Location: Crop Production Systems Research
2019 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
This is the final report for this project; replaced by bridging project 6066-22000-08-00D pending completion of National Program 305 review.
The new drift modeling paradigms with DOE (Design of Experiment) interface and analysis for AGDISP model simulation was validated and verified with field data to optimize spray delivery systems for drift reduction considering temporal weather impact in statistical analysis. The crop injury from simulated off-target drift was determined by plant biological responses and multispectral and hyperspectral remote sensing, especially leaf chlorophyll fluorescence extracted from measured hyperspectrum. The periods of stable atmosphere favorable for long-distance movement of spray deleterious to susceptible crops downwind from spray application was determined, and the surface conditions were quantitively categorized and formulated to provide a guide to reduce potential for such off-target movement of spray. Plant health zones derived with on-the-go soil sensor data and remotely-sensed imagery showed moderate potential for developing aflatoxin risk zones in corn fields. The algorithms of hyperspectral images were developed to identify sensitive bands and differentiate glyphosate-resistant Palmer amaranth and Italian ryegrass. Automated remote sensing systems were developed for agricultural aircraft. Industrial and customer grade UAVs were evaluated and used to carry portable hyperspectral, multispectral, red–green–blue, and thermal cameras to fly over crop fields. Remote sensing was used in a series of experiments over three years to obtain spectral reflectance data for use in studying differences in vegetation indices between grasses, broadleaf plants, and grass/broadleaf plant mixtures. Empirical simulations of selected non-crop winter and spring host plants of tarnished plant bug, Lygus lineolaris, were planted in field-plot experiments. Multispectral reflectance data were aerially acquired with a Real-Time Digital Airborne Camera System (RDACS) sensor and with a Geospatial Systems (DuncanTech) MS-2100 multispectral camera. The following six vegetation indices of spectral reflectance were evaluated in this study: normalized difference vegetation index, ratio vegetation index, green normalized difference vegetation index, green vegetation index, green ratio vegetation index, and Ashburn vegetation index. Vegetation indices calculated with imagery data for the grasses and broadleaves differed significantly; there appeared to be more discriminating differences between vegetation indices for grasses and broadleaf plants when the indices were based on green and near infrared or green and red spectral bands than when the indices were based on red and near infrared spectral bands. Insect data from these studies confirm that tarnished plant bug prefers broadleaf host plants but can use Italian ryegrass for food and reproduction. The narrow temporal window of host suitability for Italian ryegrass may limit its significance. Herbicide destruction of broadleaf host plants in early spring prevented the tarnished plant bug population increases that occurred in untreated plots. Palmer amaranth, redroot pigweed, and velvetleaf are major weeds infesting cotton, soybean, and corn production systems. They also serve as an alternate food source to insects such as the tarnished plant bug. In multiple experiments, it was demonstrated that computer algorithms could use multispectral and hyperspectral data to differentiate Palmer amaranth, redroot pigweed, and velvetleaf from soybean and cotton. Additionally, the optimal region of the light spectrum was identified for weed-crop discrimination.
Accomplishments
1. Web online service of temperature inversion determination for aerial applicators. Last year we reported that ARS researchers in Stoneville, Mississippi, created a web application from the meteorological data measured at the weather stations deployed by the Delta Research Center of Mississippi State University over the area of the Mississippi Delta 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. This year, to provide more accurate recommendation locally ARS scientists at Stoneville, Mississippi, several portable weather stations were further assembled and deployed at the representative locations in the Stoneville, Mississippi, area. These weather stations were made and implemented with open-source hardware and software to measure meteorological data every 15 minutes. The web application has been created accordingly to provide an online guide for local area applicators to avoid drift caused by temperature inversion.
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 continued to develop and apply digital color, multispectral, hyperspectral and thermal imaging systems for being mounted on small UAVs for assessing crop injury from dicamba, differentiating naturally-occurring glyphosate-resistant (GR) and glyphosate-susceptible (GS) weeds in crop fields, and crop phenotyping for rice breeding. Our UAV systems could cover any field on the research farms quickly and provide the images in a spatial resolution of a few centimeters or millimeters.
3. Differentiating palmer amaranth from okra and super-okra leaf cotton based on their canopy light reflectance properties. Various cultivars of cotton are grown in the southern United States. Palmer amaranth is a major weed problem of cotton production systems in this region. Sensors measuring the light reflectance properties of plants have shown potential as tools for differentiating crops from weeds. ARS researchers in Stoneville, Mississippi, identified optimal wavelengths to use for differentiating Palmer amaranth and super-okra leaf cotton (2000 nm, 2180 nm). Canopy light reflectance results were not consistent for Palmer amaranth and okra leaf cotton differentiation. Commercially available sensors can be tuned to the optimal bands identified in this study, facilitating application of remote sensing technology for Palmer amaranth discrimination from super-okra leaf cotton and implementation of the technology as a decision support tool in weed management programs.
Review Publications
Huang, Y., Brown, M. 2018. Advancing to the next generation precision agriculture. In Serraj, R., Pingali, P., editors. Agriculture and Food Systems to 2050 – Global Trends, Challenges and Opportunities. Singapore, Phillipines: World Scientific. p. 285-314. https://doi.org/10.1142/11212.
Yao, H., Huang, Y., Tang, L., Tian, L., Bhatnagar, D., Cleveland, T.E. 2018. Using hyperspectral data in precision farming applications. In: Thenkabail, P.S., Lyon, J.G., editors. Hyperspectral Remote Sensing of Vegetation. 1st Edition. Boca Raton, Florida: CRC Press. https://doi.org/10.1201/b11222.
Zhao, F., Li, R., Verhoef, W., Cogliati, S., Liu, X., Huang, Y., Guo, Y. 2018. Reconstruction of the full spectrum of solar-induced chlorophyll fluorescence: Intercomparison study for a novel method. Remote Sensing of Environment. 219:233-246.
Wen, Y., Zhang, R., Chen, L., Huang, Y., Yi, T., Xu, G. 2019. A new spray deposition pattern measurement system based on spectral analysis of a fluorescent tracer. Computers and Electronics in Agriculture. 160:14-22.
Belabid, N., Zhao, F., Brocca, L., Huang, Y., Tan, Y. 2019. Near-real-time flood forecasting based on satellite precipitation products. Remote Sensing. 11(252):1-18. https://doi.org/10.3390/rs11030252.
Fletcher, R.S., Turley, R.B. 2018. Comparing canopy hyperspectral reflectance properties of Palmer amaranth to okra and super-okra leaf cotton. American Journal of Plant Sciences. 9(13):2708-2718.
Fletcher, R.S., Fisher, D.K. 2018. A miniature integrated sensor for measuring reflectance, relative humidity, and temperature: A greenhouse example. Agricultural Sciences. 9(11):1516-1527.
