Multi Techniques for Agricultural Image Disease Classification and Detection: A Review
The agriculture sector has a significant impact on the market in every country. Identifying crop disease with conventional methods is a hard operation and it needs more time, effort, and experts with continuous farm monitoring. Blight and other crop diseases have severe consequences on crop yields a...
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| Format: | Article |
| Language: | English |
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Technoscience Publications
2022-12-01
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| Series: | Nature Environment and Pollution Technology |
| Subjects: | |
| Online Access: | https://neptjournal.com/upload-images/(11)B-3938.pdf |
| _version_ | 1797955851990859776 |
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| author | M. Amudha and K. Brindha |
| author_facet | M. Amudha and K. Brindha |
| author_sort | M. Amudha and K. Brindha |
| collection | DOAJ |
| description | The agriculture sector has a significant impact on the market in every country. Identifying crop disease with conventional methods is a hard operation and it needs more time, effort, and experts with continuous farm monitoring. Blight and other crop diseases have severe consequences on crop yields and cause enormous economic losses worldwide. Plant health monitoring and disease detection are critical components of sustainable agriculture. Machine learning and deep learning techniques are used to identify plant diseases and associated with severity detection in plant leaves. The adoption of these techniques still faces several important challenges. In recent years, improvements in technology and researchers’ interest in this area have made it possible to obtain an optimal solution. In addition to providing a detailed explanation of the proposed technique, which is deep learning architecture that uses the deep convolutional extreme learning machine (DC-ELM) for faster training, this study focuses on how machine learning and deep learning techniques detect plant diseases and infections that affect different crops. The proposed model is capable of providing good computational performance and allowing the learning process to be completed with less processing time. Finally, several challenges and problems with the existing system, as well as future research objectives, are enumerated and discussed. |
| first_indexed | 2024-04-10T23:40:41Z |
| format | Article |
| id | doaj.art-acb056727c0340808842b67c673140be |
| institution | Directory Open Access Journal |
| issn | 0972-6268 2395-3454 |
| language | English |
| last_indexed | 2024-04-10T23:40:41Z |
| publishDate | 2022-12-01 |
| publisher | Technoscience Publications |
| record_format | Article |
| series | Nature Environment and Pollution Technology |
| spelling | doaj.art-acb056727c0340808842b67c673140be2023-01-11T09:51:02ZengTechnoscience PublicationsNature Environment and Pollution Technology0972-62682395-34542022-12-012152165217510.46488/NEPT.2022.v21i05.011Multi Techniques for Agricultural Image Disease Classification and Detection: A ReviewM. Amudha and K. BrindhaThe agriculture sector has a significant impact on the market in every country. Identifying crop disease with conventional methods is a hard operation and it needs more time, effort, and experts with continuous farm monitoring. Blight and other crop diseases have severe consequences on crop yields and cause enormous economic losses worldwide. Plant health monitoring and disease detection are critical components of sustainable agriculture. Machine learning and deep learning techniques are used to identify plant diseases and associated with severity detection in plant leaves. The adoption of these techniques still faces several important challenges. In recent years, improvements in technology and researchers’ interest in this area have made it possible to obtain an optimal solution. In addition to providing a detailed explanation of the proposed technique, which is deep learning architecture that uses the deep convolutional extreme learning machine (DC-ELM) for faster training, this study focuses on how machine learning and deep learning techniques detect plant diseases and infections that affect different crops. The proposed model is capable of providing good computational performance and allowing the learning process to be completed with less processing time. Finally, several challenges and problems with the existing system, as well as future research objectives, are enumerated and discussed.https://neptjournal.com/upload-images/(11)B-3938.pdfmachine learning, deep learning, blight, plant disease, deep convolutional |
| spellingShingle | M. Amudha and K. Brindha Multi Techniques for Agricultural Image Disease Classification and Detection: A Review Nature Environment and Pollution Technology machine learning, deep learning, blight, plant disease, deep convolutional |
| title | Multi Techniques for Agricultural Image Disease Classification and Detection: A Review |
| title_full | Multi Techniques for Agricultural Image Disease Classification and Detection: A Review |
| title_fullStr | Multi Techniques for Agricultural Image Disease Classification and Detection: A Review |
| title_full_unstemmed | Multi Techniques for Agricultural Image Disease Classification and Detection: A Review |
| title_short | Multi Techniques for Agricultural Image Disease Classification and Detection: A Review |
| title_sort | multi techniques for agricultural image disease classification and detection a review |
| topic | machine learning, deep learning, blight, plant disease, deep convolutional |
| url | https://neptjournal.com/upload-images/(11)B-3938.pdf |
| work_keys_str_mv | AT mamudhaandkbrindha multitechniquesforagriculturalimagediseaseclassificationanddetectionareview |