Location: Crop Production Systems Research
2020 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
New project replaced bridging project 6066-22000-080-00D, "Application Technologies to Improve the Effectiveness of Chemical and Biological Crop Protection Materials" which expired on February 17, 2020.
Objective 1: The new optical sensing platform has been prototyped and tested to directly detect herbicide spray drift droplets in the categories of ASABE (American Society of Agricultural and Biological Engineers) standard.
Objective 2: A new multispectral camera integrating sensors of red, green, blue, red edge and near infrared narrow bands and thermal band is purchased and it has been mounted and tested on a consumer-grade and an industrial grade unmanned aerial vehicles (UAV) respectively and it will be used in field experiments next year.
Objective 3: A web-based internet application has been prototyped based on cloud-based server to build a repository of images acquired in the past few years from UAVs in the area of USDA ARS research farms at Stoneville, Mississippi, for online retrieval.
Accomplishments
1. Evaluation of a novel fluorescent compound to measure herbicide physical drift. Several thousands of incidents of injury to non-dicamba resistant broadleaf crops and other dicotyledonous flora across the agricultural landscape caused by dicamba drift arising from applications to dicamba-resistant soybean and cotton have been reported across the midwestern and southeastern regions in the U.S. from 2016 to 2019. Additionally, drift from aerial applications of glyphosate, targeted on glyphosate resistant crops to rice, sorghum, and sometimes wheat, is another issue that has plagued growers in Mississippi every year. Two ARS researchers, one agricultural engineer and one plant physiologist, in Stoneville, Mississippi, evaluated a novel technique of adding a fluorescent compound to the spray carrier to measure dicamba and glyphosate drift. In field studies, addition of the fluorescent compound, Nightops®, to dicamba plus glyphosate applications in dicamba-resistant soybean did not affect herbicide efficacy or cause crop injury. The experiments were conducted in lab using laser spray droplet sizer and in field using water sensitive cards. In the experiments to test rainfastness of Nightops, a soybean variety resistant to both dicamba and glyphosate was treated with Nightops alone and in combination with dicamba and dicamba plus glyphosate plus a drift management agent (DMA). Treated plants were sprayed with simulated rainfall for a duration of from 0 to 120 sec. After each rainfall timing, plants were allowed to dry and then imaged under black light and rated for % retention of Nightops. Results indicated that dicamba plus glyphosate plus DMA tended to have the lowest retention up to 60 sec. The results illustrate that Nightops is a reliable new tool for measuring herbicide drift.
2. Differentiation of redroot pigweed from cotton plants based on the canopy light reflectance. Redroot pigweed is a nuisance weed that affects cotton growth and yield worldwide. Being able to distinguish it from cotton would help producers and crop consultants better implement strategies used to suppress and control it. An ARS researcher in Stoneville, Mississippi, determined that redroot pigweed could be differentiated from cotton plants with okra and super okra leaf shapes based on the light reflectance properties of their canopies. Light reflectance bands sensitive to leaf pigments, leaf orientation in the plant canopy, and leaf water content were found to be optimal for redroot pigweed and cotton separation. Commercial imaging systems used on ground-based or airborne platforms can be easily tuned into the spectral bands listed in this study, thus providing managers with a tool to use in precision agriculture applications of redroot pigweed in cotton production systems.
Review Publications
Huang, Y., Fisher, D.K., Silva, M., Thomson, S.J. 2019. A real-time web tool for safe guide system for aerial application to avoid off-target movement of spray induced by staple atmospheric conditions in the Mississippi Delta. Applied Engineering in Agriculture. 35(1):31-38.
Zhang, J., Huang, Y., Reddy, K.N., Wang, B. 2019. Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning. Pest Management Science. 75:3260-3272.
Fletcher, R.S., Fisher, D.K. 2019. Spatial analysis of soybean plant height and plant canopy temperature measured with on-the-go tractor mounted sensors. Agricultural Sciences. 10/1486-1496.
Fletcher, R.S. 2019. Canopy hyperspectral reflectance of redroot pigweed versus okra and super okra leaf cotton. Agricultural Sciences. 10/1465-1476.
Huang, Y. 2019. Agricultural aviation perspective on precision agriculture in the Mississippi delta. Smart Agriculture. 1(4):12-30.
Xia, L., Zhang, R., Chen, L., Huang, Y., Xu, G., Wen, Y., Yi, T. 2019. Monitor cotton budding using SVM and UAV images. Applied Sciences. 9(4312): 1-13. https://doi.org/10.3390/app9204312.
Fisher, D.K., Fletcher, R.S., Anapalli, S.S. 2019. Evolving open-source technologies offer options for remote sensing and monitoring in agriculture. Advances in Internet of Things. 10:1-10. https://doi.org/10.4236/ait.2020.101001.
