Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop.
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| Title: | Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop. |
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| Authors: | Kambli, Mansi1, mansi.mk@somaiya.edu, Palkar, Bhakti1, bhaktiraul@somaiya.edu |
| Source: | Multimedia Tools & Applications; Nov2025, Vol. 84 Issue 36, p45297-45319, 23p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 189912273 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Kambli%2C+Mansi%22">Kambli, Mansi</searchLink><relatesTo>1</relatesTo>, <i>mansi.mk@somaiya.edu</i><br /><searchLink fieldCode="AU" term="%22Palkar%2C+Bhakti%22">Palkar, Bhakti</searchLink><relatesTo>1</relatesTo>, <i>bhaktiraul@somaiya.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>; Nov2025, Vol. 84 Issue 36, p45297-45319, 23p |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=189912273 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-025-20950-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 45297 Titles: – TitleFull: Remote sensing and machine learning methods to analyse the vegetation of sugarcane crop. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kambli, Mansi – PersonEntity: Name: NameFull: Palkar, Bhakti IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 36 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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