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

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Development and Evaluation of Unmanned Aerial Vehicles for High Throughput Phenotyping of Field-based Wheat Trials.

Growing demands for increased global yields are driving researchers to develop improved crops, capable of securing higher yields in the face of significant challenges including climate change and competition for resources. However, abilities to measure favourable physical characteristics (phenotypes) of key crops in response to these challenges is limited. For crop breeders and researchers, current abilities to phenotype field-based experiments with sufficient precision, resolution and throughput is restricting any meaningful advances in crop development. This PhD thesis presents work focused on the development and evaluation of Unmanned Aerial Vehicles (UAVs) in combination with remote sensing technologies as a solution for improved phenotyping of field-based crop experiments. Chapter 2 presents first, a review of specific target phenotypic traits within the categories of crop morphology and spectral reflectance, together with critical review of current standard measurement protocols. After reviewing phenotypic traits, focus turns to UAVs and UAV specific technologies suitable for the application of crop phenotyping, including critical evaluation of both the strengths and current limitations associated with UAV methods and technologies, highlighting specific areas for improvement. Chapter 3 presents a published paper successfully developing and evaluating Structure from Motion photogrammetry for accurate (R2 ≥ 0.93, RMSE ≤ 0.077m, and Bias ≤ -0.064m) and temporally consistent 3D reconstructions of wheat plot heights. The superior throughput achieved further facilitated measures of crop growth rate through the season; whilst very high spatial resolutions highlighted both the inter- and intra-plot variability in crop heights, something unachievable with the traditional manual ruler methods. Chapter 4 presents published work developing and evaluating modified Commercial ‘Off the Shelf’ (COTS) cameras for obtaining radiometrically calibrated imagery of canopy spectral reflectance. Specifically, development focussed on improving application of these cameras under variable illumination conditions, via application of camera exposure, vignetting, and irradiance corrections. Validation of UAV derived Normalised Difference Vegetation Index (NDVI) against a ground spectrometer from the COTS cameras (0.94 ≤ R2 ≥ 0.88) indicated successful calibration and correction of the cameras. The higher spatial resolution obtained from the COTS cameras, facilitated the assessment of the impact of background soil reflectance on derived mean Normalised Difference Vegetation Index (NDVI) measures of experimental plots, highlighting the impact of incomplete canopy on derived indices. Chapter 5 utilises the developed methods and cameras from Chapter 4 to assess the impact of nitrogen fertiliser application on the formation and senescence dynamics of canopy traits over multiple growing seasons. Quantification of changes in canopy reflectance, via NDVI, through three select trends in the wheat growth cycle were used to assess any impact of nitrogen on these periods of growth. Results showed consistent impact of zero nitrogen application on crop canopies within all three development phases. Additional results found statistically significant positive correlations between quantified phases and harvest metrics (e.g. final yield), with greatest correlations occurring within the second (Full Canopy) and third (Senescence) phases. Chapter 6 focuses on evaluation of the financial costs and throughput associated with UAVs; with specific focus on comparison to conventional methods in a real-world phenotyping scenario. A ‘cost throughput’ analysis based on real-world experiments at Rothamsted Research, provided quantitative assessment demonstrating both the financial savings (£4.11 per plot savings) and superior throughput obtained (229% faster) from implementing a UAV based phenotyping strategy to long term phenotyping of field-based experiments. Overall the methods and tools developed in this PhD thesis demonstrate UAVs combined with appropriate remote sensing tools can replicate and even surpass the precision, accuracy, cost and throughput of current strategies

Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

Integration of Multiscale Sensing Data for Phenomics Applications

Sensing technologies can be a powerful tool for phenotyping in breeding programs. Plant phenotypes can be assessed non-invasively and repeatedly across the whole population and throughout the plant development period utilizing advanced sensors and remote sensing platforms. In this study, multiscale sensing platforms—satellite, unmanned aerial vehicle (UAV), proximal sensing system, and Internet of Things (IoT) based sensing systems—equipped with sensors such as visible/RGB, multispectral, and hyperspectral systems were utilized for field-based phenomics applications. The applicability of a suitable sensing technology depends on the area of study, specific phenomics application, sensor specification, and data acquisition conditions. Three main phenomics applications were explored: (i) pasture crop health status evaluation, (ii) above-ground biomass quantity and quality evaluation in the field pea, and (iii) evaluating wheat yield potential in winter and spring wheat. The first study demonstrates the reliability of using a high-resolution satellite (ground sampling distance, GSD = 3 m) and UAV imagery for pasture management. The data from multiscale sensing data showed that the grazing density significantly affected pasture biomass (p < 0.05) only in 2019, and the vegetation index (VI) data from the two imagery types were highly correlated (r ≥ 0.78, p < 0.001, 2019). In the second study, the above-ground biomass (AGBM) and biomass quality (12 quality traits) were evaluated using UAV-based RGB and multispectral imaging, and hyperspectral sensing, respectively, in the winter pea breeding program (2019 and 2020 seasons). Three image processing approaches were evaluated for AGBM estimation, where the best results were acquired using the 3D point cloud model at 1.5 alpha shape technique showing high correlation with harvested fresh (r = 0.78–0.81, p < 0.001) and dry (r = 0.70–0.81, p < 0.001) AGBM. Similarly, the selected features from the normalized difference spectral indices and the ratio spectral indices extracted from hyperspectral data with the random forest model provided high predictive accuracy for all 12 biomass quality traits (0.81 < R2 < 0. 93; 0.05 < RMSE (%) < 1.80; 0.03 < MAE (%) < 1.32).In the wheat study, the vegetation indies were highly correlated between satellite (GSD = 0.31 m) and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) from winter and spring wheat breeding trials (2020 and 2021). The yield prediction using such VIs with the high-resolution satellite imagery (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) was also high. In addition to these two platforms, an intelligent and compact IoT-based sensor system was developed for independent and automated phenomics applications to measure and monitor plant responses in real-time. The sensor development, improvisation, and implementation encompassed three field seasons (2020, 2021, and 2022 seasons). The developed IoT-based sensor system could be successfully implemented to monitor multiple trials for timely crop management and increased resource efficiency. The system shows a high potential for supporting plant breeding programs for in-field phenotyping applications. All studies demonstrated promising results in monitoring and estimating crop performance and phenotypic traits using multiscale sensing systems

Assessment of RGB vegetation indices to estimate chlorophyll content in sugar beet leaves in the final cultivation stage

Estimation of chlorophyll content with portable meters is an easy way to quantify crop nitrogen status in sugar beet leaves. In this work, an alternative for chlorophyll content estimation using RGB-only vegetation indices has been explored. In a first step, pictures of spring-sown ‘Fernanda KWS’ variety sugar beet leaves taken with a commercial camera were used to calculate 25 RGB indices reported in the literature and to obtain 9 new indices through principal component analysis (PCA) and stepwise linear regression (SLR) techniques. The performance of the 34 indices was examined in order to evaluate their ability to estimate chlorophyll content and chlorophyll degradation in the leaves under different natural light conditions along 4 days of the canopy senescence period. Two of the new proposed RGB indices were found to improve the already good performance of the indices reported in the literature, particularly for leaves featuring low chlorophyll contents. The 4 best indices were finally tested in field conditions, using unmanned aerial vehicle (UAV)-taken photographs of a sugar beet plot, finding a reasonably good agreement with chlorophyll-meter data for all indices, in particular for I2 and (R−B)/(R+G+B). Consequently, the suggested RGB indices may hold promise for inexpensive chlorophyll estimation in sugar beet leaves during the harvest time, although a direct relationship with nitrogen status still needs to be validated

Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography

Background: The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. Results: In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with R2 and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with R2 and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the R2 and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. Conclusions: Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density

Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging

Data collection with unmanned aerial vehicles (UAVs) fills a gap on the observational scale in re-mote sensing by delivering high spatial and temporal resolution data that is required in crop growth monitoring. The latter is part of precision agriculture that facilitates detection and quan-tification of within-field variability to support agricultural management decisions such as effective fertilizer application. Biophysical parameters such as plant height and biomass are monitored to describe crop growth and serve as an indicator for the final crop yield. Multi-temporal crop surface models (CSMs) provide spatial information on plant height and plant growth. This study aims to examine whether (1) UAV-based CSMs are suitable for plant height modelling, (2) the derived plant height can be used for biomass estimation, and (3) the combination of plant height and vegetation indices has an added value for biomass estimation. To achieve these objectives, UAV-flight campaigns were carried out with a red-green-blue (RGB) camera over controlled field experiments on three study sites, two for summer barley in Western Germany and one for rice in Northeast China. High-resolution, multi-temporal CSMs were derived from the images by using computer vision software following the structure from motion (SfM) approach. The results show that plant height and plant growth can be accurately modelled with UAV-based CSMs from RGB imaging. To maximise the CSMs’ quality, accurate flight planning and well-considered data collection is necessary. Furthermore, biomass is successfully estimated from the derived plant height, with the restriction that results are based on a single-year dataset and thus require further validation. Nevertheless, plant height shows robust estimates in comparison with various vegetation indices. As for biomass estimation in early growth stages additional po-tential is found in exploiting visible band vegetation indices from UAV-based red-green-blue (RGB) imaging. However, the results are limited due to the use of uncalibrated images. Combining visible band vegetation indices and plant height does not significantly improve the performance of the biomass models. This study demonstrates that UAV-based RGB imaging delivers valuable data for productive crop monitoring. The demonstrated results for plant height and biomass estimation open new possi-bilities in precision agriculture by capturing in-field variability

Remote sensing and on-farm experiments for determining in-season nitrogen rates in winter wheat – Options for implementation, model accuracy and remaining challenges

Optimised nitrogen (N) fertilisation can be used to increase farm profits, to realise the achievement of quality goals for produce, and to reduce environmental risks in the form of leaching and/or volatilisation of N compounds from the fields. This study examined options and challenges for remote sensing-based variable rate supplemental N fertilisation in winter wheat (Triticum aestivum L.). The models were based on data from ten field trials conducted in different regions across Sweden over three years. A two-step approach for modelling optimal N rates, suitable for practical implementation in precision agriculture, was developed and evaluated. The expected accuracies for new sites and years were assessed by leave-one-entire-trial-out cross-validation. In a first step, the average N rate was modelled from site-specific information, including data that can be obtained from on-farm experiments, i.e. N uptake in plots without N fertilisation (zero-plots) and N uptake in plots with non-limiting N supply (max-plots). In the second step, additions or subtractions from this average N rate was modelled based on vegetation indices (VIs) mapped by remote sensing. Mean absolute error of the best prediction was 14 kg N ha−1. In a practical application, however, there will be additional uncertainty from several sources, e.g. uncertainty in the assessment of yield potential. The best mean N rate model was based on geographical region, cultivar, N uptake in zero-plots and yield potential, while the best model of relative N rate within the field used a new multispectral index (d75r6), which was designed to give a standardized measure of the steepness of the red edge of reflectance of a crop canopy spectrum. Several other multispectral VIs also performed well but red-green-blue indices were less useful. We conclude that remote sensing (to capture within-field spatial variation patterns), on-farm experiments (to determine the field mean N rate), and the farmers’ experience and knowledge on local conditions (e.g. to assess the yield potential), is a useful combination of information sources in decision support systems for variable rate application of N. Options and remaining research needs for the setup of such a system are discussed