YieldPredict: A Crop Yield Prediction Framework for Smart Farms

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dc.contributor.authorChoudhary, Nitu Kedarmal
dc.contributor.authorChukkapalli, Sai Sree Laya
dc.contributor.authorMittal, Sudip
dc.contributor.authorGupta, Maanak
dc.contributor.authorAbdelsalam, Mahmoud
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2020-11-19T19:27:48Z
dc.date.available2020-11-19T19:27:48Z
dc.date.issued2020-11-01
dc.descriptionIEEE International Conference on Big Data 2020
dc.description.abstractIn recent years, machine learning approaches are gaining popularity with the advent of big data. The massive amount of data generated, when served as an input to machine learning approaches, provides useful insights. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality. In this paper, we analyze the crop data collected from an agriculture site in Rajasthan, India, that includes both Rabi and Kharif cropping patterns. In addition, we utilize a smart farm ontology that contains concepts and properties related to the agricultural domain. We link the collected data and our smart farm ontology to populate a knowledge graph. We utilize the generated knowledge graph to provide structural information and aggregate data by using SPARQL queries. The aggregated data is further used by our machine learning models to predict the crop yield to benefit farmers and various stakeholders. We also analyze and compare our results obtained for various machine learning models used.en_US
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/955/YieldPredict-A-Crop-Yield-Prediction-Framework-for-Smart-Farmsen_US
dc.format.extent10 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepreprints
dc.identifierdoi:10.13016/m2wxdq-x1h8
dc.identifier.citationNitu Kedarmal Choudhary, Sai Sree Laya Chukkapalli, Sudip Mittal, Maanak Gupta, Mahmoud Abdelsalam, and Anupam Joshi, YieldPredict: A Crop Yield Prediction Framework for Smart Farms, IEEE International Conference on Big Data 2020en_US
dc.identifier.urihttp://hdl.handle.net/11603/20108
dc.language.isoen_USen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© 2020 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titleYieldPredict: A Crop Yield Prediction Framework for Smart Farmsen_US
dc.typeTexten_US

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