in deep learning techniques for plant pathology |
| title_short | Crop-saving with AI: latest trends in deep learning techniques for plant pathology |
| title_sort | crop saving with ai latest trends in deep learning techniques for plant pathology |
| topic | deep learning disease detection computer vision machine learning plant disease vision transformers |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1224709/full |
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