FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136331220> ?p ?o ?g. }

• W3136331220 endingPage "1463" @default.
• W3136331220 startingPage "1437" @default.
• W3136331220 abstract "Abstract This study investigated the potential of in-season airborne hyperspectral imaging for the calibration of robust forage yield and quality estimation models. An unmanned aerial vehicle (UAV) and a hyperspectral imager were used to capture canopy reflections of a grass-legume mixture in the range of 450 nm to 800 nm. Measurements were performed over two years at two locations in Southeast and Central Norway. All images were subject to radiometric and geometric corrections before being processed to ortho-images, carrying canopy reflectance information. The data (n = 707) was split in two, using half the data for model calibration and the remaining half for validation. Several powered partial least squares regression (PPLSR) models were fitted to the reflectance data to estimate fresh (FM) and dry matter (DM) yields, as well as crude protein (CP), dry matter digestibility (DMD), neutral detergent fibre (NDF), and indigestible neutral detergent fibre (iNDF) content. Prediction performance of these models was compared with the prediction performance of simple linear regression (SLR) models, which were based on selected vegetation indices and plant height. The highest prediction accuracies for general models, based on the pooled data, were achieved by means of PPLSR, with relative root-mean-square errors of validation of 14.2% (2550 kg FM ha −1 ), 15.2% (555 kg DM ha −1 ), 11.7% (1.32 g CP 100 g −1 DM), 2.4% (1.71 g DMD 100 g −1 DM), 4.8% (2.72 g NDF 100 g −1 DM), and 12.8% (1.32 g iNDF 100 g −1 DM) for the prediction of FM, DM, CP, DMD, NDF, and iNDF content, respectively. None of the tested SLR models achieved acceptable prediction accuracies." @default.
• W3136331220 created "2021-03-29" @default.
• W3136331220 creator A5033626804 @default.
• W3136331220 creator A5045408173 @default.
• W3136331220 creator A5060774466 @default.
• W3136331220 creator A5089630290 @default.
• W3136331220 date "2021-03-18" @default.
• W3136331220 modified "2023-10-14" @default.
• W3136331220 title "Forage yield and quality estimation by means of UAV and hyperspectral imaging" @default.
• W3136331220 cites W1872907955 @default.
• W3136331220 cites W1964272420 @default.
• W3136331220 cites W1965106709 @default.
• W3136331220 cites W1970217081 @default.
• W3136331220 cites W1972664953 @default.
• W3136331220 cites W1974820932 @default.
• W3136331220 cites W1976049210 @default.
• W3136331220 cites W2006644004 @default.
• W3136331220 cites W2023097472 @default.
• W3136331220 cites W2026055474 @default.
• W3136331220 cites W2046404820 @default.
• W3136331220 cites W2053501943 @default.
• W3136331220 cites W2064920866 @default.
• W3136331220 cites W2069267285 @default.
• W3136331220 cites W2081737457 @default.
• W3136331220 cites W2088304553 @default.
• W3136331220 cites W2105756833 @default.
• W3136331220 cites W2125117729 @default.
• W3136331220 cites W2129107479 @default.
• W3136331220 cites W2133751300 @default.
• W3136331220 cites W2135092399 @default.
• W3136331220 cites W2149081460 @default.
• W3136331220 cites W2159961845 @default.
• W3136331220 cites W2192020007 @default.
• W3136331220 cites W2300272464 @default.
• W3136331220 cites W2313541398 @default.
• W3136331220 cites W2579656072 @default.
• W3136331220 cites W2620896114 @default.
• W3136331220 cites W2751223463 @default.
• W3136331220 cites W2775162792 @default.
• W3136331220 cites W2802904960 @default.
• W3136331220 cites W2803775013 @default.
• W3136331220 cites W2843415492 @default.
• W3136331220 cites W2883715112 @default.
• W3136331220 cites W2938406589 @default.
• W3136331220 cites W2948135055 @default.
• W3136331220 cites W2983376237 @default.
• W3136331220 cites W2998236111 @default.
• W3136331220 doi "https://doi.org/10.1007/s11119-021-09790-2" @default.
• W3136331220 hasPublicationYear "2021" @default.
• W3136331220 type Work @default.
• W3136331220 sameAs 3136331220 @default.
• W3136331220 citedByCount "16" @default.
• W3136331220 countsByYear W31363312202021 @default.
• W3136331220 countsByYear W31363312202022 @default.
• W3136331220 countsByYear W31363312202023 @default.
• W3136331220 crossrefType "journal-article" @default.
• W3136331220 hasAuthorship W3136331220A5033626804 @default.
• W3136331220 hasAuthorship W3136331220A5045408173 @default.
• W3136331220 hasAuthorship W3136331220A5060774466 @default.
• W3136331220 hasAuthorship W3136331220A5089630290 @default.
• W3136331220 hasBestOaLocation W31363312201 @default.
• W3136331220 hasConcept C101000010 @default.
• W3136331220 hasConcept C105795698 @default.
• W3136331220 hasConcept C108597893 @default.
• W3136331220 hasConcept C120665830 @default.
• W3136331220 hasConcept C121332964 @default.
• W3136331220 hasConcept C142724271 @default.
• W3136331220 hasConcept C159078339 @default.
• W3136331220 hasConcept C165838908 @default.
• W3136331220 hasConcept C205649164 @default.
• W3136331220 hasConcept C22354355 @default.
• W3136331220 hasConcept C2776133958 @default.
• W3136331220 hasConcept C2779370140 @default.
• W3136331220 hasConcept C2780138947 @default.
• W3136331220 hasConcept C33923547 @default.
• W3136331220 hasConcept C39432304 @default.
• W3136331220 hasConcept C48921125 @default.
• W3136331220 hasConcept C59822182 @default.
• W3136331220 hasConcept C62649853 @default.
• W3136331220 hasConcept C6557445 @default.
• W3136331220 hasConcept C71924100 @default.
• W3136331220 hasConcept C86803240 @default.
• W3136331220 hasConceptScore W3136331220C101000010 @default.
• W3136331220 hasConceptScore W3136331220C105795698 @default.
• W3136331220 hasConceptScore W3136331220C108597893 @default.
• W3136331220 hasConceptScore W3136331220C120665830 @default.
• W3136331220 hasConceptScore W3136331220C121332964 @default.
• W3136331220 hasConceptScore W3136331220C142724271 @default.
• W3136331220 hasConceptScore W3136331220C159078339 @default.
• W3136331220 hasConceptScore W3136331220C165838908 @default.
• W3136331220 hasConceptScore W3136331220C205649164 @default.
• W3136331220 hasConceptScore W3136331220C22354355 @default.
• W3136331220 hasConceptScore W3136331220C2776133958 @default.
• W3136331220 hasConceptScore W3136331220C2779370140 @default.
• W3136331220 hasConceptScore W3136331220C2780138947 @default.
• W3136331220 hasConceptScore W3136331220C33923547 @default.
• W3136331220 hasConceptScore W3136331220C39432304 @default.
• W3136331220 hasConceptScore W3136331220C48921125 @default.

• next