Location: Crop Production Systems Research
2017 Annual Report
Objectives
Objective 1: Develop analytical methods and integrate them into decision support tools for effective aerial application.
Sub-objective 1.1: Verify new drift modeling paradigms with field data; optimize spray delivery systems for drift reduction considering temporal weather differences in statistical analysis.
Sub-objective 1.2: Determine plant injury due to off-target drift by spray sampling, biological measurements, and remote sensing.
Sub-objective 1.3: Determine periods of stable atmosphere favorable for long-distance movement of spray deleterious to susceptible crops downwind from spray application; quantify the effect of surface conditions with weather and incorporate this information into new guidelines for pilots to reduce potential for off-target movement of spray.
Objective 2: Develop laboratory, ground application, and aerial systems for delivery of biological control agents such as non-toxigenic A. flavus for control of mycotoxin and evaluate their effectiveness with bio-assay analysis.
Sub-objective 2.1: Determine field conditions that promote fungal contamination using on-the-go soil sensors and remote sensing; map risk zones for targeted application.
Sub-objective 2.2: Develop aerial application systems to deliver biological control agents and evaluate effectiveness of control with bio-assay analysis.
Objective 3: Develop methodologies that utilize existing remote sensing technologies for user- accessible agricultural aircraft and Unmanned Aerial System (UAS) platforms to detect invasive weeds and wild host plants for insect pests and distinguish between herbicide resistant and non-resistant weeds for use in selective spray management strategies.
Sub-objective 3.1: Identify spectral signatures and classification techniques to distinguish herbicide resistant from non-herbicide resistant weeds; evaluate imaging sensors using the identified signatures and map the distribution of herbicide resistant weeds for selective spraying.
Sub-objective 3.2: Identify spectral bands and classification techniques most useful in discriminating wild host plants of the tarnished plant bug from other land cover features and evaluate airborne imagery acquired to map these plants in and surrounding agricultural fields.
Sub-objective 3.3: Develop accessible remote sensing and rapid image processing systems for targeted application that can be operated by agricultural pilots; develop lightweight remote sensing systems requiring minimal user intervention for Unmanned Aerial Systems (UAS).
Approach
This project seeks to advance application technology through improvements in 1) drift management technologies and models; 2) aerial systems to effectively deliver biological control agents; and 3) remote sensing systems usable by pilots for agricultural aircraft to identify herbicide-damaged plants, invasive weeds, and wild host plants. While drift management is a concern for all pesticide applications, it is of particular concern for aerial applications. The use of herbicide-resistant crop varieties has increased use of glyphosate, both exacerbating the drift problem and giving rise to herbicide resistant weeds that need to be dealt with. Biological control is making headway, but aerial systems are needed to apply these agents. Aerial systems will be developed to effectively deliver liquid formulations of non-toxigenic biological agents to control mycotoxins in corn. Experiments for drift will attempt to reduce confounding of treatment data with environmental effects, preserving statistical precision of the experiments. Specific guidelines for pilots to prevent spraying during temperature inversions will be developed. The deleterious effects of off-target herbicide drift will be detected using spray and biological sampling, and hyperspectral and multispectral remote sensing. Remote sensing will also be used to detect herbicide resistant weeds and wild hosts for plant bug for targeted management. Improvements in remote sensing and rapid image analysis systems will allow accessibility of these systems by agricultural pilots. Autonomous Unmanned Aerial (or ”drone”) platforms will be developed with rapid image analysis capabilities for areas not served by agricultural aircraft. Experiments are also proposed to demonstrate the validity of techniques developed.
Progress Report
The study has been conducted to simulate spray deposit and downwind drift with the AgDisp spray drift model through the design of experiments. On the basis of simulation study field data on low-drift nozzles for different nozzle angles, orifice sizes, spray rates and release altitudes were further collected to validate the simulation.
The study has been conducted to assess soybean, cotton and corn injuries from off-target drift of aerially applied glyphosate by biological response measurements, spray sampling and aerial multispectral remote sensing. The studies were further extended to use of unmanned aerial vehicle (UAV) to detect soybean response to dicamba spray by low-altitude multispectral remote sensing combined with biological response measurement.
A web site has been created for agricultural pilots in Mississippi Delta to have online advice the timing of spray to avoid spray drift caused by temperature inversions. The data used to calculate for the web site were collected from weather stations established in this areas.
First year data analysis was completed for the corn aflatoxin study. For the second year of the study, airborne imagery was collected with the Tetracam multispectral camera system of study sites (an additional field was added to the study). Soil and vegetation zones were established with the data and computer software. Corn ears were sampled based on established soil and vegetation zones and sent to a laboratory for aflatoxin measurements.
Greenhouse and field studies were designed to characterize glyphosate resistance in Johnsongrass using hyperspectral plant sensing in continuing the previous studies on Palmer amaranth and Italian ryegrass. The studies were for rapid differentiation between glyphosate-resistant and glyphosate-sensitive weeds species through proximal hyperspectral remote sensing.
Airborne imagery was collected with the Tetracam multispectral system of areas containing wild host plants. The imagery has been qualitatively assessed.
The dual GoPro camera system was developed and used on air tractor 402B and multirotor UAVs to acquire near-infrared images in addition to true color red-green-blue images to compose aerial color-infrared imagery to indicate crop vigor and weed density and distribution area-wide and crop fields in the research farms. A small digital thermal camera was also used on air tractor and UAVs to detect the temperature variation over the surface of crop canopy in the research farms.
The Micasense RedEdge and Sequoia cameras were set up with GPS and downwelling light sensor to be used on Air Tractor and UAVs. Special aircraft and UAV mounts have been fabricated and installed for the camera system.
Accomplishments
1. Temperature inversion determination for aerial applicators. ARS researchers in Stoneville, Mississippi, set up weather stations throughout the Stoneville, Mississippi area. The data on wind speed, air temperature, and solar intensity measured at these weather stations were transferred wirelessly to a web site in the cloud. A web application was created from the data on the web site to provide online recommendations to aerial applicators in this area when the temperature inversion could occur to allow them to avoid the spray cloud flying out of the treated area. It is important for aerial applicators to work at the time to maximize the precision of spray targeting. The web application with the backend calculation from weather data provides a real-time, user-friendly access for aerial applicators to determine the atmospheric stability conditions at their locations in Mississippi Delta. This project is also funded by Mississippi Soybean Promotion Board (MSPB) and the ARS Principal Investigator, Stoneville, Mississippi, is invited to present in the annual site-visit meeting for MSPB board meeting.
2. Unmanned aerial vehicle (UAV) remote sensing of crop fields. UAV remote sensing significantly helps to improve crop field monitoring for precision agriculture with low cost, flexibility and high-resolution data. ARS researchers in Stoneville, Mississippi, have developed digital color, multispectral, and thermal imaging systems for being mounted on small UAVs. The applications of these systems have included soybean injury from dicamba spray, soybean and cotton plant height estimation, and cotton yield estimation. Our UAV systems could cover any field on the research farm quickly and provide the images in a spatial resolution of a few centimeter. This year we started a new project to uniquely use UAVs to detect naturally grown glyphosate-resistant (GR) and glyphosate-susceptible (GS) weeds in soybean fields with digital color and multispectral cameras at very low altitude of 10 m. The work is to transfer the previous greenhouse and field research results to regular crop fields to detect naturally grown GR and GS weeds to provide as-applied weed maps for site-specific weed management. This project is a collaboration with the Geosystems Research Institute, Mississippi State University and funded by Mississippi Soybean Promotion Board (MSPB) and the ARS Principal Investigator is invited to present in the annual site-visit meeting for MSPB board meeting. Our research drew attention of domestic and international academia, industry and stakeholders and the lead scientist has been invited to talk on international, national and regional meetings.
3. Cotton and Pigweed discrimination with hyperspectral data. To implement strategies to control Palmer amaranth and redroot pigweed infestations in cotton production systems, managers need effective techniques to identify the weeds. Leaf light reflectance measurements have shown promise as a tool to distinguish crops from weeds. Studies have targeted plants with green leaves. Cotton lines exist that have bronze, green, or yellow leaves. ARS researchers in Stoneville, Mississippi, evaluated leaf light reflectance profiles of cotton lines with bronze, green, and yellow leaves, Palmer amaranth, and redroot pigweed. Three regions of the light spectrum were identified (600 to 700 nm, 710 nm, and 1460 nm) for differentiating the cotton lines from Palmer amaranth and redroot pigweed. Ground-based and airborne sensors can be tuned into the regions of spectrum identified, facilitating using remote sensing technology for Palmer amaranth and redroot pigweed identification in cotton production systems.
4. Soybean weed classification tool. Weed management is a major component of a soybean production system; thus, managers need tools to help them distinguish soybean from weeds. An ARS researcher in Stoneville, Mississippi, trained a computer algorithm to differentiate soybean and three broad leaf weeds: Palmer amaranth, redroot pigweed, and velvetleaf. The algorithm uses vegetation indices derived from light reflectance properties of leaves as the input variable for soybean and weed discrimination. The algorithm readily distinguished soybean and velvetleaf from the two pigweeds (Palmer amaranth and redroot pigweed) and from each other with classification accuracies ranging from 93.3% to 100%. Results suggest combining pigweed into one class to improve classification accuracy. Findings support further application of machine learning algorithms and light reflectance properties of plant leaves as tools for soybean and weed discrimination with a potential application of this technology in site-specific weed management programs.
Review Publications
Fletcher, R.S., Reddy, K.N. 2016. Random forest and leaf multispectral reflectance data to differentiate three soybean varieties from two pigweeds. Computers and Electronics in Agriculture. 128:199-206.
Huang, Y., Brand, H., Sui, R., Thomson, S.J., Furukawa, T., Ebelhar, M.W. 2017. Cotton yield estimation using very high-resolution digital images acquired on a low-cost small unmanned aerial vehicle. Transactions of the ASABE. 59(6):1563-1574.
Zhao, F., Dai, X., Verhoef, W., Guo, Y., Tol, C., Li, Y., Huang, Y. 2016. FluorWPS: A Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy. Remote Sensing of Environment. 187:385-399.
Fletcher, R.S. 2016. Using vegetation indices as input into ramdom forest for soybean and weed classification. American Journal of Plant Sciences. 7:2186-2198.
Fletcher, R.S., Reddy, K.N., Turley, R.B. 2016. Spectral discrimination of two pigweeds from cotton with different leaf colors. American Journal of Plant Sciences. 7:2138-2150.
Huang, Y., Ouellet-Plamondon, C.M., Thomson, S.J., Reddy, K.N. 2017. Characterizing downwind drift deposition of aerially applied glyphosate using RbCI as tracer. International Journal of Agricultural and Biological Engineering. 10(3):31-36.
Huang, Y., Thomson, S. 2016. Atmospheric stability determination at different time intervals for determination of aerial application timing. Journal of Biosystems Engineering. 41(4):337-341.
Lu, L., Li, X., Huang, Y., Qin, Y., Huang, H. 2017. Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance. Ecological Modeling. 354:1-10.
Thomson, S.J., Huang, Y., Fritz, B.K. 2017. Atmospheric stability intervals influencing the potential for off-target movement of spray in aerial application. International Journal of Agricultural Science and Technology. 5(1):1-17.
Yang, G., Liu, J., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., Zhang, R. 2017. The application of flexible unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives. Frontiers in Plant Science. 8:1-26.
