Location: Crop Production Systems Research
2024 Annual Report
Objectives
1. Develop herbicide and pesticide application technologies with improved spray drift models and drift management technologies.
1.1. Evaluate spray deposits and off-target spray drift using measurement and analysis protocols that account for environmental effects and treatment effects of aerial spray application from agricultural aircraft that can serve a dual role, applying pesticides to agricultural fields and functioning as a platform for camera systems to obtain imagery of agriculture fields.
1.2. Create and evaluate drift models to detect herbicide injury in crop production systems and optimize drift models with field sampling, remote sensing, and meteorological data to reduce drift and minimize crop injury.
2. Develop or adopt remote sensing methods and systems to develop risk management zones, identify spraying zones, and monitor spraying drift and effect.
2.1. Develop remote sensing systems based on an unmanned aerial vehicle with the capability of rapid image processing for targeted spray applications to herbicide-resistant weeds.
2.2. Assemble an unmanned aerial vehicle that has the capabilities of dual use, aerial spray application and image acquisition, to complete the following tasks:
(1) test the system to determine how environmental factors affect spray deposits and herbicide drift prior to planting and post planting in a crop production system, (2) evaluate the camera system for developing preplant and post plant site-specific weed management zones to use for herbicide management, and (3) test the camera system to determine the efficacy of the herbicides applied.
2.3. Evaluate pan-sharpened high-resolution satellite imagery for establishing plant health zones and zone susceptibility to herbicide damage in crop fields.
3. Create and enhance internet mobile platform-based data service to assist applicators, consultants, and farmers to make site-specific farm operation decisions.
3.1. Develop a method to complete the following tasks or imagery to be used in a web-based mobile platform: (1) radiometrically correct imagery from multiple imaging sources (i.e., space-borne, airborne, and UAV imagery) and make them comparable to each other radiometrically, (2) optimize the images to scales appropriate for field observation, (3) overlay risk management and spraying zones derived from Sub-objectives 2.2 and 2.3 on the imagery, and (4) integrate images acquired from the different sources into a global data cube with unified spatial, spectral, and temporal dimensions.
3.2. Develop a web-based mobile application to be accessed by users for spray timing during the day, for most recent field conditions, and for historical field data and images; analyses and reported data will be on meteorological observations and field and radiometrically corrected crop remote sensing data obtained with Red-Green-Blue (RGB), multispectral (RGB, Red-Edge and Near Infrared (NIR)), hyperspectral (Visible and NIR (VNIR)), and thermal imaging sensors.
Approach
This project seeks to 1) improve spray drift models and develop drift management technologies used to minimize crop injury caused by aerial spray applications, 2) develop remote sensing systems and methods for spray zone identification and crop field monitoring, and 3) create an internet mobile platform-based data service to assist site-specific farming decisions. Drift management is a concern for all pesticide applications, particularly for aerial applications. The use of herbicide-resistant (HR) crop varieties has increased the use of herbicides, exacerbating the drift problem and giving rise to HR weeds that need to be identified and controlled. Agricultural societies need more information on the role that remote sensing can play in assessing drift and its damage to crops and the best way to process imagery in a timely but cost-effective manner. The internet and apps are the gateways for obtaining and sharing information. The void on internet-based mobile platforms that producers and consultants can use as a decision support tool for precision agriculture needs to be addressed. Experiments for spray deposition and drift will be conducted along with field imaging to attempt to reduce confounding of treatment data with environmental effects, preserving statistical precision of the experiments. The drift models for crop injury assessment will be created and verified. Advancements in remote sensing and rapid image analysis systems will minimize accessibility of these systems by agricultural pilots. Protocols will be developed to create risk management zones by identifying spray zones and by monitoring spray drift and effect. Guidelines will be produced for pilots to prevent spraying during temperature inversions. A web-based mobile platform will be developed that contains calibrated images (high-resolution satellite, agricultural aircraft, and unmanned aerial vehicle) for producers to use for monitoring the field status.
Progress Report
ARS Researchers at Stoneville, Mississippi, continued developing remote sensing methods and systems to establish risk management zones, identify spraying zones, and monitor spray drift effects. Plant physical measurements, satellite imagery, and unmanned aerial vehicle (UAV) imagery were collected from soybean fields subjected to glyphosate herbicide injury. Databases were created and further analyzed in a geographic information system, a computer that is used to manage, analyze, edit, output, and visualize geographic data. The unmanned aerial system used for spray applications software was upgraded, a remote ID broadcast module was added to the UAV and tested, laboratory and spray tests were completed, and a miniature camera system was selected with the potential of being used on the spray UAV to capture imagery sensitive to different light frequencies.
Accomplishments
1. Surveying agricultural landscapes in Mississippi with high-resolution satellite imagery. The fusion of satellite images consisting of different pixel sizes (“picture elements”) has shown promise for monitoring natural resources and farming areas. In rural areas of Mississippi, agricultural landscapes can range from complex mixtures of vegetation and built-up areas to dense vegetative regions. These areas are ideal for satellite remote sensing; however, information is lacking on using pan-sharpened satellite imagery (“merging of images with different pixel sizes”) to survey agricultural landscapes in rural areas of Mississippi. Pan-sharpening aims to make satellite imagery clearer and, thus, easier to interpret. Free and open-source software was used by ARS researchers at Stoneville, Mississippi, to test and identify image fusion techniques that were useful in merging high-resolution panchromatic (0.3 m pixel size) and multispectral (1.2 m pixel size) commercial satellite imagery to assess an agricultural landscape consisting of a combination of landcover features found in rural areas of Mississippi. The best fusion process algorithm was a trous wavelet transform with the injection model 3. The key to success is selecting an image fusion process that does not compromise the image integrity. The research provides an affordable and user-friendly remote sensing approach to collect crop data and make site-specific application decisions.
2. Velvetleaf control with bioherbicides. Velvetleaf is an invasive, problematic weed in corn, soybean, and cotton in the eastern and southern United States. A single plant can produce up to 17,000 seeds that may persist for up to 50 years in the soil and emerge throughout the crop growing season when conditions are favorable. These factors have led to a large seed reservoir in many areas where corn and soybeans are grown. The fungus Fusarium lateritium (FL) has shown potential as a bioherbicide for velvetleaf control. Greenhouse and field experiments were by ARS researchers at Stoneville, Mississippi, established to examine possible synergistic disease interactions with the herbicide 2,4-DB [4-(2,4-dichlorophenoxy) butyric acid; Butoxone]. The goal was to improve the bioherbicidal potential of FL and to mitigate some physical and environmental restrictions limiting its utility as a bioherbicide. In greenhouse experiments, a synergistic herbicidal:bioherbicidal interaction occurred from sequential applications of 2,4-DB followed immediately or after seven days by FL. Infection and weed control were inhibited by tank mixtures of 2, 4-DB and FL, and by sequential applications of FL followed by 2,4-DB. Similar results were observed in soybean fields. These results suggested timely applications of 2,4-DB, and FL may effectively control velvetleaf. These findings provided strategies and scientific guidance to support applicators and growers interested in using bioherbicides for velvetleaf control.
Review Publications
Boyette, C.D., Hoagland, R.E., Stetina, K.C. 2024. Interaction of a bioherbicidal fungus and a phenoxy herbicide for controlling velvetleaf (abutilon theophrasti). Biocontrol Science and Technology. 34(4):323-335. https://doi.org/10.1080/09583157.2024.2324396.
Hoagland, R.E., Boyette, C.D. 2024. Interaction of gibberellic acid and glyphosate on growth and phenolic metabolism in soybean seedlings. Agronomy. 14(4). Article 14040684. https://doi.org/10.3390/agronomy14040684.
Young, S.L., Anderson, J.V., Baerson, S.R., Bajsa Hirschel, J.N., Blumenthal, D.M., Boyd, C.S., Boyette, C.D., Brennan, E.B., Cantrell, C.L., Chao, W.S., Chee Sanford, J.C., Clements, D.D., Dray Jr, F.A., Duke, S.O., Porter, K.M., Fletcher, R.S., Fulcher, M.R., Gaskin, J., Grewell, B.J., Hamerlynck, E.P., Hoagland, R.E., Horvath, D.P., Law, E.P., Madsen, J., Martin, D.E., Mattox, C.M., Mirsky, S.B., Molin, W.T., Moran, P.J., Mueller, R.C., Nandula, V.K., Newingham, B.A., Pan, Z., Porensky, L.M., Pratt, P.D., Price, A.J., Rector, B.G., Reddy, K.N., Sheley, R.L., Smith, L., Smith, M., Snyder, K.A., Tancos, M.A., West, N.M., Wheeler, G.S., Williams, M., Wolf, J.E., Wonkka, C.L., Wright, A.A., Xi, J., Ziska, L.H. 2023. Agricultural Research Service weed science research: past, present, and future. Weed Science. 71(4):312-327. https://doi.org/10.1017/wsc.2023.31.
Fletcher, R.S. 2023. Comparing pan-sharpening algorithms to access an agriculture area: a mississippi case study. Agricultural Sciences. 14(9):1206-1221. https://doi.org/10.4236/as.2023.149081.
Kumar, C., Mubvumba, P., Huang, Y., Dhillon, J., Reddy, K.N. 2023. Multi-stage corn yield prediction using high-resolution (UAV) multispectral data and machine learning models. Agronomy Journal. 13(5):1277. https://doi.org/10.3390/agronomy.