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• W4200528078 abstract "Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R2) = 0.527-0.667] compared to using a single multispectral sensor (R2 = 0.130-0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R2 = 0.667, root mean square error (RMSE) = 0.881 t ha-1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha-1. The EWF model outperformed at all the individual growth stages with R2 varying from 0.677 to 0.729. The best prediction result (R2 = 0.729, RMSE = 0.831 t ha-1, RRMSE = 14.3%, and MAE = 0.684 t ha-1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield." @default.
• W4200528078 created "2021-12-31" @default.
• W4200528078 creator A5041617856 @default.
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• W4200528078 date "2021-12-20" @default.
• W4200528078 modified "2023-10-01" @default.
• W4200528078 title "Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data" @default.
• W4200528078 cites W1964050442 @default.
• W4200528078 cites W1964217023 @default.
• W4200528078 cites W1968262514 @default.
• W4200528078 cites W1991739869 @default.
• W4200528078 cites W1993273815 @default.
• W4200528078 cites W1998686312 @default.
• W4200528078 cites W1998943389 @default.
• W4200528078 cites W2000613913 @default.
• W4200528078 cites W2004787007 @default.
• W4200528078 cites W2011475440 @default.
• W4200528078 cites W2012686349 @default.
• W4200528078 cites W2024069451 @default.
• W4200528078 cites W2029567272 @default.
• W4200528078 cites W2034794420 @default.
• W4200528078 cites W2041137640 @default.
• W4200528078 cites W2047454491 @default.
• W4200528078 cites W2051055546 @default.
• W4200528078 cites W2052700773 @default.
• W4200528078 cites W2056352756 @default.
• W4200528078 cites W2059488281 @default.
• W4200528078 cites W2067777246 @default.
• W4200528078 cites W2075801690 @default.
• W4200528078 cites W2082087893 @default.
• W4200528078 cites W2087047858 @default.
• W4200528078 cites W2094420085 @default.
• W4200528078 cites W2095939249 @default.
• W4200528078 cites W2097536090 @default.
• W4200528078 cites W2111947859 @default.
• W4200528078 cites W2112674101 @default.
• W4200528078 cites W2113410727 @default.
• W4200528078 cites W2118308777 @default.
• W4200528078 cites W2125459357 @default.
• W4200528078 cites W2129804846 @default.
• W4200528078 cites W2139925058 @default.
• W4200528078 cites W2144877227 @default.
• W4200528078 cites W2149813070 @default.
• W4200528078 cites W2161815745 @default.
• W4200528078 cites W2210859295 @default.
• W4200528078 cites W2317582304 @default.
• W4200528078 cites W2495898644 @default.
• W4200528078 cites W2531338938 @default.
• W4200528078 cites W2614563231 @default.
• W4200528078 cites W2619751036 @default.
• W4200528078 cites W2646675373 @default.
• W4200528078 cites W2729098382 @default.
• W4200528078 cites W2736116482 @default.
• W4200528078 cites W2742109465 @default.
• W4200528078 cites W2744957585 @default.
• W4200528078 cites W2757246795 @default.
• W4200528078 cites W2765275914 @default.
• W4200528078 cites W2767657507 @default.
• W4200528078 cites W2791160863 @default.
• W4200528078 cites W2792792330 @default.
• W4200528078 cites W2797462888 @default.
• W4200528078 cites W2803133125 @default.
• W4200528078 cites W2809360944 @default.
• W4200528078 cites W2897017401 @default.
• W4200528078 cites W2898710507 @default.
• W4200528078 cites W2902773593 @default.
• W4200528078 cites W2912977378 @default.
• W4200528078 cites W2913858069 @default.
• W4200528078 cites W2920653747 @default.
• W4200528078 cites W2935876239 @default.
• W4200528078 cites W2964052062 @default.
• W4200528078 cites W2979915629 @default.
• W4200528078 cites W2989660890 @default.
• W4200528078 cites W2996041315 @default.
• W4200528078 cites W2997068971 @default.
• W4200528078 cites W3034787265 @default.
• W4200528078 cites W3037131441 @default.
• W4200528078 cites W3037417372 @default.
• W4200528078 cites W3060826539 @default.
• W4200528078 cites W3082521893 @default.
• W4200528078 cites W3101137437 @default.
• W4200528078 cites W3108919541 @default.
• W4200528078 cites W3110128224 @default.
• W4200528078 cites W3120192914 @default.
• W4200528078 cites W3135871359 @default.
• W4200528078 cites W3155970018 @default.
• W4200528078 cites W3159396524 @default.
• W4200528078 cites W3174823696 @default.
• W4200528078 cites W4239944110 @default.
• W4200528078 doi "https://doi.org/10.3389/fpls.2021.730181" @default.
• W4200528078 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34987529" @default.
• W4200528078 hasPublicationYear "2021" @default.
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