Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

cg.contactshwetapokhariyal9@gmail.comen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerIndian Institute of Remote Sensing - IIRSen_US
cg.contributor.centerGovind Ballabh Pant University of Agriculture and Technologyen_US
cg.contributor.crpResilient Agrifood Systems - RAFSen_US
cg.contributor.funderNot Applicableen_US
cg.contributor.initiativeExcellence in Agronomyen_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.countryINen_US
cg.coverage.regionSouthern Asiaen_US
cg.creator.idGovind, Ajit: 0000-0002-0656-0004en_US
cg.identifier.doihttps://dx.doi.org/10.3390/agronomy13092302en_US
cg.isijournalISI Journalen_US
cg.issn2073-4395en_US
cg.issue3en_US
cg.journalAgronomyen_US
cg.subject.actionAreaResilient Agrifood Systemsen_US
cg.subject.agrovocagricultureen_US
cg.subject.agrovocsoil managementen_US
cg.subject.agrovocwater managementen_US
cg.subject.agrovocremote sensingen_US
cg.subject.agrovoccrop managementen_US
cg.subject.agrovocindiaen_US
cg.subject.agrovocmachine learningen_US
cg.subject.impactAreaClimate adaptation and mitigationen_US
cg.subject.impactAreaNutrition, health and food securityen_US
cg.subject.impactAreaPoverty reduction, livelihoods and jobsen_US
cg.subject.impactAreaGender equality, youth and social inclusionen_US
cg.subject.sdgSDG 1 - No povertyen_US
cg.subject.sdgSDG 2 - Zero hungeren_US
cg.subject.sdgSDG 8 - Decent work and economic growthen_US
cg.subject.sdgSDG 13 - Climate actionen_US
cg.subject.sdgSDG 15 - Life on landen_US
cg.volume19en_US
dc.contributorPatel, N. R.en_US
dc.contributorGovind, Ajiten_US
dc.creatorPokhariyal, Shwetaen_US
dc.date.accessioned2024-08-15T18:21:43Z
dc.date.available2024-08-15T18:21:43Z
dc.description.abstractIn 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.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/ce501e1a417ad213db9196cd24538e79en_US
dc.identifier.citationShweta 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.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/69481
dc.languageenen_US
dc.publisherMDPIen_US
dc.rightsCC-BY-4.0en_US
dc.sourceAgronomy;19,(2023)en_US
dc.titleMachine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Reviewen_US
dc.typeJournal Articleen_US
dcterms.available2023-08-31en_US
mel.impact-factor3.3en_US

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