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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Research Project #427340

Research Project: Application Technologies to Improve the Effectiveness of Chemical and Biological Crop Protection Materials

Location: Crop Production Systems Research

2015 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
Spray deposit results from the AgDisp spray drift model were compared with results from a field study characterized by low-variable wind. Mass accountancy showed that the AgDisp model overpredicted spray deposit by a factor of two. This was attributed to limitations in spray collector efficiency, which is now being quantified under controlled conditions. Decision rules for agricultural pilots to avoid spraying during temperature inversions are being incorporated into a web-based system that can be accessed by farm managers and agricultural pilots. Aerial spray nozzles were evaluated for uniform application of non-toxigenic Aspergillis flavus fungus, for biological control of mycotoxins. A hyperspectral imaging system (HIS) has been used for two greenhouse studies. Results indicated that soybean injury from glyphosate could be detected within 24 hours after application using a modified first-derivative analysis method. A greenhouse study was designed to characterize glyphosate resistance in Palmer amaranth and Italian ryegrass weeds using hyperspectral plant sensing, for determination of effective dose to mitigate each weed’s biomass by 50% (ED50). A field study was designed for rapid differentiation between glyphosate-resistant and glyphosate-sensitive weeds using proximal remote sensing. The new generation miniature multispectral remote sensing camera was mounted on a multi-rotor unmanned aerial vehicle (UAV) with newly integrated gimbal and GPS mission control. This system was tested successfully for fully autonomous flight to remotely sense our crop fields.


Accomplishments
1. Remote sensing of selected winter and spring host plants of tarnished plant bug. The ability to detect wild host plant areas with geo-spatial technologies should facilitate the site-specific application of vegetation management strategies to reduce tarnished plant bug populations before the cropping season begins. Researchers with USDA, ARS, Crop Production Systems Research Unit, Stoneville, MS, collaborated with researchers at Austin Peay State and Mississippi State Universities to demonstrate that tarnished plant bug prefers broadleaf plants as host plants and that aerial imagery has potential as a tool to generally classify vegetation into broadly defined categories such as annual grasses, grass/broadleaf mixtures, and annual broadleaf groups. The broadleaf groups could be targeted for spray applications. Findings will be useful in site-specific vegetation management decision programs such as prototype area-wide tarnished plant bug management experiments conducted in the Delta region of Mississippi.


Review Publications
Fletcher, R.S., Smith, J.R., Mengistu, A., Ray, J.D. 2014. Relationships between microsclerotia content and hyperspectral reflectance data in soybean tissue infected by Macrophomina phaseolina. American Journal of Plant Sciences. 5:3737-3744.
Huang, J., Tian, L., Liang, S., Becker-Reshef, I., Huang, Y., Su, W., Fan, J., Wu, W. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology. 204:106-221.
Sudbrink, D.L., Thomson, S.J., Fletcher, R.S., Harris, A., English, P.J., Robbins, J.T. 2015. Remote sensing of selected winter and spring host plants of tarnished plant bug (Heteroptera: Miridae) and herbicide use strategies as a management tactic. American Journal of Plant Sciences. 6:1313-1327.
Huang, Y. and Thomson, S.J. 2015. Remote sensing for cotton farming. In: Cotton, 2nd edition, Eds. D. D. Fang and R.G. Percy. American Society of Agronomy, Inc., Crop Science Society of America, and Soil Society of America, Inc. Madison, WI, USA, Agronomy Monograph 57:1-26.
Huang, Y., Reddy, K.N., Thomson, S.J., Yao, H. 2015. Assessment of soybean injury from glyphosate using airborne multispectral remote sensing. Pesticide Management Science. 71:545-552.
Zhao, F., Guo, Y., Huang, Y., Reddy, K.N., Zhao, Y., Molin, W.T. 2015. Detection of the onset of glyphosate-induced soybean plant injury through chlorophyll fluorescence signal extraction and measurement. Journal of Applied Remote Sensing (JARS). 9(1):1-12.
Huang, Y., Hoffmann, W.C., Lan, Y., Fritz, B.K., Thomson, S.J. 2014. Development of a low-volume sprayer for an unmanned autonomous helicopter. Journal of Agricultural Science. 7(1):148-153.
Zhao, F., Li, Y., Dai, X., Verhoef, W., Guo, Y., Shang, H., Gu, X., Huang, Y. 2014. The impact of sensor field of view and distance on field measurements of directional reflectance factors: a simulation study for row crops. Remote Sensing of Environment. 156:129-142.
Reddy, K.N., Huang, Y., Lee, M.A., Nandula, V.K., Fletcher, R.S., Thomson, S.J., Zhao, F. 2014. Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S Wats.): hyperspectral reflectance properties of plants and potential for classification. Pest Management Science. 70:1910-1917.