Abstract:There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m 2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.

Miscanthus is a perennial energy grass predominantly used for combustion but there is increasing interest in fermenting the cell-wall carbohydrates or green-cutting for soluble sugars to produce bioethanol. Our aims were to: (1) quantify non-structural carbohydrates (NSC), (2) observe the timing of seasonal shifts in the stems and rhizome, and (3) identify developmental and/or climatic conditions that promoted carbohydrate remobilization from the stems to the rhizome during senescence. Two genotypes of Miscanthus sinensis, a Miscanthus sacchariflorus and a Miscanthus × giganteus were grown at replicated field sites in Aberystwyth, West Wales and Harpenden, South East England. NSC were quantified from the rhizome and aboveground organs and then correlated with climatic data collected from on-site weather stations. PAR and maximum daily temperatures were higher at Harpenden throughout the year, but daily minimum temperatures were lower. Senescence was accelerated at Harpenden. Carbohydrates were retained within the stems of nonflowering genotypes, at both sites, in winter and were still present after a frost event to −2°C. Rhizome starch concentrations were at least equal to the previous winter's levels (February 2011) by September. Lower daily minimum temperatures accelerate the rate of senescence and warmer daily maximum temperatures cannot counteract this effect. At current yields, M. × giganteus, could produce 0.7 t ha −1 of NSC in addition to ligno-cellulosic biomass in November but with concerted breeding efforts this could be targeted for improvement as has been achieved in other crops. Shifting harvests forward to November would not leave the rhizome depleted of carbohydrates.

Willows (Salix spp.) grown as short rotation coppice (SRC) are viewed as a sustainable source of biomass with a positive greenhouse gas (GHG) balance due to their potential to fix and accumulate carbon (C) below ground. However, exploiting this potential has been limited by the paucity of data available on below ground biomass allocation and the extent to which it varies between genotypes. Furthermore, it is likely that allocation can be altered considerably by environment. To investigate the role of genotype and environment on allocation, four willow genotypes were grown at two replicated field sites in southeast England and west Wales, UK. Above and below ground biomass was intensively measured over two two-year rotations. Significant genotypic differences in biomass allocation were identified, with below ground allocation differing by up to 10% between genotypes. Importantly, the genotype with the highest below ground biomass also had the highest above ground yield. Furthermore, leaf area was found to be a good predictor of below ground biomass. Growth environment significantly impacted allocation; the willow genotypes grown in west Wales had up to 94% more biomass below ground by the end of the second rotation. A single investigation into fine roots showed the same pattern with double the volume of fine roots present. This greater below ground allocation may be attributed primarily to higher wind speeds, plus differences in humidity and soil characteristics. These results demonstrate that the capacity exists to breed plants with both high yields and high potential for C accumulation.

Vegetation indices, such as the Normalised Difference Vegetation Index (NDVI), are common metrics used for measuring traits of interest in crop phenotyping. However, traditional measurements of these indices are often influenced by multiple confounding factors such as canopy cover and reflectance of underlying soil, visible in canopy gaps. Digital cameras mounted to Unmanned Aerial Vehicles offer the spatial resolution to investigate these confounding factors, however incomplete methods for radiometric calibration into reflectance units limits how the data can be applied to phenotyping. In this study, we assess the applicability of very high spatial resolution (1 cm) UAV-based imagery taken with commercial off the shelf (COTS) digital cameras for both deriving calibrated reflectance imagery, and isolating vegetation canopy reflectance from that of the underlying soil. We present new methods for successfully normalising COTS camera imagery for exposure and solar irradiance effects, generating multispectral (RGB-NIR) orthomosaics of our target field-based wheat crop trial. Validation against measurements from a ground spectrometer showed good results for reflectance (R2 ≥ 0.6) and NDVI (R2 ≥ 0.88). Application of imagery collected through the growing season and masked using the Excess Green Red index was used to assess the impact of canopy cover on NDVI measurements. Results showed the impact of canopy cover artificially reducing plot NDVI values in the early season, where canopy development is low.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.