Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning.

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Bibliographic Details
Title: Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning.
Authors: Billah MM; Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States., Maimaitijiang M; Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States., Kaushal S; Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States., Millett B; Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States., Khan SN; Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD, United States., Halder J; Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States., Kleinjan J; Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States., Sehgal SK; Department of Agronomy, Horticulture, and Plant Science, South Dakota State University, Brookings, SD, United States.
Source: Frontiers in plant science [Front Plant Sci] 2026 May 28; Vol. 17, pp. 1812052. Date of Electronic Publication: 2026 May 28 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101568200 Publication Model: eCollection Cited Medium: Print ISSN: 1664-462X (Print) Linking ISSN: 1664462X NLM ISO Abbreviation: Front Plant Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1664-462X
DOI:10.3389/fpls.2026.1812052