FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4253553381> ?p ?o ?g. }

• W4253553381 abstract "Abstract Background The objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020’s tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets. Results First-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. Conclusions In addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs." @default.
• W4253553381 created "2022-05-12" @default.
• W4253553381 creator A5064749726 @default.
• W4253553381 creator A5077784983 @default.
• W4253553381 creator A5087093703 @default.
• W4253553381 date "2021-04-05" @default.
• W4253553381 modified "2023-09-28" @default.
• W4253553381 title "Prediction of plant-level tomato biomass and yield using machine learning with unmanned aerial vehicle imagery" @default.
• W4253553381 doi "https://doi.org/10.21203/rs.3.rs-344860/v1" @default.
• W4253553381 hasPublicationYear "2021" @default.
• W4253553381 type Work @default.
• W4253553381 citedByCount "1" @default.
• W4253553381 countsByYear W42535533812022 @default.
• W4253553381 crossrefType "posted-content" @default.
• W4253553381 hasAuthorship W4253553381A5064749726 @default.
• W4253553381 hasAuthorship W4253553381A5077784983 @default.
• W4253553381 hasAuthorship W4253553381A5087093703 @default.
• W4253553381 hasBestOaLocation W42535533811 @default.
• W4253553381 hasConcept C105795698 @default.
• W4253553381 hasConcept C12267149 @default.
• W4253553381 hasConcept C134306372 @default.
• W4253553381 hasConcept C148483581 @default.
• W4253553381 hasConcept C154945302 @default.
• W4253553381 hasConcept C169258074 @default.
• W4253553381 hasConcept C182365436 @default.
• W4253553381 hasConcept C22679943 @default.
• W4253553381 hasConcept C33923547 @default.
• W4253553381 hasConcept C41008148 @default.
• W4253553381 hasConceptScore W4253553381C105795698 @default.
• W4253553381 hasConceptScore W4253553381C12267149 @default.
• W4253553381 hasConceptScore W4253553381C134306372 @default.
• W4253553381 hasConceptScore W4253553381C148483581 @default.
• W4253553381 hasConceptScore W4253553381C154945302 @default.
• W4253553381 hasConceptScore W4253553381C169258074 @default.
• W4253553381 hasConceptScore W4253553381C182365436 @default.
• W4253553381 hasConceptScore W4253553381C22679943 @default.
• W4253553381 hasConceptScore W4253553381C33923547 @default.
• W4253553381 hasConceptScore W4253553381C41008148 @default.
• W4253553381 hasLocation W42535533811 @default.
• W4253553381 hasLocation W42535533812 @default.
• W4253553381 hasOpenAccess W4253553381 @default.
• W4253553381 hasPrimaryLocation W42535533811 @default.
• W4253553381 hasRelatedWork W2022356511 @default.
• W4253553381 hasRelatedWork W2202048117 @default.
• W4253553381 hasRelatedWork W2423455227 @default.
• W4253553381 hasRelatedWork W2742495185 @default.
• W4253553381 hasRelatedWork W3122308606 @default.
• W4253553381 hasRelatedWork W3204182506 @default.
• W4253553381 hasRelatedWork W4224121874 @default.
• W4253553381 hasRelatedWork W4320026695 @default.
• W4253553381 hasRelatedWork W4327511089 @default.
• W4253553381 hasRelatedWork W2345184372 @default.
• W4253553381 isParatext "false" @default.
• W4253553381 isRetracted "false" @default.
• W4253553381 workType "article" @default.