Haly L. Neely

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Multispectral and DSLR sensors for assessing crop stress in corn and cotton using fixed-wing unmanned air systems

As small unmanned aircraft systems become increasingly affordable, reliable, and formally recognized under federal regulation, they become increasingly attractive as novel platforms for civil applications. This paper details the development and demonstration of fixed-wing unmanned aircraft systems for precision agriculture tasks. Tasks such as soil moisture content and high throughput phenotyping are considered. Rationale for sensor, vehicle, and ground equipment selections are provided, in addition to developed flight operation procedures for minimal numbers of crew. Preliminary imagery results are presented and analyzed, and these results demonstrate that fixed-wing unmanned aircraft systems modified to carry non-traditional sensors at extended endurance durations can provide high quality data that is usable for serious scientific analysis.

High clearance phenotyping systems for season-long measurement of corn, sorghum and other row crops to complement unmanned aerial vehicle systems

The next generation of plant breeding progress requires accurately estimating plant growth and development parameters to be made over routine intervals within large field experiments. Hand measurements are laborious and time consuming and the most promising tools under development are sensors carried by ground vehicles or unmanned aerial vehicles, with each specific vehicle having unique limitations. Previously available ground vehicles have primarily been restricted to monitoring shorter crops or early growth in corn and sorghum, since plants taller than a meter could be damaged by a tractor or spray rig passing over them. Here we have designed two and already constructed one of these self-propelled ground vehicles with adjustable heights that can clear mature corn and sorghum without damage (over three meters of clearance), which will work for shorter row crops as well. In addition to regular RGB image capture, sensor suites are incorporated to estimate plant height, vegetation indices, canopy temperature and photosynthetically active solar radiation, all referenced using RTK GPS to individual plots. These ground vehicles will be useful to validate data collected from unmanned aerial vehicles and support hand measurements taken on plots.

Spatial analysis of multispectral and thermal imagery from multiple platforms

Airborne and satellite remote sensing can potentially be used to model crop characteristics. However, satellite imagery usually exhibit low spatial and temporal resolutions, and manned aircraft imagery, despite improved resolutions, is not cost-effective. Recent developments in UAV remote sensing have allowed for imagery at improved spatial resolutions relative to satellites and at a fraction of the cost relative to manned aircraft. Furthermore, UAVs offer potential advantages over proximal soil sensors (i.e. EM-38) in terms of in-season decision making. However, it is unclear at this point whether these benefits translate to higher quality information. This question has relevance within fields that exhibit contrasting environments, such as soil spatial variability. Therefore, the objectives of this paper were twofold: 1) to quantify improvements in UAV-based plant (cotton) modelling relative to proximal sensing (i.e. EM-38), manned aircraft, and satellites (Landsat 8); and 2) to determine how such modeling can be affected by soil spatial variability. Results indicate that UAVs show higher nugget/sill ratios and larger ranges than manned aircraft and satellites. These results have implications for predicting agronomic variables (i.e. yield, plant height), as well as soil/plant sampling.

Strategies for soil-based precision agriculture in cotton

The goal of precision agriculture is to increase crop yield while maximizing the use efficiency of farm resources. In this application, UAV-based systems are presenting agricultural researchers with an opportunity to study crop response to environmental and management factors in real-time without disturbing the crop. The spatial variability soil properties, which drive crop yield and quality, cannot be changed and thus keen agronomic choices with soil variability in mind have the potential to increase profits. Additionally, measuring crop stress over time and in response to management and environmental conditions may enable agronomists and plant breeders to make more informed decisions about variety selection than the traditional end-of-season yield and quality measurements. In a previous study, seed-cotton yield was measured over 4 years and compared with soil variability as mapped by a proximal soil sensor. It was found that soil properties had a significant effect on seed-cotton yield and the effect was not consistent across years due to different precipitation conditions. However, when seed-cotton yield was compared to the normalized difference vegetation index (NDVI), as measured using a multispectral camera from a UAV, predictions improved. Further improvement was seen when soil-only pixels were removed from the analysis. On-going studies are using UAV-based data to uncover the thresholds for stress and yield potential. Long-term goals of this research include detecting stress before yield is reduced and selecting better adapted varieties.