The utility of unmanned aerial vehicles (UAV) imagery in retrieving phenotypic data to support plant breeding research has been a topic of increasing interest in recent years. The advantages of image-based phenotyping are related to the high spatial and temporal resolution of the retrieved data and the non-destructive and rapid method of data acquisition. This study trains parametric and nonparametric regression models to retrieve leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional vegetation cover (fCover), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and grain yield (GY) of winter durum wheat breeding experiment from four-bands UAV images. A ground dataset, collected during two field campaigns and complemented with data from a previous study, is used for model development. The dataset is split at random into two parts, one for training and one for testing the models. The tested parametric models use the vegetation index formula and parametric functions. The tested nonparametric models are partial least square regression (PLSR), random forest regression (RFR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The retrieved biophysical variables along with traditional phenotypic traits (plant height, yield, and tillering) are analysed for detection of genetic diversity, proximity, and similarity in the studied genotypes. Analysis of variance (ANOVA), Duncan’s multiple range test, correlation analysis, and principal component analysis (PCA) are performed with the phenotypic traits. The parametric and nonparametric models show close results for GY retrieval, with parametric models indicating slightly higher accuracy (R2 = 0.49; MRSE = 0.58 kg/plot; rRMSE = 6.1%). However, the nonparametric model, GPR, computes per pixel uncertainty estimation, making it more appealing for operational use. Furthermore, our results demonstrate that grain filling was better than flowering phenological stage to predict GY. The nonparametric models show better results for biophysical variables retrieval, with GPR presenting the highest prediction performance. Nonetheless, robust models are found only for LAI (R2 = 0.48; MRSE = 0.64; rRMSE = 13.5%) and LCC (R2 = 0.49; MRSE = 31.57 mg m−2; rRMSE = 6.4%) and therefore these are the only remotely sensed phenotypic traits included in the statistical analysis for preliminary assessment of wheat productivity. The results from ANOVA and PCA illustrate that the retrieved remotely sensed phenotypic traits are a valuable addition to the traditional phenotypic traits for plant breeding studies. We believe that these preliminary results could speed up crop improvement programs; however, stronger interdisciplinary research is still needed, as well as uncertainty estimation of the remotely sensed phenotypic traits.

No abstract

Phenocams that capture images of a given area in the RGB or near-infrared (NIR) spectrum have been used for more than a decade to estimate phenology in natural landscapes and crop fields. The aim of our study is to estimate phenological parameters, start (SOS) and end (EOS) of season, for barley, from RGB and NIR Phenocam and compare them with in-situ observations from two sites, one with growing season 2014/2015 and the other with growing season 2021/2022. Time series of Phenocam Green Chromatic Coordinate (GCC) and Normalized Difference Vegetation Index (NDVI) were computed then scaled to Harmonized Landsat-8 and Sentinel-2 surface (HLS), available for both sites, and Sentinel-2 (S2), available for only one site, datasets. The HLS and S2 datasets were gap filled with classical and machine learning methods before the scaling. Phenological parameters were extracted from the scaled GCC and NDVI Phenocam data and from the gap filled HLS and S2 datasets. Our preliminary results show that the SOS can be modelled with one day difference compared with the in-situ observed with the scaled Phenocam NDVI and a week difference compared with the in-situ observed with gap filled HLS and S2 datasets with both vegetation indices.

Еstimation of crop canopy parameters is important task for remote sensing monitoring of agriculture and constructing strategies for within-field management. The main objective of this study is to evaluate the retrieval from Sentinel-2 images by parametric and non-parametric statistical models several crop canopy parameters for monitoring before winter and after winter rapeseed crop in real farming conditions of North East Bulgaria. For the calibration of the models in-situ data from three field campaigns is used. For most of the studied parameters models with good accuracy were identified, except for aboveground fresh biomass. The best identified model for vegetation fraction (RMSEcv=0.14%) and plant density (RMSEcv=9 nb/m2) were parametric models with three band vegetation index (3BSI-Tian) and linear fitting function for the first, three band vegetation index (3BSI-Verreslt) and polynomial for the second parameter. For aboveground dry biomass (RMSEcv=52 g/m²), mean plant height (RMSEcv=4cm) and nitrogen concentration in fresh biomass (RMSEcv=2%) the best models were non-parametric, Gaussian Processes Regression for the first parameter and Variational Heteroscedastic variant of the Gaussian Processes Regression for the other two.