FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311461947> ?p ?o ?g. }

• W4311461947 endingPage "101933" @default.
• W4311461947 startingPage "101933" @default.
• W4311461947 abstract "Accurate estimation of disease severity in the field is a key to minimize the yield losses in agriculture. Existing disease severity assessment methods have poor accuracy under field conditions. To overcome this limitation, this study used thermal and visible imaging with machine learning (ML) and model combination (MC) techniques to estimate plant disease severity under field conditions. Field experiments were conducted during 2017–18, 2018–19 and 2021–22 to obtain RGB and thermal images of chickpea cultivars with different levels of wilt resistance grown in wilt sick plots. ML models were constructed using four different datasets created using the wilt severity and image derived indices. ML models were also combined using MC techniques to assess the best predictor of the disease severity. Results indicated that the Cubist was the best ML model, while the KNN model was the poorest predictor of chickpea wilt severity under field conditions. MC techniques improved the prediction accuracy of wilt severity over individual ML models. Combining ML models using the least absolute deviation technique gave the best predictions of wilt severity. The results obtained in the present study showed the MC techniques coupled with ML models improved the prediction accuracies of plant disease severity under field conditions." @default.
• W4311461947 created "2022-12-26" @default.
• W4311461947 creator A5000017665 @default.
• W4311461947 creator A5006330339 @default.
• W4311461947 creator A5041081967 @default.
• W4311461947 creator A5047836403 @default.
• W4311461947 date "2023-03-01" @default.
• W4311461947 modified "2023-10-02" @default.
• W4311461947 title "Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions" @default.
• W4311461947 cites W1555904231 @default.
• W4311461947 cites W1964503221 @default.
• W4311461947 cites W1967320885 @default.
• W4311461947 cites W1973700570 @default.
• W4311461947 cites W1977780283 @default.
• W4311461947 cites W1986528915 @default.
• W4311461947 cites W1989712641 @default.
• W4311461947 cites W1989863789 @default.
• W4311461947 cites W1999974018 @default.
• W4311461947 cites W2000932885 @default.
• W4311461947 cites W2001507422 @default.
• W4311461947 cites W2012014449 @default.
• W4311461947 cites W2028241065 @default.
• W4311461947 cites W2039952872 @default.
• W4311461947 cites W2048387680 @default.
• W4311461947 cites W2057182962 @default.
• W4311461947 cites W2058998445 @default.
• W4311461947 cites W2068424727 @default.
• W4311461947 cites W2068778426 @default.
• W4311461947 cites W2070493638 @default.
• W4311461947 cites W2082137964 @default.
• W4311461947 cites W2089763487 @default.
• W4311461947 cites W2093020519 @default.
• W4311461947 cites W2103959917 @default.
• W4311461947 cites W2111286455 @default.
• W4311461947 cites W2112458566 @default.
• W4311461947 cites W2115570761 @default.
• W4311461947 cites W2117063635 @default.
• W4311461947 cites W2123101917 @default.
• W4311461947 cites W2127546500 @default.
• W4311461947 cites W2136701119 @default.
• W4311461947 cites W2136704614 @default.
• W4311461947 cites W2148877877 @default.
• W4311461947 cites W2161020850 @default.
• W4311461947 cites W2163450852 @default.
• W4311461947 cites W2166516660 @default.
• W4311461947 cites W2302952123 @default.
• W4311461947 cites W2490548105 @default.
• W4311461947 cites W2613126452 @default.
• W4311461947 cites W2621694982 @default.
• W4311461947 cites W27682692 @default.
• W4311461947 cites W2770914795 @default.
• W4311461947 cites W2801490189 @default.
• W4311461947 cites W2805142011 @default.
• W4311461947 cites W2809863894 @default.
• W4311461947 cites W2889682228 @default.
• W4311461947 cites W2911964244 @default.
• W4311461947 cites W2914609490 @default.
• W4311461947 cites W2918084323 @default.
• W4311461947 cites W2958796117 @default.
• W4311461947 cites W2965502336 @default.
• W4311461947 cites W2998697012 @default.
• W4311461947 cites W3023590303 @default.
• W4311461947 cites W3032830899 @default.
• W4311461947 cites W3085944235 @default.
• W4311461947 cites W3087613434 @default.
• W4311461947 cites W3093950702 @default.
• W4311461947 cites W3100504477 @default.
• W4311461947 cites W3124203304 @default.
• W4311461947 cites W3134588381 @default.
• W4311461947 cites W3153963714 @default.
• W4311461947 cites W3165544214 @default.
• W4311461947 cites W3178598619 @default.
• W4311461947 cites W3180574219 @default.
• W4311461947 cites W3183457194 @default.
• W4311461947 cites W3197413489 @default.
• W4311461947 cites W3198159648 @default.
• W4311461947 cites W3204858687 @default.
• W4311461947 cites W3205412344 @default.
• W4311461947 cites W3215640553 @default.
• W4311461947 cites W4200064788 @default.
• W4311461947 cites W4205924964 @default.
• W4311461947 cites W4220818887 @default.
• W4311461947 cites W4280605314 @default.
• W4311461947 cites W4285391973 @default.
• W4311461947 cites W4291710846 @default.
• W4311461947 cites W4294541781 @default.
• W4311461947 doi "https://doi.org/10.1016/j.ecoinf.2022.101933" @default.
• W4311461947 hasPublicationYear "2023" @default.
• W4311461947 type Work @default.
• W4311461947 citedByCount "4" @default.
• W4311461947 countsByYear W43114619472023 @default.
• W4311461947 crossrefType "journal-article" @default.
• W4311461947 hasAuthorship W4311461947A5000017665 @default.
• W4311461947 hasAuthorship W4311461947A5006330339 @default.
• W4311461947 hasAuthorship W4311461947A5041081967 @default.
• W4311461947 hasAuthorship W4311461947A5047836403 @default.
• W4311461947 hasBestOaLocation W43114619471 @default.
• W4311461947 hasConcept C105795698 @default.

• next