FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2731567662> ?p ?o ?g. }

• W2731567662 endingPage "2489" @default.
• W2731567662 startingPage "2478" @default.
• W2731567662 abstract "Phenological data are important ratings of the in-season growth of crops, though this assessment is generally limited at both spatial and temporal levels during the crop cycle for large breeding nurseries. Unmanned aerial systems (UAS) have the potential to provide high spatial and temporal resolution for phenotyping tens of thousands of small field plots without requiring substantial investments in time, cost, and labor. The objective of this research was to determine whether an accurate remote sensing-based method could be developed to estimate grain yield using aerial imagery in small-plot wheat (Triticum aestivum L.) yield evaluation trials. The UAS consisted of a modified consumer-grade camera mounted on a low-cost unmanned aerial vehicle and was deployed multiple times throughout the growing season in yield trials of advanced breeding lines with irrigated and drought-stressed environments at the International Maize and Wheat Improvement Center in Ciudad Obregon, Sonora, Mexico. We assessed data quality and evaluated the potential to predict grain yield on a plot level by examining the relationships between information derived from UAS imagery and the grain yield. Using geographically weighted (GW) models, we predicted grain yield for both environments. The relationship between measured phenotypic traits derived from imagery and grain yield was highly correlated (r = 0.74 and r = 0.46 [p < 0.001] for drought and irrigated environments, respectively). Residuals from GW models were lower and less spatially dependent than methods using principal component regression, suggesting the superiority of spatially corrected models. These results show that vegetation indices collected from high-throughput UAS imagery can be used to predict grain and for selection decisions, as well as to enhance genomic selection models." @default.
• W2731567662 created "2017-07-14" @default.
• W2731567662 creator A5009404553 @default.
• W2731567662 creator A5032530400 @default.
• W2731567662 creator A5054554913 @default.
• W2731567662 creator A5059664632 @default.
• W2731567662 creator A5061266478 @default.
• W2731567662 creator A5078976855 @default.
• W2731567662 date "2017-06-27" @default.
• W2731567662 modified "2023-10-03" @default.
• W2731567662 title "Application of Geographically Weighted Regression to Improve Grain Yield Prediction from Unmanned Aerial System Imagery" @default.
• W2731567662 cites W1973749534 @default.
• W2731567662 cites W1975117056 @default.
• W2731567662 cites W1980398988 @default.
• W2731567662 cites W1985345354 @default.
• W2731567662 cites W1995784367 @default.
• W2731567662 cites W2004675407 @default.
• W2731567662 cites W2015037454 @default.
• W2731567662 cites W2017859040 @default.
• W2731567662 cites W2021099147 @default.
• W2731567662 cites W2024027663 @default.
• W2731567662 cites W2024788798 @default.
• W2731567662 cites W2025238960 @default.
• W2731567662 cites W2027563724 @default.
• W2731567662 cites W2028040416 @default.
• W2731567662 cites W2034958267 @default.
• W2731567662 cites W2035695840 @default.
• W2731567662 cites W2038546252 @default.
• W2731567662 cites W2047120335 @default.
• W2731567662 cites W2059488281 @default.
• W2731567662 cites W2063405905 @default.
• W2731567662 cites W2070971469 @default.
• W2731567662 cites W2079958139 @default.
• W2731567662 cites W2083336903 @default.
• W2731567662 cites W2089229588 @default.
• W2731567662 cites W2106611604 @default.
• W2731567662 cites W2114266601 @default.
• W2731567662 cites W2114891756 @default.
• W2731567662 cites W2116542035 @default.
• W2731567662 cites W2118178527 @default.
• W2731567662 cites W2118476033 @default.
• W2731567662 cites W2121885753 @default.
• W2731567662 cites W2126902408 @default.
• W2731567662 cites W2130916293 @default.
• W2731567662 cites W2140959043 @default.
• W2731567662 cites W2145911219 @default.
• W2731567662 cites W2148333466 @default.
• W2731567662 cites W2159474015 @default.
• W2731567662 cites W2164672226 @default.
• W2731567662 cites W2165670902 @default.
• W2731567662 cites W2168036630 @default.
• W2731567662 cites W2326982326 @default.
• W2731567662 cites W2364902821 @default.
• W2731567662 cites W2467491686 @default.
• W2731567662 cites W2495369798 @default.
• W2731567662 cites W2564527027 @default.
• W2731567662 cites W4233070360 @default.
• W2731567662 cites W4248704160 @default.
• W2731567662 doi "https://doi.org/10.2135/cropsci2016.12.1016" @default.
• W2731567662 hasPublicationYear "2017" @default.
• W2731567662 type Work @default.
• W2731567662 sameAs 2731567662 @default.
• W2731567662 citedByCount "25" @default.
• W2731567662 countsByYear W27315676622018 @default.
• W2731567662 countsByYear W27315676622019 @default.
• W2731567662 countsByYear W27315676622020 @default.
• W2731567662 countsByYear W27315676622021 @default.
• W2731567662 countsByYear W27315676622022 @default.
• W2731567662 countsByYear W27315676622023 @default.
• W2731567662 crossrefType "journal-article" @default.
• W2731567662 hasAuthorship W2731567662A5009404553 @default.
• W2731567662 hasAuthorship W2731567662A5032530400 @default.
• W2731567662 hasAuthorship W2731567662A5054554913 @default.
• W2731567662 hasAuthorship W2731567662A5059664632 @default.
• W2731567662 hasAuthorship W2731567662A5061266478 @default.
• W2731567662 hasAuthorship W2731567662A5078976855 @default.
• W2731567662 hasBestOaLocation W27315676621 @default.
• W2731567662 hasConcept C100970517 @default.
• W2731567662 hasConcept C105795698 @default.
• W2731567662 hasConcept C126343540 @default.
• W2731567662 hasConcept C134121241 @default.
• W2731567662 hasConcept C137580998 @default.
• W2731567662 hasConcept C137660486 @default.
• W2731567662 hasConcept C142724271 @default.
• W2731567662 hasConcept C152877465 @default.
• W2731567662 hasConcept C191897082 @default.
• W2731567662 hasConcept C192562407 @default.
• W2731567662 hasConcept C205649164 @default.
• W2731567662 hasConcept C27438332 @default.
• W2731567662 hasConcept C2776133958 @default.
• W2731567662 hasConcept C2778102629 @default.
• W2731567662 hasConcept C2992211155 @default.
• W2731567662 hasConcept C33923547 @default.
• W2731567662 hasConcept C39432304 @default.
• W2731567662 hasConcept C48921125 @default.
• W2731567662 hasConcept C51417038 @default.
• W2731567662 hasConcept C62649853 @default.
• W2731567662 hasConcept C6557445 @default.

• next