Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries

Citation: Haghighattalab, A., Perez, L. G., Mondal, S., Singh, D., Schinstock, D., Rutkoski, J., . . . Poland, J. (2016). Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, 12, 15. https://doi.org/10.1186/s13007-016-0134-6Background: Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For field-based high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. Results: We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries

Near real-time monitoring of buried oil pipeline right-of-way for third-party incursion

Many security systems employing different methods have been proposed to protect buried oil pipelines transporting petroleum products from the well head via the refinery to: depots and other receiving stations. Currently there is a security gap in the monitoring of these buried pipelines in real time and in keeping them protected from third party interference. This thesis addresses the problem of monitoring these systems by developing an automated image analysis system with the aid of a low-cost multisensory Unmanned Aerial Vehicle (UAV) for monitoring of buried pipeline right-of-way (ROW). The method used in this research is based on the identification of threat objects of interest from the video frame sequences of the pipeline right-of-way acquired by the UAV. This is achieved by training the system to recognise objects of interest using trained correlation filters. To determine the geographical location of detected objects, the Video frame sequences captured by the UAV platform were ortho-rectified to form ortho-images which were then mosaicked to form a seamless Digital Surface Model (DSM) covering the test area using a photogrammetry model. The DSM formed from the mosaicking of ortho-images is then emerged with a digital globe for geo-referencing of detected objects. Experiments were carried out on a test field located in United Kingdom and Nigeria, where video and telemetry data were collected, then processed using the techniques created in this research. The results demonstrated that the developed correlation filter was able to detect objects of interest despite the distortions that come with the object image, due to the fact that the expected distortion was compensated for using the training images. When compared with the 6 control points in the digital globe the accuracy of the two-dimension DSM gave a misalignment error of between 2 and 3 metres

An evaluation of imagery from an unmanned aerial vehicle (UAV) for the mapping of intertidal macroalgae on Seal Sands, Tees Estuary, UK

The Seal Sands area of Teesmouth is designated a Special Protection Area under the habitats directive because guideline concentrations of nutrients in coastal waters are exceeded. This may be responsible for extensive growth of the green filamentous macroalgae Enteromorpha sp., and literature suggests that algal cover in the intertidal zone is detrimental to the feeding behaviour of wading bird species. Although numerous studies have highlighted the causes and consequences of macroalgal cover, the complex spatial and temporal dynamics of macroalgal bloom growth are not as well understood, and hence there is a need to develop a precise and cost effective monitoring method for the mapping and quantifying of algal biomass. Previous studies have highlighted several image processing techniques that could be applied to high resolution airborne imagery in order to predict algal biomass. In order to test these methods, high resolution imagery was acquired in the Sea լ Sands area using a lightweight SmartPlanes SmartOne unmanned aerial vehicle (UAV) equipped with a near-infrared sensitive 5-megapixel Canon IXUS compact camera, a standard 6-megapixel Canon IXUS compact camera and a Garmin Geko 201 handheld GPS device. Imagery was acquired in November 2006 and June 2007 in order to examine the spectral response of Enteromorpha sp. at different time periods within a macroalgal growth cycle. Images were mosaicked and georeferenced using ground control points located with a Leica 1200 differential GPS and processed to allow for analysis of their spectral and textural properties. Samples of macroalgal cover were collected, georeferenced and their dry biomass content obtained for ground truthing. Although textural entropy and inertia did not correlate significantly with macroalgal biomass, normalised green-red difference index (NGRDI), normalised difference vegetation index (NDVI) and colour saturation computed on the imagery showed a good degree of linear correlation with Enteromorpha sp. dry weight, achieving coefficients of determination in excess of r(^2)= 0.6 for both the November2006 and June 2007 image sets. Linear regression was used to establish predictive models to estimate macroalgal biomass from image spectral properties. Enteromorpha sp. Biomass estimations of 71.4 g DW m(^-2) and 7.9g DW m(^-2) were established for the November 2006 and June2007 data acquisition sessions respectively. Despite a lack of previous biomass quantification for Seal Sands, the favourable performance of a UAV in terms of operating cost and man hours required for image acquisition suggests that unmanned aerial vehicles may present a viable method for the mapping of intertidal algal biomass on an annual basis

Field phenomics:will it enable crop improvement?

Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders’ needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches

Enhancing Remote Sensing for Agriculture Using Small Unmanned Aerial Systems: San Diego, CA, as a Test Case

The development of small Global Positioning System (GPS) antennas and microprocessors has propelled the advancement of affordable Small Unmanned Aerial Systems (SUASs), which will dramatically expand the remote sensing field, making timely, high-resolution imagery readily available. The low cost and simple operation of SUASs makes them an attractive option for agriculture. Flying a SUAS 400 ft above ground level (AGL) in a flight path that allows for significant image overlap can yield sub- 5cm resolution imagery, which in turn can be mosaicked and used for multispectral imagery analysis. With results rivaling the most advanced commercial imaging sensors, SUASs can be used to identify stressed vegetation and aid in decision making that ultimately leads to more efficient farming practices and consistent yields. Furthermore, minimal operating costs promote reduced revisit times and enable persistent collection to monitor changes over time

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

A review of current and potential applications of remote sensing to study the water status of horticultural crops

Published: 17 January 2020With increasingly advanced remote sensing systems, more accurate retrievals of crop water status are being made at the individual crop level to aid in precision irrigation. This paper summarises the use of remote sensing for the estimation of water status in horticultural crops. The remote measurements of the water potential, soil moisture, evapotranspiration, canopy 3D structure, and vigour for water status estimation are presented in this comprehensive review. These parameters directly or indirectly provide estimates of crop water status, which is critically important for irrigation management in farms. The review is organised into four main sections: (i) remote sensing platforms; (ii) the remote sensor suite; (iii) techniques adopted for horticultural applications and indicators of water status; and, (iv) case studies of the use of remote sensing in horticultural crops. Finally, the authors’ view is presented with regard to future prospects and research gaps in the estimation of the crop water status for precision irrigation.Deepak Gautam and Vinay Paga