Source: CONCURRENT SOLUTIONS, LLC submitted to
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
Multistate No.
Program Code
Project Start Date
Jun 1, 2015
Project End Date
Jan 31, 2017
Grant Year
Project Director
Pilgrim, R. A.
Recipient Organization
Performing Department
Non Technical Summary
Glyphosate had been used for decades as a pre-emergence herbicide in commercial agriculture. The introduction of herbicide-resistant GMO soybeans and other herbicide-resistant crops made glyphosate the most popular herbicide for grain farmers in the U.S. As the use of glyphosate in post-emergence applications increased, several important weeds, among them Palmer amaranth or pigweed (Amaranthus palmeri), and mare's tail also called horseweed (Conyza canadensis), adapted varieties resistant to this non-selective systemic herbicide. In the last 10 years, a growing number of acres of otherwise arable land have been abandoned to weed species that cannot be effectively managed in an economically viable manner.This proposal offers a new approach to weed management for commercial agriculture; a means to apply herbicide exclusively to weeds in the presence crop plants. The proposed approach combines an autonomous carrier platform to navigate the field, a machine vision system to identify plants of interest and to direct the pointing and operation of a precision, leaf-specific herbicide applicators. These applicators, can be pointed at specific plants and parts of plants such as the leaves to project very small quantities of concentrated herbicide onto specific locations with great precision. The ability to apply any herbicide directly and exclusively to weeds in the presence of the crop expands the number and types of herbicides, including those herbicides previously restricted to burn-down applications, that can be now be applied in a post emergence herbicide treatment.While the primary purpose of the proposed system is weed control, it provides several other potential advantages to commercial farming. Among these are: (1) a means to geolocate and monitor the progress of specific plants during the growing season by capturing and saving images collected during repeated trips through the field; (2) scouting for pests and diseases as well as estimating yields and variations in yields across fields; (3) the ability to determine herbicide effectiveness on a plant-by-plant basis and to collect evidence of crop damage due to splatter or drift; (4) to more accurately verify herbicide resistance in weeds to better control the statistical models used in combined experiments and split-plot field trials as well as identifying and harvesting herbicide-resistant weed seed for agricultural research.
Animal Health Component
Research Effort Categories

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
Knowledge Area
213 - Weeds Affecting Plants;

Subject Of Investigation
1820 - Soybean;

Field Of Science
2080 - Mathematics and computer sciences;
Goals / Objectives
Goal 1 - Reclaiming arable lands lost to herbicide resistant weeds and improving the yields on fields still in use.This goal supports the USDA strategic goal to Assist Rural Communities to Create Prosperity so they are Self-Sustaining.Goal 2 - Reduce the use of herbicides and reduce or eliminate herbicide drift and runoff. This goal is related to the USDA strategic goal to Ensure That Our National Forests and Private Working Lands are Conserved, Restored, and Made More Resilient to Climate Change, While Enhancing Our Water Resources.Goal 3 - Give the farmer a choice of whether to grow GMO or non-GMO crops. For example the cost of weed management in non-GMO soybean can be three times the cost for GMO soybean, however the increase in herbicide-resistant weeds is diminishing the cost differential. More significantly the ability to apply any herbicide to weeds in the presence of crops without damage to the crop plants would further reduce this difference. With the higher price per bushel and higher worldwide demand for non-GMO soybeans, our system support the USDA strategic goal to Help America Promote Agricultural Production and Biotechnology Exports as America Works to Increase Food Security.Goal 4 - To greatly reduce or eliminate the application of any herbicides to food crops. The recent concerns about the glyphosate adjuvant polyethoxylated tallow amine (POE-15) has been determined to be a toxin that is known to be damaging to human cells. This goal supports the USDA strategic goal to Ensure That All of America's Children Have Access To Safe, Nutritious, And Balanced Meals.In this Phase I SBIR we are designing an autonomous system for selective application of herbicide to weeds in the presence of crop plants. In order to reduce technical risk we have defined a number of objectives to support our design decisions leading to the implementation of a prototype system in Phase II.Objective 1 - Machine Vision: Can the machine vision system identify the weeds of importance to soybean growers in the presence of crops at every growth stage?Objective 2 - Targeting & Delivery: Can the leaf-specific herbicide application system deliver herbicide exclusively to the weeds at all growth stages without adversely affecting the crop?Objective 3 - Application Rates: What is the fastest rate that the leaf-specific herbicide application system can effectively target weeds?Object 4 - Dosage & Formulation: What is the best herbicide types, dosage, concentration and formulation for leaf-specific applications?
Project Methods
In this Phase I effort we are incorporating a variety of methods to reduce techncal risk in the design and development of our prototype herbicide application system.Field Image Collection - We are collecting images in designated soybean fields that have known infestations of herbicide resistant weeds. These images will be collected at various growth stages including pre-emergence, early emergence, open canopy, and late stage closed canopy. These images are geolocated so that they can be correlated with each other. The conditions of the field and the herbicide, crop type and soil treatments for these fields will be recorded.Software Development - The field images will be used for the development of machine vision software algorithms to detect crop vs weed and to target selective herbicide applications. Some images taken along crop rows will be used to develop autonomous vehicle navigation software.Herbicide Effectiveness - We have contracted with a weed specialist to help us determine the types of herbicide that will be effective to manage glyphosate resistant weeds. As part of this effort our weed scientist will conduct studies of site of action and mode of action for each herbicide type. We will determine applicability of each herbicide to our herbicide ejector and autonomous platform. To reduce splatter and runoff, we will conduct greenhouse experiments to compare adjutants and surfactants that are added to herbicides.Gantry Experiments - We have developed a gantry rail system for greenhouse experiments in which cameras and herbicide application modules are moved over plant beds containing crop plants and weeds. The gantry system permits us to simulated field activities but with the benefit of repeatability.

Progress 06/01/15 to 01/31/17

Target Audience: Farmers/growers Crop consultants, agronomists Application contractors/Weed control businesses Agricultural co-ops and "channel companies" (e.g., WinField, CPS) Farm equipment manufacturers Herbicide/GMO conglomerates This technology has potential impact at all levels of US agriculture. Local farmers/growers will have available a more precise method of weed control, allowing them to simultaneously reduce herbicide use and better control HR weed populations, thus getting higher yields while saving money on inputs. This technology will "trickle up" within the agricultural industry. Crop consultants/agronomists will have more options in their prescriptions and recommendations. Application contractors/weed control businesses and regional agricultural companies and co-ops can offer additional services.Herbicideand GMO conglomerates can use previously un-marketed chemicals which were either too expensive to use in broadcast spraying or damaging to the crop plants. Changes/Problems: Deviation from research schedule: To provide sufficient time to acquire seeds from herbicide-resistant weed populations and cultivate them for the leaf-specific herbicide application study, USDA granted a 1-year, no-cost extension due for the project, bringing the total project time from 8 months to 20 months. Small deviation from research goal: Although we originally proposed to use only the visible light spectrum for imaging and analysis, we also began incorporating simultaneous imaging in the near infra-red (NIR) spectrum. This opened the possibility of enhancing our data set with variations of the normalized difference vegetation index (NDVI) calculation. Unexpected outcome: Our image processing speeds have advanced orders of magnitude beyond those originally anticipated due to advances in both our hardware design and software algorithms. As a result, a slow-moving autonomous platform is no longer required to accomplish leaf-specific herbicide application. Instead, existing, human-driven farm vehicles can be used, moving at common tractor speeds. Unexpected outcome: We learned that for some types of weeds (e.g., marestail), applying a herbicide droplet to the central grow point can be more effective than applying a droplet to multiple leaves. This finding may influence our target selection moving forward. Unexpected outcome: We now intend to consider on-label usage of currently approved herbicides in addition to original plan of off-label use of unapproved chemicals. This outcome is not science- or engineering-related, but a result of our commercialization research: that the combined political, legal, financial, strategic partnering, IP, and time scale barriers to off-label chemical use are larger than anticipated. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

What was accomplished under these goals? Project Description Concurrent Solutions, LLC (CS-LLC) is developing the science and technology for controlling weeds, including herbicide-resistant (HR) weeds, by placing herbicides exclusively onto an individual weed leaf (or leaves) in the presence of crop plants in a field, without spraying and without impacting nearby crop plants. The project addresses two issues. First, the proliferation of HR weeds in recent decades, which is at least partially attributable to accelerated natural selection pressure due to broadcast spraying of non-selective herbicides on GMO crops. The proliferation of HR weeds has had the effect of reducing crop yields while increasing the costs of weed control. Second, providing an alternative or complement to broadcast spraying would also reduce herbicide drift beyond fields, runoff into water bodies, and the amount of herbicide residuals in the food supply, while giving farmers more latitude in seed selection to optimize yields. These project goals support 4 of the USDA's strategic goals. Activities CS-LLC is uniquely qualified to develop the proposed technology for the agricultural domain, based on their years of experience developing sensing and kinetic strike technologies for the DoD. During the Phase I effort our team established the feasibility of our proposed technology by focusing on a specific crop (soybeans) and specific weeds of concern to farmers (Palmer amaranth and marestail). CS-LLC personnel collected field image data of both weeds and crops and used these images to develop effective and efficient software algorithms capable of identifying and differentiating crop leaves and weed leaves in real-time from a moving platform. Simultaneously, our weed expert at the University of Kentucky performed a novel dose-response study on HR strains of weeds of concern to demonstrate that our leaf-specific application method is capable of controlling weeds. Finally, CS-LLC personnel engineered and constructed prototype mobile imaging equipment and precision herbicide ejectors capable of addressing the kinetic challenges of our proposed goals. The results of this Phase I work has demonstrated the promise of our technological approach to leaf-specific weed control. Impact This technology has potential impact at all levels of US agriculture as follows. Researchers, regulators, crop consultants, and agronomists will learn the benefits of the approach and begin recommending it to growers. Farmers will begin choosing to use the technology to better control HR weeds and increase their yields, thus creating a market demand. In response, application contractors, weed control businesses, farm co-ops, and regional agricultural companies will begin to offer new services using large-scale leaf-specific herbicide application. These changes will drive farm equipment manufacturers to innovate and manufacture new applicator products and allow herbicide manufacturers and GMO companies to use previously un-marketed chemicals which were either too expensive to use in broadcast spraying or damaging to the crop plants. The end result will be new conditions for US agriculture, such as a diminishing rather than increasing HR weed problem, more options for seed selection and herbicide treatments, and smaller amounts of herbicides in the environment and food supply. Accomplishments We identified 4 main objectives to achieve our project goals, relating to machine vision, targeting/delivery, application rates, and dosage/formulation. Both our goals and objectives are described above. The methods, data, and results are summarized below, by task: Objective 1 - Machine Vision: We collected field images at all growth stages in both the visible and near infrared spectrums. We used the images to develop machine vision software algorithms capable of differentiating between soybean plants, Palmer amaranth, and marestail. We based these algorithms on related software we built capable of identifying black grass stalks in wheat fields. These algorithms are feasible because our cameras resolve 3 pixels per millimeter, which is orders of magnitude more detail than competing imaging systems. This resolution is even theoretically enough to identify differences between standard Palmer and HR strains Palmer strains because it can pick up leaf traits such as petiole length, smoothness, and chevron colorations. Objective 2 - Targeting & Delivery: We constructed a number of experimental apparatus, including a 40 foot long greenhouse, a 24 foot long gantry with computer-controlled sled, and two prototype high-precision herbicide droplet ejectors. We conducted experiments in platform motion, targeting, splatter, and wind effects. Accuracy and splatter tests using our new herbicide ejector prototypes were significant improvements over the versions we built in 2004. Our new prototype applicator can dispense 1-10 microliter herbicide droplets within 1-10 milliseconds (at up to 261 shots per second) and hit a ½ inch target from a moving platform traveling 2 feet above the target. The splatter from such a shot will leave 99.9% of the herbicide on the leaf, causing minimal impact to adjacent leaves and no impact to leaves more than 1 inch away. Objective 3 - Application Rates: We conducted experiments using our imaging system, gantry, and herbicide ejector prototypes. We plugged the results into analytical models that factor in variables including, but not limited to, target density, processing speed, targeting speed, firing rate, platform motion, herbicide droplet time-of-flight, and wind deflection. We designed and built specialized computer hardware optimized to run our machine vision and targeting algorithms in the least amount of time possible. Our model shows that, with a sufficiently large array of herbicide ejector nozzles, it would be practical using current technology to tow a leaf-specific herbicide ejector system at speeds of up to 8 mph. Objective 4 - Dosage & Formulation: Our weed expert at University of Kentucky selected 5 systemic herbicides for the dose-response study after considering sites/modes of action from 12 WSSA herbicide groups. The novel study focused on off-label herbicide usage at high concentrations applied to weeds up to 6 inches tall. Rather than spraying plants, concentrated herbicides were directly applied to selected weed leaves (for Palmer amaranth) and grow points (for marestail) at precise doses ranging from 1 to 10 microliters using a micro pipette. Glyphosate emerged as the most effective herbicide, capable of killing all but the largest plants in the study using just 5 microliters applied to 1-2 leaves or the growing point. Conclusion In successfully completing the four project objectives, we have demonstrated the feasibility of our leaf-specific herbicide application approach to precision weed control in row crops. Our approach is ideal for weeds up to 8 inches in height, post crop emergence and prior to canopy closure. We can use computer vision to automatically identify weeds, target them, and apply discrete droplets of herbicide to them without impacting the adjacent crops. The tiny amounts of active ingredient required (micrograms) applied to ~1-3 leaves of each plant represent a 90-99% reduction in herbicide use for full-field weed control. The farm equipment involved can be driven through a field at speeds of up to 8 mph. We believe that any one of our results is significant enough to warrant further work. Taken in aggregate, our results may represent a new paradigm for precision weed control.