Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383721808> ?p ?o ?g. }
- W4383721808 endingPage "454" @default.
- W4383721808 startingPage "454" @default.
- W4383721808 abstract "The productivity of wheat in the Mediterranean region is under threat due to climate-change-related environmental factors, including fungal diseases that can negatively impact wheat yield and quality. Wheat phenotyping tools utilizing affordable, high-throughput plant phenotyping (HTPP) techniques, such as aerial and ground RGB images and quick canopy and leaf sensors, can aid in assessing crop status and selecting tolerant wheat varieties. This study focused on the impact of fungal diseases on wheat productivity in the Mediterranean region, considering the need for a precise selection of tolerant wheat varieties. This research examined the use of affordable HTPP methods, including imaging and active multispectral sensors, to aid in crop management for improved wheat health and to support commercial field phenotyping programs. This study evaluated 40 advanced lines of bread wheat (Triticum aestivum L.) at five locations across northern Spain, comparing fungicide-treated and untreated blocks under fungal disease pressure (Septoria, brown rust, and stripe rust observed). Measurements of leaf-level pigments and canopy vegetation indexes were taken using portable sensors, field cameras, and imaging sensors mounted on unmanned aerial vehicles (UAVs). Significant differences were observed in Dualex flavonoids and the nitrogen balance index (NBI) between treatments in some locations (p < 0.001 between Elorz and Ejea). Measurements of canopy vigor and color at the plot level showed significant differences between treatments at all sites, highlighting indexes such as the green area (GA), crop senescence index (CSI), and triangular greenness index (TGI) in assessing the effects of fungicide treatments on different wheat cultivars. RGB vegetation indexes from the ground and UAV were highly correlated (r = 0.817 and r = 0.810 for TGI and NGRDI). However, the Greenseeker NDVI sensor was found to be more effective in estimating grain yield and protein content (R2 = 0.61–0.7 and R2 = 0.45–0.55, respectively) compared to the aerial AgroCam GEO NDVI (R2 = 0.25–0.35 and R2 = 0.12–0.21, respectively). We suggest as a practical consideration the use of the GreenSeeker NDVI as more user-friendly and less affected by external environmental factors. This study emphasized the throughput benefits of RGB UAV HTPPs with the high similarity between ground and aerial results and highlighted the potential for HTPPs in supporting the selection of fungal-disease-resistant bread wheat varieties." @default.
- W4383721808 created "2023-07-11" @default.
- W4383721808 creator A5004584546 @default.
- W4383721808 creator A5011080719 @default.
- W4383721808 creator A5013826215 @default.
- W4383721808 creator A5016048018 @default.
- W4383721808 creator A5038019762 @default.
- W4383721808 creator A5040413856 @default.
- W4383721808 creator A5040750201 @default.
- W4383721808 creator A5044527033 @default.
- W4383721808 creator A5050116839 @default.
- W4383721808 creator A5072329153 @default.
- W4383721808 creator A5072638605 @default.
- W4383721808 creator A5092434762 @default.
- W4383721808 date "2023-07-08" @default.
- W4383721808 modified "2023-10-16" @default.
- W4383721808 title "Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties" @default.
- W4383721808 cites W1516502116 @default.
- W4383721808 cites W1576598527 @default.
- W4383721808 cites W1779377117 @default.
- W4383721808 cites W1978329109 @default.
- W4383721808 cites W1986986307 @default.
- W4383721808 cites W1988775351 @default.
- W4383721808 cites W2000171158 @default.
- W4383721808 cites W2001948755 @default.
- W4383721808 cites W2003704456 @default.
- W4383721808 cites W2003724897 @default.
- W4383721808 cites W2007023231 @default.
- W4383721808 cites W2011163307 @default.
- W4383721808 cites W2022718332 @default.
- W4383721808 cites W2028606616 @default.
- W4383721808 cites W2041777957 @default.
- W4383721808 cites W2044734963 @default.
- W4383721808 cites W2047548450 @default.
- W4383721808 cites W2051419256 @default.
- W4383721808 cites W2051774321 @default.
- W4383721808 cites W2052521036 @default.
- W4383721808 cites W2074464158 @default.
- W4383721808 cites W2097333789 @default.
- W4383721808 cites W2123101917 @default.
- W4383721808 cites W2129719999 @default.
- W4383721808 cites W2135435166 @default.
- W4383721808 cites W2141632163 @default.
- W4383721808 cites W2149136881 @default.
- W4383721808 cites W2170781359 @default.
- W4383721808 cites W2224635638 @default.
- W4383721808 cites W2324540353 @default.
- W4383721808 cites W2507933978 @default.
- W4383721808 cites W2745024650 @default.
- W4383721808 cites W2789833233 @default.
- W4383721808 cites W2883640548 @default.
- W4383721808 cites W2905894635 @default.
- W4383721808 cites W2947572107 @default.
- W4383721808 cites W2955365692 @default.
- W4383721808 cites W2974026369 @default.
- W4383721808 cites W2987372848 @default.
- W4383721808 cites W3014162671 @default.
- W4383721808 cites W3016537670 @default.
- W4383721808 cites W3037056604 @default.
- W4383721808 cites W3046551762 @default.
- W4383721808 cites W3102884643 @default.
- W4383721808 cites W3103608186 @default.
- W4383721808 cites W3146889079 @default.
- W4383721808 cites W3207445082 @default.
- W4383721808 cites W4206133898 @default.
- W4383721808 cites W4281401122 @default.
- W4383721808 cites W4281689682 @default.
- W4383721808 cites W4293657304 @default.
- W4383721808 cites W585276788 @default.
- W4383721808 doi "https://doi.org/10.3390/drones7070454" @default.
- W4383721808 hasPublicationYear "2023" @default.
- W4383721808 type Work @default.
- W4383721808 citedByCount "0" @default.
- W4383721808 crossrefType "journal-article" @default.
- W4383721808 hasAuthorship W4383721808A5004584546 @default.
- W4383721808 hasAuthorship W4383721808A5011080719 @default.
- W4383721808 hasAuthorship W4383721808A5013826215 @default.
- W4383721808 hasAuthorship W4383721808A5016048018 @default.
- W4383721808 hasAuthorship W4383721808A5038019762 @default.
- W4383721808 hasAuthorship W4383721808A5040413856 @default.
- W4383721808 hasAuthorship W4383721808A5040750201 @default.
- W4383721808 hasAuthorship W4383721808A5044527033 @default.
- W4383721808 hasAuthorship W4383721808A5050116839 @default.
- W4383721808 hasAuthorship W4383721808A5072329153 @default.
- W4383721808 hasAuthorship W4383721808A5072638605 @default.
- W4383721808 hasAuthorship W4383721808A5092434762 @default.
- W4383721808 hasBestOaLocation W43837218081 @default.
- W4383721808 hasConcept C101000010 @default.
- W4383721808 hasConcept C104317684 @default.
- W4383721808 hasConcept C137580998 @default.
- W4383721808 hasConcept C142724271 @default.
- W4383721808 hasConcept C197321923 @default.
- W4383721808 hasConcept C25989453 @default.
- W4383721808 hasConcept C2776008901 @default.
- W4383721808 hasConcept C2776133958 @default.
- W4383721808 hasConcept C2780306465 @default.
- W4383721808 hasConcept C2994440102 @default.
- W4383721808 hasConcept C39432304 @default.