Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review
cg.contact | shwetapokhariyal9@gmail.com | en_US |
cg.contributor.center | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.contributor.center | Indian Institute of Remote Sensing - IIRS | en_US |
cg.contributor.center | Govind Ballabh Pant University of Agriculture and Technology | en_US |
cg.contributor.crp | Resilient Agrifood Systems - RAFS | en_US |
cg.contributor.funder | Not Applicable | en_US |
cg.contributor.initiative | Excellence in Agronomy | en_US |
cg.contributor.project-lead-institute | International Center for Agricultural Research in the Dry Areas - ICARDA | en_US |
cg.coverage.country | IN | en_US |
cg.coverage.region | Southern Asia | en_US |
cg.creator.id | Govind, Ajit: 0000-0002-0656-0004 | en_US |
cg.identifier.doi | https://dx.doi.org/10.3390/agronomy13092302 | en_US |
cg.isijournal | ISI Journal | en_US |
cg.issn | 2073-4395 | en_US |
cg.issue | 3 | en_US |
cg.journal | Agronomy | en_US |
cg.subject.actionArea | Resilient Agrifood Systems | en_US |
cg.subject.agrovoc | agriculture | en_US |
cg.subject.agrovoc | soil management | en_US |
cg.subject.agrovoc | water management | en_US |
cg.subject.agrovoc | remote sensing | en_US |
cg.subject.agrovoc | crop management | en_US |
cg.subject.agrovoc | india | en_US |
cg.subject.agrovoc | machine learning | en_US |
cg.subject.impactArea | Climate adaptation and mitigation | en_US |
cg.subject.impactArea | Nutrition, health and food security | en_US |
cg.subject.impactArea | Poverty reduction, livelihoods and jobs | en_US |
cg.subject.impactArea | Gender equality, youth and social inclusion | en_US |
cg.subject.sdg | SDG 1 - No poverty | en_US |
cg.subject.sdg | SDG 2 - Zero hunger | en_US |
cg.subject.sdg | SDG 8 - Decent work and economic growth | en_US |
cg.subject.sdg | SDG 13 - Climate action | en_US |
cg.subject.sdg | SDG 15 - Life on land | en_US |
cg.volume | 19 | en_US |
dc.contributor | Patel, N. R. | en_US |
dc.contributor | Govind, Ajit | en_US |
dc.creator | Pokhariyal, Shweta | en_US |
dc.date.accessioned | 2024-08-15T18:21:43Z | |
dc.date.available | 2024-08-15T18:21:43Z | |
dc.description.abstract | 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. | en_US |
dc.format | en_US | |
dc.identifier | https://mel.cgiar.org/reporting/downloadmelspace/hash/ce501e1a417ad213db9196cd24538e79 | en_US |
dc.identifier.citation | 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). | en_US |
dc.identifier.status | Open access | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.11766/69481 | |
dc.language | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | CC-BY-4.0 | en_US |
dc.source | Agronomy;19,(2023) | en_US |
dc.title | Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review | en_US |
dc.type | Journal Article | en_US |
dcterms.available | 2023-08-31 | en_US |
mel.impact-factor | 3.3 | en_US |