Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products

Simple item page
dc.contributor.authorKshetrimayum, A.
dc.contributor.authorGoyal, A.
dc.contributor.authorRamesh, H.
dc.contributor.authorBhadra, B.K.
dc.date.accessioned2026-02-04T12:25:42Z
dc.date.issued2024
dc.description.abstractPearl millet (Pennisetum glaucum L.R.Br.), is the most widely cultivated food crop after rice, wheat, and maize. The aim of the project is to determine the crop acreage of Pearl millet (Bajra) using Sentinel-1A SAR data and Machine Learning Algorithm to determine the yield estimation of the Pearl millet crop at the tehsil level using the Monteith approach. The classification overall accuracy is found to be 86.48% for Agra district and 80.15% for Firozabad district. The Relative Deviation of yield estimation for the Agra and Firozabad districts is found to be 10.14 and 6, respectively. © 2023 Mapping Sciences Institute, Australia and Geospatial Council of Australia.
dc.identifier.citationJournal of Spatial Science, 2024, 69, 2, pp. 573-592
dc.identifier.issn14498596
dc.identifier.urihttps://doi.org/10.1080/14498596.2023.2259857
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/21488
dc.publisherMapping Sciences Institute Australia
dc.subjectcrop yield
dc.subjectestimation method
dc.subjectmachine learning
dc.subjectmillet
dc.subjectSentinel
dc.subjectsynthetic aperture radar
dc.subjectyield response
dc.subjectAgra
dc.subjectIndia
dc.subjectUttar Pradesh
dc.titleSemi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products

Files

Collections

Journal Articles