UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using a VIs time series, and predicted yield using a peak descriptor derived from a VIs time series with 2.3 Mg DM ha−1 of the root mean square error (RMSE). The study demonstrates the potential of UAVs’ multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production

Estimating radiation interception in heterogeneous orchards using high spatial resolution airborne imagery

This letter outlines a method for quantifying the fraction of intercepted photosynthetically active radiation (fIPAR) from high spatial resolution airborne images acquired from an unmanned aerial vehicle. Airborne campaigns provided imagery of peach and citrus orchards using a six-band multispectral camera with 15-cm resolution. At the time of the airborne flights, field measurements of fIPAR taken with a ceptometer and structural data were obtained to characterize the study sites. Measuring fIPAR can be time consuming because of the need to sample for spatial and temporal variability. In this context, remote sensing techniques are useful as they make it possible to assess large areas. There is a lack of studies exploring the use of remote sensing techniques to estimate fIPAR in structurally complex crops. In this letter, the use of high spatial resolution imagery allowed us to classify each study plot into three pure components: vegetation, shaded soil, and sunlit soil. The radiation intercepted by a canopy is determined by the architecture and optical properties of the canopy. Consequently, the fractions of each component and their pure reflectance were used to estimate fIPAR in each study area with rmse = 0.06 for orange orchards and peach orchards. © 2013 IEEE.Peer Reviewe

High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields