Location: Crop Production Systems Research
2021 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
The unmanned aerial vehicle with dual-use was fully assembled, and the spray system was tested in a greenhouse setting. Plant physical measurements, soil data, and satellite imagery were obtained of soybean fields subjected to herbicide injury.
Accomplishments
1. Agricultural chemical applicators. Agricultural chemical applicators need to follow proper spray procedures to prevent susceptible crops, animals, people, or other living organisms from being injured far downwind. Spraying during stable atmospheric conditions should be avoided to avert surface-temperature inversion-induced off-target drift of crop protection materials. ARS researchers in Stoneville, Mississippi, have developed a website to provide a real-time online guide for determining the proper time to conduct aerial spray during the day. Furthermore, they created another web application specifically for Stoneville, Mississippi, with data measured from weather stations constructed from inexpensive open-source electronics, accessories, and software for more accurate online guidance for site-specific drift management. The web application was adapted for use on mobile terminals, such as smartphones and tablets, and provides timely guidance for aerial applicators and producers to avoid spray drift and air quality issues long distances downwind in the area.
2. Pigweeds negatively impact crop production systems throughout the world. Pigweeds are distinguished from each other using manual methods that are tedious and time-consuming to complete. An ARS researcher in Stoneville, Mississippi, determined narrowband remotely sensed vegetation indices designed to measure biophysical and biochemical properties of plants have the potential for separating between two to three pigweed species. The study focused on the following pigweeds: Palmer amaranth, redroot pigweed, smooth pigweed, spiny pigweed, waterhemp, and tumble pigweed. The biochemical index and the red edge index were the most consistent in pigweed species separation. It is believed that two or more vegetation indices should be used in an ensemble approach to maximize pigweed separation in the future. Future research should focus on that concept and evaluating other spectral band combinations as indices for differentiating pigweed species from each other and other weeds and crops.
Review Publications
Huang, Y., Ma, W., Fisher, D.K. 2021. Development and evaluation of an optical sensing system for detection of herbicide spray droplets. Advances in Internet of Things. 11:1-9. https://doi.org/10.4236/ait.2021.111001.
Fletcher, R.S., Fisher, D.K. 2021. Testing an open-source multi brand sensor node to monitor variability of environmental conditions inside a greenhouse. Agricultural Sciences. 12(3):159-180. https://doi.org/10.4236/as.2021.123011.
Fisher, D.K., Fletcher, R.S., Anapalli, S.S., Pinnameneni, S.R. 2021. Python software integrates with microcontrollers and electronic hardware to ease development for open-source research and scientific applications. Modern Intrumentation. https://doi.org/10.4236/ait.2021.111004.
Zhang, T., Huang, Y., Reddy, K.N., Yang, P., Zhang, J. 2021. Using machine learning and hyperspectral images to assess damages to corn plant caused by glyphosate and to evaluate recoverability. Agronomy. 11:583. https://doi.org/10.3390/agronomy11030583.
Fletcher, R.S. 2020. Assessing hyperspectral vegetation indices responses of six pigweed species. American Journal of Plant Sciences. 11(12):1934-1948. https://doi.org/10.4236/ajps.2020.1112138.
Zhou, X., Zhang, J., Chen, D., Huang, Y., Kong, W., Yuan, L., Ye, H., Huang, W. 2020. Assessment of leaf chlorophyll content models for winter wheat using Landsat-8 multispectral remote sensing data. Remote Sensing. 12(2574):1-18. https://doi.org/http://dx.doi.org/10.3390/rs12162574.
Yang, X., Zhao, Y., Street, G.M., Huang, Y., To, S., Purswell, J.L. 2021. Classification of broiler behaviors using triaxial accelerometer and machine learning. Animal-The International Journal of Animal Biosciences. https://doi.org/10.1016/j.animal.2021.100269.
Yu, X., Zhu, W., Wei, J., Jia, S., Wang, A., Huang, Y., Zhao, Y. 2021. Estimation of ecological water supplement for typical bird protection in the Yellow River Delta wetland. Ecological Applications. 127. Article 107783. https://doi.org/10.1016/j.ecolind.2021.107783.
