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Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression
| Title: | Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression | Authors: | Adak, Alper; Murray, Seth C; Bozinovic, Sofija S   ; Lindsey, Regan; Nakasagga, Shakirah; Chatterjee, Sumantra; Anderson, Steven LII; Wilde, Scott |
Issue Date: | 2021 | Publication: | REMOTE SENSING | ISSN: | 2072-4292 Remote Sensing Search Idenfier |
Type: | Article | Collation: | vol. 13 br. 11 str. 2141-2141 | DOI: | 10.3390/rs13112141 | WoS-ID: | 000660610600001 | Scopus-ID: | 2-s2.0-85107890481 | URI: | https://enauka.gov.rs/handle/123456789/825169 | Project: | USDA-NIFA-AFRIUnited States Department of Agriculture (USDA) [2017-67013-26185, 2020-68013-32371, 202167013-33915] USDA-NIFA Hatch funds Texas A&M AgriLife Research Texas Corn Producers Board Eugene Butler Endowed Chair in Biotechnology |
Metadata source: | (Preuzeto iz Nasi u WoS) | M-category: | 21aM21a |
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