Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat

Abstract

Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for nondestructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha −1 , T2 = 180 kg ha −1 , and T3 = 240 kg ha −1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R 2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R 2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R 2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha −1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.

QTL mapping of root traits in wheat under different phosphorus levels using hydroponic culture

“…1 ). Zhongmai 895 performed higher as compared to Yangmai 16 across the P treatments, as previously reported elite for high root growth at seedling stage and field-based nitrogen use efficiency [ 35 , 36 ]. SDW and RRS were negatively correlated across treatments (Fig.…”

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

“…These five VIs could be selected as the best ones to predict variations among the genotypes for grain yield. Previously, some studies have reported that GNDVI and NDRE were the best predictors for grain yield and nutrient uptake efficiencies across the growth stages [5,64]. Therefore, these VIs can be used to select high-yielding genotypes with high accuracy in large breeding programs.…”

“…The UAV-based multispectral vegetation indices have been increasingly exploited to predict plant physiological traits by crop breeding studies [5,64]. In this study, 20 VIs covering all five light bands captured by the multispectral sensor were taken to evaluate the machine learning models for grain yield prediction.…”

UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat

“…The application of UAV-based imaging techniques has been broadening in several areas of agricultural sciences thanks to their ability to analyze plant temperature and color discrepancies between distinct biological samples ( Padmavathi and Thangadurai, 2016 ; Cambaza et al, 2019 ). Many recent studies have exploited UAV-based sensors to monitor, detect and phenotype plant stresses in forestry ( Sagan et al, 2019 ), as well as to estimate leaf nitrogen concentration ( Lu et al, 2021 ), water and nitrogen use efficiencies ( Yang et al, 2020 ), and salinity stress ( Johansen et al, 2019 ) in different crops. Plant pathologists are recently also benefiting from the application of UAV-based sensors; in fact, UAV multispectral and hyperspectral imaging have been used to detect Xylella fastidiosa ( Castrignanò et al, 2021 ) and FHB ( Liu et al, 2020 ), respectively.…”