Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review
In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growt...
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| Format: | Journal Article |
| Language: | Inglés |
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MDPI
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/152182 |
| _version_ | 1855531090784026624 |
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| author | Pokhariyal, Shweta Patel, N. R. Govind, Ajit |
| author_browse | Govind, Ajit Patel, N. R. Pokhariyal, Shweta |
| author_facet | Pokhariyal, Shweta Patel, N. R. Govind, Ajit |
| author_sort | Pokhariyal, Shweta |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. |
| format | Journal Article |
| id | CGSpace152182 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1521822026-01-15T02:12:23Z Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review Pokhariyal, Shweta Patel, N. R. Govind, Ajit agriculture soil management water management remote sensing crop management india machine learning In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management. 2024-09-11T17:55:27Z 2024-09-11T17:55:27Z Journal Article https://hdl.handle.net/10568/152182 en Open Access application/pdf MDPI Shweta Pokhariyal, N. R. Patel, Ajit Govind. (31/8/2023). Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review. Agronomy, 19 (3). |
| spellingShingle | agriculture soil management water management remote sensing crop management india machine learning Pokhariyal, Shweta Patel, N. R. Govind, Ajit Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review |
| title | Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review |
| title_full | Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review |
| title_fullStr | Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review |
| title_full_unstemmed | Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review |
| title_short | Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review |
| title_sort | machine learning driven remote sensing applications for agriculture in india a systematic review |
| topic | agriculture soil management water management remote sensing crop management india machine learning |
| url | https://hdl.handle.net/10568/152182 |
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