Applications of CNN in leaf diseases: A critical survey
Crop diseases can significantly impact crop yield and overall productivity, posing challenges for farmers in increasing output and market prices. Early detection of these diseases is crucial for preventing further spread and reducing their impact. To overcome this, researchers have utilized image pr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/04/itmconf_I3cs2023_01014.pdf |
| _version_ | 1797775784601976832 |
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| author | Satyanarayana L.V. Chandrasekhar Rao D. |
| author_facet | Satyanarayana L.V. Chandrasekhar Rao D. |
| author_sort | Satyanarayana L.V. |
| collection | DOAJ |
| description | Crop diseases can significantly impact crop yield and overall productivity, posing challenges for farmers in increasing output and market prices. Early detection of these diseases is crucial for preventing further spread and reducing their impact. To overcome this, researchers have utilized image processing technology, including deep learning techniques such as convolutional neural networks (CNNs), to detect crop diseases. In this critical survey, we provide a comprehensive review of recent studies and developments in the use of CNNs for identifying leaf diseases in agricultural plants. We discuss the benefits and drawbacks of different deep learning techniques and image processing methods for disease diagnosis and management in agriculture. Our research highlights the potential of CNNs and deep learning to significantly advance the field of agricultural research and development. We also analyze the factors affecting the outcomes of each technique, including the accuracy, precision. Our study emphasizes the need for further research and development to optimize the use of CNNs in agricultural applications, particularly for improving disease management and crop productivity. |
| first_indexed | 2024-03-12T22:40:38Z |
| format | Article |
| id | doaj.art-bea6337aaedb45129fcd05c3d63604e2 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-12T22:40:38Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-bea6337aaedb45129fcd05c3d63604e22023-07-21T09:41:32ZengEDP SciencesITM Web of Conferences2271-20972023-01-01540101410.1051/itmconf/20235401014itmconf_I3cs2023_01014Applications of CNN in leaf diseases: A critical surveySatyanarayana L.V.0Chandrasekhar Rao D.1Department of IT, Veer Surendra Sai University of TechnologyDepartment of IT, Veer Surendra Sai University of TechnologyCrop diseases can significantly impact crop yield and overall productivity, posing challenges for farmers in increasing output and market prices. Early detection of these diseases is crucial for preventing further spread and reducing their impact. To overcome this, researchers have utilized image processing technology, including deep learning techniques such as convolutional neural networks (CNNs), to detect crop diseases. In this critical survey, we provide a comprehensive review of recent studies and developments in the use of CNNs for identifying leaf diseases in agricultural plants. We discuss the benefits and drawbacks of different deep learning techniques and image processing methods for disease diagnosis and management in agriculture. Our research highlights the potential of CNNs and deep learning to significantly advance the field of agricultural research and development. We also analyze the factors affecting the outcomes of each technique, including the accuracy, precision. Our study emphasizes the need for further research and development to optimize the use of CNNs in agricultural applications, particularly for improving disease management and crop productivity.https://www.itm-conferences.org/articles/itmconf/pdf/2023/04/itmconf_I3cs2023_01014.pdf |
| spellingShingle | Satyanarayana L.V. Chandrasekhar Rao D. Applications of CNN in leaf diseases: A critical survey ITM Web of Conferences |
| title | Applications of CNN in leaf diseases: A critical survey |
| title_full | Applications of CNN in leaf diseases: A critical survey |
| title_fullStr | Applications of CNN in leaf diseases: A critical survey |
| title_full_unstemmed | Applications of CNN in leaf diseases: A critical survey |
| title_short | Applications of CNN in leaf diseases: A critical survey |
| title_sort | applications of cnn in leaf diseases a critical survey |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/04/itmconf_I3cs2023_01014.pdf |
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