Location: Crop Production Systems Research2022 Annual Report
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.
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.
The unmanned aerial vehicle (UAV) was upgraded to meet government security safety specifications, and additional training was completed on using the UAV for spray applications. The UAV spray system was equipped with different spray nozzles and tested in laboratory and field settings. Plant biophysical measurements and satellite imagery were collected of soybean fields subjected to glyphosate herbicide injury.
1. Hyperspectral imaging to detect disease and insert stresses of tea. Plant monitoring for disease and insect stresses is essential for crop management. ARS researchers in Stoneville, Mississippi, in collaboration with resarchers from Hangzhou Danzi University, have developed a hyperspectral imaging method to rapidly detect and discriminate disease and insect stresses in tea plants. The results indicated that the developed method could detect and distinguish between stresses caused by diseases or insects. This study provides valuable information on a hyperspectral imaging method that can be applied to tea plant monitoring for effective management. The accomplishment aligns with the NP304 integrated pest management strategies.
Zhao, X., Huang, Y., Zhang, J., Yuan, L., Xu, J. 2022. Detection and discrimination of disease and insect stress of tea plants using hyperspectral imaging combined with wavelet analysis.. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.106717.
Dhakal, M., Huang, Y., Locke, M.A., Reddy, K.N., Moore, M.T., Krutz, J., Gholson, D., Bajgain, R. 2022. Assessment of cotton and sorghum stand establishment using UAV-based multispectral and DSLR-based RGB imagery. Agrosystems, Geosciences & Environment. 5(2):e20247. https://doi.org/10.1002/agg2.20247.
Huang, Y., Zhao, X., Pan, Z., Reddy, K.N., Zhang, J. 2022. Hyperspectral plant sensing for differentiating Glyphosate-resistant and Glyphosate-susceptible Johnsongrass through machine learning. Pest Management Science. 78:2370-2377. https://doi.org/10.1002/ps.6864.