Location: Crop Production Systems Research
2016 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 and downwind drift were simulated with the AgDisp spray drift model through the design of experiments. The results were organized to compare with results from field studies in different nozzle/booms systems with variable wind conditions. The simulation indicated that the impact factors could be feasibly optimized to enhance the function of controllable (system) factors to counter the uncontrollable (weather) factor, which is valuable in guiding practical application operations.
Soybean, cotton and corn injuries from off-target drift of aerially applied glyphosate have been evaluated by biological response measurements, spray sampling and aerial multispectral remote sensing. The results indicated that the data from biological responses, spray sampling and remote sensing were highly correlated and remote sensing variables and features could be a good surrogate of biological responses with spray deposit quantification to provide a rapid and cost-effective method to detect crop herbicide injury in a relatively large area.
The calculation logic of the decision rules has been established in Mississippi Delta for agricultural pilots to avoid spraying during temperature inversions and it is being incorporated into the backend of a web-based system with the meteorological data from Mississippi State University for farm managers and agricultural pilots to access to decide the timing of aerial application.
Soil data and airborne imagery was collected with an on-the-go soil system and multispectral camera system, respectively, at the corn aflatoxin study site. 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 studied were designed to characterize glyphosate resistance in Italian ryegrass using hyperspectral plant sensing. The greenhouse study was for determination of effective dose to mitigate the weed’s biomass by 50% (ED50) through hyperspectral imaging. The field study was for rapid differentiation between glyphosate-resistant and glyphosate-sensitive Italian ryegrass through proximal hyperspectral remote sensing.
Ground-based light reflectance measurements of plants serving as host for the tarnished plant bug were collected with a ground-based spectroradiometer. Also, light reflectance measurements were acquired from other plants, soil, and man-made features within the study area. Data have been qualitatively analyzed; spectral bands have been identified for distinguishing between plants of interest and other land-cover features.
An innovative dual GoPro camera system was developed for unmanned aerial vehicle (UAV) to acquire near-infrared images in addition to true color RGB images to compose aerial color-infrared imagery. This new system was tested successfully for low-altitude remote sensing over crop canopy in our research farms. A new, small digital thermal camera was also developed and tested successfully for UAV to sense the temperature variation over the surface of crop canopy in our research farms.
The Tetracam mini camera system was upgraded to the Tetracam micro system. Spectral sensitivity of the new system has been tested in a greenhouse study. A camera mount and a image acquisition trigger have been developed to use the system in an agricultural aircraft. Also, a specialized aircraft mount has been fabricated and tested for the camera system light correction apparatus.
Accomplishments
1. Temperature inversion determination for aerial applicators. The decision rules have been validated and established for Mississippi Delta to provide recommendations to aerial applicators when the temperature inversion could occur to allow them to avoid the spray cloud flying out of the treated area. Researchers at USDA-ARS, Crop Production Systems Research Unit, Stoneville, Mississippi, collaborated with researchers at Mississippi State University to demonstrate that the determination of the likelihood of temperature inversion. 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 real time weather data provides a 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 PI is invited to present in the annual site-visit meeting for MSPB board meeting.
2. Hyperspectral remote sensing (HRS) of glyphosate-resistant (GR) and glyphosate-susceptible (GS) weeds. The ability to detect GR and GS weeds with HRS technologies should facilitate the site-specific weed management by identifying the GR and GS weed populations in crop fields. Scientists at USDA-ARS, Crop Production Systems Research Unit, Stoneville, Mississippi, conducted greenhouse and field studies of Palmer Amaranth and Italian Ryegrass using hyperspectral imager and handheld spectroradiometer. The results indicated that based on spectral signatures GR and GS Palmer Amaranth and Italian Ryegrass could be differentiated with high accuracies. The accuracies of GR and GS Palmer Amaranth classification were higher than 90% while the accuracies for Italian Ryegrass were in the range from 70% to 80%. The challenge remained is to integrate consistent sensitive bands for sensor design to develop a portable device for farmers to use in fields. This project is funded by Mississippi Soybean Promotion Board (MSPB) and the PI is invited to present in the annual site-visit meeting for MSPB board meeting.
3. Unmanned aerial vehicle (UAV) remote sensing of crop fields. UAV remote sensing has significantly helped to improve crop field monitoring for precision agriculture with low cost, flexibility and high-resolution data. Scientists at USDA-ARS, Crop Production Systems Research Unit, Stoneville, Mississippi, have developed digital color, multispectral, and thermal imaging systems for being mounted on small UAVs. The applications of these systems include soybean injury from dicamba spray, soybean and cotton plant height estimation, and cotton yield estimation. Our UAV systems could cover any field in the research farm quickly and provide the images in a spatial resolution of a few centimeter. Our research drew attention domestically and internationally from academia, industry and stakeholders and the lead scientist has been invited to talk on international, national and regional meetings. The collaboration with the Geosystems Research Institute, Mississippi State University, has been established to acquire UAV images over our research field to scale up with the satellite observations.
4. Soybean Velvetleaf Discrimination Tool. Velvetleaf infestations negatively impact row crop production throughout the United States. To implement management strategies to control velvetleaf, managers need tools for differentiating it from crop plants. An ARS scientist in the Crop Production System Research Unit, Stoneville, Mississippi used leaf light reflectance data and a computer learning algorithm referred to as random forest to distinguish soybean from velvetleaf. Accuracies greater than 86% were achieved with the data and computer algorithm. Findings support further application of the random forest machine learner along with remotely-sensed data as tools for velvetleaf soybean discrimination with future implications for site-specific management of velvetleaf.
An ARS scientist in the Crop Production System Research Unit, Stoneville, MS participated in a Ag Science Day held at Alcorn State University, a historically black college and university (HBCU). Scientist discussed crop and weed discrimination based on their leaf light reflectance properties and conducted a hands-on activity focusing on qualitative assessment of airborne imagery of agricultural fields. Faculty, staff, college and high school students, and state and federal government personnel attended the Ag Science Day. Scientist participation in the Ag Science Day meets ARS initiative to work with underserved communities.
An ARS scientist in the Crop Production System Research Unit, Stoneville, MS gave a lab tour to faculty, staff, and students of Mississippi Valley State University (HBCU). Scientist discussed using an on-the-go soil system to develop maps showing spatial variability of soil properties within fields and employing ground-based light measurements to distinguish crops from weeds. Lab tour meets ARS initiative to work with underserved communities.
Review Publications
Zhao, F., Guo, Y., Huang, Y., Zhao, H., Liu, G. 2015. Quantitative estimation of the fluorescent parameters for crop leaves with the Bayesian inversion. Remote Sensing. 7:14179-14199.
Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., Tian, L., Wu, W. 2015. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology. 216:188-202.
Huang, Y., Lee, M.A., Thomson, S.J., Reddy, K.N. 2016. Ground-based hyperspectral remote sensing for weed management in crop production. International Journal of Agricultural and Biological Engineering. 9(2):98-109.
Deng, W., Huang, Y., Zhao, C., Wang, X. 2015. Identification of seedling cabbages and weeds using hyperspectral imaging. International Journal of Agricultural and Biological Engineering. 8(5):65-72.
Fletcher, R.S. 2015. Testing leaf multispectral reflectance data as input into random forest to differentiate velvetleaf from soybean. American Journal of Plant Sciences. 6:3193-3204.
Zhang, J., Huang, Y., Yuan, L., Yang, G., Chen, L., Zhao, C. 2016. Using satellite multispectral imagery for damage mapping armyworm (Spodoptera frugiperda) in maize damage at a regional scale. Pest Management Science. 72:335-348.
