Location: Crop Production Systems Research
2023 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
Plant biophysical measurements, satellite imagery, and unmanned aerial vehicle imagery were collected of soybean fields subjected to glyphosate herbicide injury. The datasets were integrated into geographic information system software and analyzed. For the unmanned aerial vehicle used for spray testing, the software to control the UAV was upgraded, and the UAV was equipped with different spray nozzles and tested in laboratory and field settings.
Accomplishments
1. Temporal comparison of soil apparent electrical conductivity. On-the-go sensor systems measuring apparent electrical conductivity in soils within agricultural fields have provided valuable information to producers, consultants, and researchers on understanding soil spatial patterns and their relationship with crop components. Nevertheless, more information is needed in Mississippi on the longevity of the measurements. An ARS researcher in Stoneville, Mississippi, compared the spatial patterns of apparent electrical conductivity data collected from a research plot in 2016 and 2021 and determined that the spatial patterns of the data were consistent between years. The user has at least a five-year window from the first data collection to the next data collection to determine the relationship of the apparent electrical conductivity data to other soil and agronomic variables. Furthermore, the research analysis was completed with open-source software available to anyone with access to a desktop or laptop computer. This study's findings benefit farmers, consultants, and researchers interested in using on-the-go soil sensors and computer software to make management decisions.
Review Publications
Fletcher, R.S. 2022. Temporal comparison of apparent electrical conductivity: A case study on clay and loam soils in Mississippi. Agricultural Sciences. https://doi.org/10.4236/as.2022.138058.
Kharel, T.P., Bhandari, A.B., Mubvumba, P., Tyler, H.L., Fletcher, R.S., Reddy, K.N. 2023. Mix species cover crop biomass estimation using planet imagery. Sensors. https://doi.org/10.3390/s23031541.
Fletcher, R.S. 2023. Machine learning mapping of soil electrical conductivity in mississippi. Agricultural Sciences. Vol.14 No.7 July 2023. https://doi.org/10.4236/as.2023.147061.
