Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review
<p dir="ltr">Agriculture can ensure food security and enhance monetary benefits if practiced with modern technologies and supported with artificial intelligence (AI). Modern advancements in farming practices have revolutionized the production of food vegetation. However, crop cultiva...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
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| _version_ | 1864513543164919808 |
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| author | Avneet Kaur (712349) |
| author2 | Gurjit S. Randhawa (8763288) Farhat Abbas (5480) Mumtaz Ali (670928) Travis J. Esau (17541300) Aitazaz A. Farooque (17541303) Rajandeep Singh (13223363) |
| author2_role | author author author author author author |
| author_facet | Avneet Kaur (712349) Gurjit S. Randhawa (8763288) Farhat Abbas (5480) Mumtaz Ali (670928) Travis J. Esau (17541300) Aitazaz A. Farooque (17541303) Rajandeep Singh (13223363) |
| author_role | author |
| dc.creator.none.fl_str_mv | Avneet Kaur (712349) Gurjit S. Randhawa (8763288) Farhat Abbas (5480) Mumtaz Ali (670928) Travis J. Esau (17541300) Aitazaz A. Farooque (17541303) Rajandeep Singh (13223363) |
| dc.date.none.fl_str_mv | 2024-12-30T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3510456 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Artificial_Intelligence_Driven_Smart_Farming_for_Accurate_Detection_of_Potato_Diseases_A_Systematic_Review/29605217 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Crop and pasture production Food sciences Environmental sciences Ecological applications Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Artificial intelligence Deep learning Food security Machine learning Potato disease forecasting |
| dc.title.none.fl_str_mv | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Agriculture can ensure food security and enhance monetary benefits if practiced with modern technologies and supported with artificial intelligence (AI). Modern advancements in farming practices have revolutionized the production of food vegetation. However, crop cultivation faces several threats including insect and pest attacks and disease infections on plant leaves. For example, one of the most consumed foods vegetables universally—potatoes, are vulnerable to diseases like Late Blight (LB), Early Blight (EB), and others. These infections must be controlled to enhance food quality and yield. Conventional disease detection techniques are slow and depend on human involvement, which may be laborious and erroneous. However, AI tools, for instance, Machine Learning (ML) and Deep Learning (DL), offer precise and well-timed solutions for disease detection, classification, and eradication. A comprehensive review of literature has been conducted by examining over 400 articles to focus on 72 studies including 14 reviews publications on ML and DL models about potato disease forecasting using different techniques. It highlights the need for proficient disease control by integrating image and climate data. It further aids in addressing challenges like data availability and geographical variations. It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. The importance of accurate disease detection and eradication has been reported for food security, financial stability, and sustainable farming practices. Progressions in disease forecasts aid farmers in making informed decisions, minimizing crop losses, and reducing pesticide use through targeted application of agrochemicals with the use of AI-driven variable rate sprayers. This leads to healthier crops, market stability, and a more sustainable farming environment.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3510456" target="_blank">https://dx.doi.org/10.1109/access.2024.3510456</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_434884969ceac7e6138d58c69a3a17cb |
| identifier_str_mv | 10.1109/access.2024.3510456 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605217 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic ReviewAvneet Kaur (712349)Gurjit S. Randhawa (8763288)Farhat Abbas (5480)Mumtaz Ali (670928)Travis J. Esau (17541300)Aitazaz A. Farooque (17541303)Rajandeep Singh (13223363)Agricultural, veterinary and food sciencesCrop and pasture productionFood sciencesEnvironmental sciencesEcological applicationsInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationData management and data scienceMachine learningArtificial intelligenceDeep learningFood securityMachine learningPotato disease forecasting<p dir="ltr">Agriculture can ensure food security and enhance monetary benefits if practiced with modern technologies and supported with artificial intelligence (AI). Modern advancements in farming practices have revolutionized the production of food vegetation. However, crop cultivation faces several threats including insect and pest attacks and disease infections on plant leaves. For example, one of the most consumed foods vegetables universally—potatoes, are vulnerable to diseases like Late Blight (LB), Early Blight (EB), and others. These infections must be controlled to enhance food quality and yield. Conventional disease detection techniques are slow and depend on human involvement, which may be laborious and erroneous. However, AI tools, for instance, Machine Learning (ML) and Deep Learning (DL), offer precise and well-timed solutions for disease detection, classification, and eradication. A comprehensive review of literature has been conducted by examining over 400 articles to focus on 72 studies including 14 reviews publications on ML and DL models about potato disease forecasting using different techniques. It highlights the need for proficient disease control by integrating image and climate data. It further aids in addressing challenges like data availability and geographical variations. It has been learned that image-processing techniques overwhelm the existing research and have the potential to integrate meteorological data. The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. The importance of accurate disease detection and eradication has been reported for food security, financial stability, and sustainable farming practices. Progressions in disease forecasts aid farmers in making informed decisions, minimizing crop losses, and reducing pesticide use through targeted application of agrochemicals with the use of AI-driven variable rate sprayers. This leads to healthier crops, market stability, and a more sustainable farming environment.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3510456" target="_blank">https://dx.doi.org/10.1109/access.2024.3510456</a></p>2024-12-30T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3510456https://figshare.com/articles/journal_contribution/Artificial_Intelligence_Driven_Smart_Farming_for_Accurate_Detection_of_Potato_Diseases_A_Systematic_Review/29605217CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296052172024-12-30T06:00:00Z |
| spellingShingle | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review Avneet Kaur (712349) Agricultural, veterinary and food sciences Crop and pasture production Food sciences Environmental sciences Ecological applications Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Artificial intelligence Deep learning Food security Machine learning Potato disease forecasting |
| status_str | publishedVersion |
| title | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| title_full | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| title_fullStr | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| title_full_unstemmed | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| title_short | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| title_sort | Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review |
| topic | Agricultural, veterinary and food sciences Crop and pasture production Food sciences Environmental sciences Ecological applications Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Machine learning Artificial intelligence Deep learning Food security Machine learning Potato disease forecasting |