Advances in Agronomy, Vol. 76
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Your download advances was a URL that this research could well Help. Your production moved a Intensity that this training could not find. Advances in battery life for rotary-wing air vehicles to enable greater coverage, and advances in cameras—faster frame rate, shorter exposure time, and higher resolution—for fixed-wing air vehicles are needed. Remote sensing is effective for estimating crop biophysical properties [ 41 — 43 ], and data from multispectral cameras are commonly converted to descriptive indices like normalized difference vegetation index NDVI to estimate crop characteristics from remote sensing data.
The objective of this case study was to investigate the use of UAV remote sensing data for developing empirical models to estimate leaf area index LAI and percent canopy cover of wheat. Flights were conducted over a variety trial of winter wheat Triticum aestivum L on January 23 and February 26, , with the Sentek sensor on the Anaconda fixed-wing UAV. The field contained a large number of 1. Four 2. Reflectance values for the pixels representing each plot were averaged to calculate NDVI Fig 11 , which was compared to ground truth data.
Ground-truth canopy cover was estimated from photographs of selected plots collected with a digital camera mounted on a handheld pole looking downward at the crop. These strong relationships suggest that UAV remote sensing data can be used to provide reasonably accurate estimates of LAI and percent canopy cover. A drawback to the empirical models used in this study is that they tend to be site-specific and applicable only to similar situations [ 44 ].
However, they can readily provide a ranking of plots on an arbitrary scale, which would often be adequate for the scientific situation at hand; i. B Correlation between wheat ground cover estimated on the ground and NDVI calculated from aerial imagery. Soil variability contributes to high levels of variability in plant environments within most agricultural fields.
Soil properties such as clay content are strong drivers of crop growth and set the maximum yield potential through nutrient and water storage capability. Ground-based proximal sensing to estimate soil apparent electrical conductivity EC a has become common for mapping soil properties in the field Corwin and Lesch, Maps of soil variability are valuable to growers in establishing management zones, but these tend to cover broad areas and thus lack detailed data on differences in local environments. Other factors also affect plant environments in the field, like rainfall distribution and intensity.
Data that characterize crop canopy variation during the growing season, like remotely sensed vegetation indices, integrate soil and weather conditions into a spatiotemporal representation of plant response to local environment. The end-of-season representation of plant response to local environment is yield. Thus it is important to consider whether UAV images collected during the growing season can indicate environmental crop stresses that will be borne out in a yield map collected at harvest. If growers were able to link crop stress to environmental factors like soil type, they could potentially develop beneficial site-specific management practices such as variable-rate irrigation.
Thus the objectives of this work were 1 to evaluate the relationship between end-of-season crop yield—cotton in this case—and during-season UAV vegetative indices, and 2 to consider how soil properties relate to the expression of environmental crop stress. A single cotton variety Phytogen was planted on a 30 ha field. A survey of soil EC a was conducted with an EM38 Geonics Limited, Mississauga, Ontario, Canada instrument when the soil was near field capacity, and the EC a data were classified with K-means clustering to segment the field into predominately sandy, loamy, and clayey textures Fig 13b.
Within each textual group, plant height, stand count and seed-cotton yield were measured for ground truth data. B Apparent electrical conductivity EC a map of the soil. A set of images was collected on August 26, , a month before harvest, with the Sentek camera onboard the Anaconda fixed-wing UAV. UAV images with 6. A wet spring resulted in flooding early in the season that led to erosion and ponding after planting and ultimately low stand counts, and later in the season during peak transpiration, little rainfall occurred, reducing yield in the sandier areas.
These damaged areas are indicated as bare soil in Fig 13a. Medium textured i. While the experiment produced only moderate correlations, it did provide an indication that UAV images can estimate crop stress during the season, and they provide a means to consider the effect of soil variability on local plant environment. Weed management is a constant challenge that growers face each year. Particularly important information includes dominant weed species, weed size, crop growth stage, etc. Routine field scouting is important to identify site-specific weed issues and take appropriate control measures, but it is tedious and expensive because large areas need to be covered in a short time, and entering fields cannot be done under wet weather conditions.
In rice production, field scouting is even more challenging because long-term flooding can be part of the growth regime. However, UAVs may provide practical solutions to field scouting for weeds.
The objective of this case study was to evaluate UAV remote sensing data for assessment of mid-season weed infestations prior to herbicide application. Various weed control treatments were applied in a grain sorghum field planted on June 12, The dominant weed species included Palmer amaranth Amaranthus palmeri , barnyardgrass Echinochloa crus-galli , Texas panicum Panicum texanum , and morningglory Ipomoea spp.
Six herbicide treatments with four replications were used along with a non-treated check. Each plot measured 4 m by 8 m and consisted of four crop rows. Images Fig 15a were captured with the true-color camera onboard the Lancaster fixed-wing air vehicle, which flew at m AGL on September 3, Ground truth was in the form of visual weed control assessments by an expert made on a scale of 0 to , with 0 representing no weed suppression compared to the non-treated check and representing complete control weed-free plot.
The Excess Green Index ExG; [ 48 ] was calculated from the mosaicked image to enhance spectral differences between the vegetation and soil, followed by a K-means classification Fig 15b. Similar-size regions of interest were created for each plot, including three treated rows and the spaces between them. The ratio of the total count of classified vegetation pixels to the total count of classified soil pixels was calculated for each region of interest as an estimate of weed infestation severity.
The sorghum canopy was assumed to be uniform across treatments, and variation in vegetation pixel counts was thus assumed to be caused by weed infestation differences. A Aerial image mosaic of a weed management experiment with 28 plots. B Classification of soil brown and vegetation green. C Comparison between estimated weed control from aerial imagery and ground truth weed control of each treatment.
D Correlation between estimated weed control from aerial imagery and ground truth weed control based on 28 observations. One difficulty in differentiating weed from crop was the classification of pixels as exclusively plant or soil material. Improvements in this regard could be approached by increasing image resolution with an improved camera detector, reducing motion blur by slowing the air vehicle or reducing the camera exposure time, or using the spectral features of pixels to determine relative contributions of soil and plant i.
Another difficulty was the lack of weed volume information from the two-dimensional aerial image. Weed scientists usually make application decisions according to several factors including weed coverage and volume. Adding 3D surface model information could be helpful in this regard. The first year of this project has enabled a large team of researchers to develop a system of equipment and standard processes for collecting and rapidly analyzing high-quality, low-altitude, aerial images from UAVs over a large research farm. The interdisciplinary nature of the project, while critical to its success, introduced challenges associated with developing shared language and goals.
The project demonstrated that integrating UAVs and sensors in such a context is challenging and requires a great deal of planning and coordination, but by the end of the first season, the turnaround time on mosaicked images approached 48 h. Analyses have been conducted for high-throughput phenotyping and agronomic purposes, with some measure of success. Many breeding and agronomic research programs were involved in this project, and four specific case studies were highlighted. It would be easy to think that UAV images are merely higher spatial resolution mm to cm remotely sensed images, but image data from UAVs must be handled in a different manner than traditional remote sensing data at m-scale resolutions.
At such low coverages there is a need to mosaick images together to cover a broader area, and at such high spatial resolutions there is a need to ensure against blurred pixels due to motion. Certain critical sensor criteria are required for providing high-quality image mosaics at high resolution. Less straightforward is the fact that minimum exposure time limits the actual GSD along the flight path, because at small GSD the air vehicle speed may cover the pixel distance in less time than the camera requires for exposure.
Also, camera frame rate along with air vehicle speed limits the amount of forward overlap, which is essential to creating good mosaics.
Agriculture and the environment - OECD
Thus, an ideal UAV camera has high resolution, high frame rate, and short exposure time. There are also critical air vehicle criteria for providing good images and mosaics.
It is necessary that images be captured as close to on-nadir as possible. This situation can be achieved by some combination of a stable air vehicle and a camera gimbal to keep the camera pointed down regardless of air vehicle attitude, but gimbals are not common in small UAS, although one was used on the rotary-wing air vehicle used in this project. Without a gimbal, it is important that the air vehicle remain as level as possible during image capture.
Otherwise, geographic registration accuracy in the mosaicking process suffers, and DSM calculations may have unacceptable errors as well. Another safeguard against images that are far off-nadir is to use IMU data to screen out images taken when the air vehicle experienced unacceptable roll, pitch, or yaw.
Ideally such a screening process could be automated in software for image pre-processing. Finally, it is common to find at the end of a flying mission that the image overlap was not adequate in certain areas covered. A real-time, during-flight, calculation of the need to re-visit part of the flight coverage area would minimize the possibility of poor mosaics caused by inadequate image overlap.
Certain innovative methods were developed and used in this project.
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Historically, radiometric calibration has involved laying out on a field a large tarp with varying shades of known reflectance values. The image pixels associated with this target are used to convert pixel values to reflectance values throughout the image. In UAV remote sensing, many images are typically used to make up one large image mosaic. Therefore, it is inadequate to have one small calibration target in a large field that may be imaged over the course of a min. Additionally, the objective of this project involved making numerous flights over the same field, so laying out targets repeatedly, particularly when they are also used as GCPs requiring GPS data to be collected, is not a viable option.
Thus semi-permanent tiles were installed throughout each route pack, and the tiles are measured regularly for changes in spectral reflectance. The regulatory requirement in the U. A recent change to FAA regulations requires operators to register the UAVs being flown, in addition to requiring flight training. Project participants need regular updates to keep up with the current regulatory status. This project boasts a large number of faculty, but it also involves numerous graduate students and postdoctoral scholars who will be among the first generation of scientists to use the approaches developed in decision making processes.
Developing appropriate training programs for this multi-disciplinary work is critical. As this project continues into its second year, objectives will transition somewhat from developing flying and data collection protocols to experimenting with new sensors and developing analysis methods that facilitate real world decisions in breeding and agronomics.
As goals and aims evolve and grow larger, the importance of developing a shared language among engineers, data processers, and field scientists will continue to be critical to success. Analyzing image data in a way that provides actionable information will be a key goal.
One aspect of this involves developing process-based linkages between soil and crop response. Ultimately all the agronomic technologies should be transitioned to decision-support applications for growers and their service providers. With that in mind, the technology and processes can be daunting, so early adopters will have substantial challenges to overcome. The technology must be transferred to the user community in practical ways. Finally, many image analysis tasks are time consuming, so writing code to automate repetitive processes is a major need in upcoming work.
For example, plant breeders working on this project had 38, plots in that had to be delineated by manually drawing polygons on image mosaics, one of the most time consuming activities in the project. These plots were typically adjacent to one another, and plot-level data had to be extracted from them, requiring coordination between data processing experts and field researchers who know where their plots are and the exact regions of interest.
Prototype software was developed to automate identification of individual plots as well as radiometric calibration of images, and this software will be further tested in the growing season. The software is also being designed to assist field scientists in developing image-based metrics like leaf-area index, above ground biomass, and plant height. This software should dramatically increase throughput and decrease error in future studies. Joshua A.
Harris of Aerospace Engineering and Brandon Hartley of Biological and Agricultural Engineering provided indispensable help on system integration and field implementation. Farm manager Alfred Nelson created and maintained air strips, provided agronomic support for the larger field trials, and coordinated between the project and other farm activities. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract 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.
Introduction Demand for Increased Agricultural Productivity To address the food and fiber needs of a world population increasing from 7. Download: PPT. Sensing Technology UAVs are commonly outfitted with customizable sensor payloads for agricultural data collection. Necessity of Interdisciplinary Collaboration UAV-based remote sensing in agriculture has recently become an active research specialization with demonstrated success in a few targeted projects, but an operational framework for routine collection of this kind of data in close partnership with field researchers has not been extensively investigated.
Fig 1. Gartner hype cycle cartoon of the subjective value and development stage of various technologies discussed here. Objectives The goals of this project were 1 to provide quick-turnaround, high-resolution, high-quality image data to a diverse group of agricultural researchers across numerous fields and plots on a large research farm; 2 to establish the workflow of data collection and processing as well as communication and coordination between experts that are required for such an endeavor; and 3 to develop methods for plant breeders and agronomists to incorporate UAS collected remote sensing data to improve their results and decision making ability.
Materials and Methods Interdisciplinary Teams The overall research group consisted of five teams Fig 2 and roughly 40 scientists and engineers. Fig 2. Fig 3. Workflow to start a UAV project for phenotyping and agronomic research, from interdisciplinary team establishment to decision making. Table 2. Fig 5. External physical characteristics of UAVs used in this study. Table 3. Specifications and configurations of the three UAVs used in this study. Flight and Sensor Configuration Most flights were made within 2. Fig 7. Relationship between flight and sensor parameters used to determine optimal flight and sensor configurations before flights use Anaconda fixed-wing and Sentek multispectral camera as an example here.
Fig 8. Three different flight paths over a ha route pack evaluated by Anaconda fixed-wing UAV in this study. Geographic Registration and Radiometric Correction Ground control points GCPs are critical for geographic registration of images during mosaicking and when overlaying images collected at different times. Data Pre-processing After a flight, pre-processing was implemented on the raw image data for 1 image correction and format conversion with proprietary software for a specific sensor; 2 ortho-mosaicking with Pix4Dmapper; 3 secondary band registration, if necessary; 4 radiometric calibration to convert pixel values from digital numbers to reflectance; and 5 brightness adjustment to alleviate cloud shadow effect if necessary.
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