FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3127924241> ?p ?o ?g. }

• W3127924241 endingPage "105992" @default.
• W3127924241 startingPage "105992" @default.
• W3127924241 abstract "Efficient selection of drought-tolerant crops requires identification and high-throughput phenotyping (HTP) of the complex functional (especially canopy-conductance) traits that elicit plant responses to continually fluctuating environmental conditions. However, phenotyping of such dynamic physiology-based traits has been immensely challenging especially due to the limited availability of adequate methods that can provide continuous measurements of plant-water relations. Therefore, gravimetric phenotyping of plants is being increasingly used to allow one-to-one monitoring of plant-water relations and generate continuous evapotranspiration (ET) profiles. The gravimetric sensors or load cells can provide ET estimates at very high frequencies, e.g. 15-min interval, as chosen by the user. There is however, no study on understanding the optimum frequency or the sampling time at which ET needs to be monitored, such that data-redundancy, noise and processing overhead could be reduced. Hence, this paper makes a novel attempt in identifying the optimum sampling time for phenotyping ET from load cells time series. The proposed procedure includes an ensemble Machine-Learning (ML) approach for optimizing the sampling time through time series forecasting of ET profiles and classification of genotypes using the forecasted ET values. High-frequency load cells data from the LeasyScan, HTP platform, ICRISAT were used to derive the ET profiles at frequencies or scales varying from 15-min to 180-min, followed by ET forecasting and classification at each frequency. For both forecasting and classification, an ensemble of three ML algorithms i.e. Support Vector Machines (SVM), Artificial Neural Network (ANN) and Random Forests (RF) were leveraged. Consequently, the performance metrics (of both the operations) obtained from the ensemble were used to compute the entropy-based optimum sampling time. The results reveal that 60-min interval HTP data could be credibly used for both, forecasting ET as well as correctly classifying the genotypes." @default.
• W3127924241 created "2021-02-15" @default.
• W3127924241 creator A5016463871 @default.
• W3127924241 creator A5020155847 @default.
• W3127924241 creator A5052217638 @default.
• W3127924241 creator A5055843876 @default.
• W3127924241 creator A5059431204 @default.
• W3127924241 creator A5077949635 @default.
• W3127924241 creator A5080991193 @default.
• W3127924241 creator A5081221981 @default.
• W3127924241 date "2021-03-01" @default.
• W3127924241 modified "2023-10-16" @default.
• W3127924241 title "An ensemble machine learning approach for determination of the optimum sampling time for evapotranspiration assessment from high-throughput phenotyping data" @default.
• W3127924241 cites W117385563 @default.
• W3127924241 cites W1191826283 @default.
• W3127924241 cites W1509177177 @default.
• W3127924241 cites W1577841048 @default.
• W3127924241 cites W1836741323 @default.
• W3127924241 cites W1974614303 @default.
• W3127924241 cites W1974825269 @default.
• W3127924241 cites W1978959816 @default.
• W3127924241 cites W1981179689 @default.
• W3127924241 cites W1984147295 @default.
• W3127924241 cites W1985700489 @default.
• W3127924241 cites W1988518729 @default.
• W3127924241 cites W1989533052 @default.
• W3127924241 cites W1990653740 @default.
• W3127924241 cites W2015654807 @default.
• W3127924241 cites W2021833436 @default.
• W3127924241 cites W2040395995 @default.
• W3127924241 cites W2067877300 @default.
• W3127924241 cites W2070986256 @default.
• W3127924241 cites W2102204044 @default.
• W3127924241 cites W2105993120 @default.
• W3127924241 cites W2108183654 @default.
• W3127924241 cites W2115499157 @default.
• W3127924241 cites W2116825089 @default.
• W3127924241 cites W2123096215 @default.
• W3127924241 cites W2132379769 @default.
• W3127924241 cites W2162497755 @default.
• W3127924241 cites W2164843739 @default.
• W3127924241 cites W2168156818 @default.
• W3127924241 cites W2170505850 @default.
• W3127924241 cites W2172064003 @default.
• W3127924241 cites W2185489349 @default.
• W3127924241 cites W2270937275 @default.
• W3127924241 cites W2310874860 @default.
• W3127924241 cites W2466829632 @default.
• W3127924241 cites W2518417964 @default.
• W3127924241 cites W2520584901 @default.
• W3127924241 cites W2556118848 @default.
• W3127924241 cites W2556315116 @default.
• W3127924241 cites W260519431 @default.
• W3127924241 cites W2610501306 @default.
• W3127924241 cites W2610951070 @default.
• W3127924241 cites W2743464688 @default.
• W3127924241 cites W2749106749 @default.
• W3127924241 cites W2789760001 @default.
• W3127924241 cites W2791252587 @default.
• W3127924241 cites W2903132559 @default.
• W3127924241 cites W2911964244 @default.
• W3127924241 cites W2921467030 @default.
• W3127924241 cites W2963100393 @default.
• W3127924241 cites W2979025903 @default.
• W3127924241 cites W2998168058 @default.
• W3127924241 cites W3008554629 @default.
• W3127924241 cites W3010807909 @default.
• W3127924241 cites W3014229143 @default.
• W3127924241 cites W3036299832 @default.
• W3127924241 cites W3093048683 @default.
• W3127924241 cites W571200655 @default.
• W3127924241 doi "https://doi.org/10.1016/j.compag.2021.105992" @default.
• W3127924241 hasPublicationYear "2021" @default.
• W3127924241 type Work @default.
• W3127924241 sameAs 3127924241 @default.
• W3127924241 citedByCount "15" @default.
• W3127924241 countsByYear W31279242412021 @default.
• W3127924241 countsByYear W31279242412022 @default.
• W3127924241 countsByYear W31279242412023 @default.
• W3127924241 crossrefType "journal-article" @default.
• W3127924241 hasAuthorship W3127924241A5016463871 @default.
• W3127924241 hasAuthorship W3127924241A5020155847 @default.
• W3127924241 hasAuthorship W3127924241A5052217638 @default.
• W3127924241 hasAuthorship W3127924241A5055843876 @default.
• W3127924241 hasAuthorship W3127924241A5059431204 @default.
• W3127924241 hasAuthorship W3127924241A5077949635 @default.
• W3127924241 hasAuthorship W3127924241A5080991193 @default.
• W3127924241 hasAuthorship W3127924241A5081221981 @default.
• W3127924241 hasBestOaLocation W31279242411 @default.
• W3127924241 hasConcept C105795698 @default.
• W3127924241 hasConcept C106131492 @default.
• W3127924241 hasConcept C116834253 @default.
• W3127924241 hasConcept C119857082 @default.
• W3127924241 hasConcept C12267149 @default.
• W3127924241 hasConcept C124101348 @default.
• W3127924241 hasConcept C140779682 @default.
• W3127924241 hasConcept C154945302 @default.
• W3127924241 hasConcept C169258074 @default.

• next