The Now and Next of Cotton Scouting Technology

When it comes to in-season management, the cotton crop presents agronomists with challenges that must be monitored.


With the plethora of technologies unleashed on agriculture for a wide range of crops in recent years, a number of individuals, companies, and organizations are working to test their mettle in the cotton market. Among those leading the way is Ed Barnes, Senior Director of Agricultural and Environmental Research at Cotton Inc.

Barnes works extensively with companies and Universities on identifying the best pathways to commercializing technology for in-season scouting, and there are several promising products and practices that have, or will soon, emerge for use in the cotton production cycle.

Irrigation, Pests

The ubiquity of phone connectivity has led to targeted, highly functional apps to provide support and help maximize every scouting trip in the field.

For example, while irrigation in the southeast and mid-south regions has increased substantially in cotton and other crops, it’s a tricky business. Hyper-local pop-up storm activity is the norm, which confounds weather models based on ground sensing technology. And less is understood about the physiology of cotton as far as the impact of moisture depravation — how much yield is lost if no rain falls during boll fill time?

Developed by the University of Georgia in conjunction with Cotton Incorporated, the SmartIrrigation Cotton app has been available to alert field scouts to irrigation deficit issues in fields since 2014. The app combines data from a farm-based sensor station, which is required for maximum effectiveness, with data aggregated from a range of available sources: Meteorological data, soil parameters, crop growth stage, crop coefficients, measured rainfall, and irrigation applications. The app returns an estimate of root zone soil water deficits in terms of inches of water and percent of total, which can be used to decide whether the time is right to apply water to a field.

GA Cotton Insect Advisor is an expert system for determining Extension prescribed insecticide treatments for management of cotton insect pests in the state of Georgia. The app displays the most appropriate insecticide or tankmix after users provide the appropriate week of bloom, predominant stink bug species, percent internal boll injury, and other pests present. At present, the app is intended for management of stink bugs. Recommendations are based on information on the manufacturer’s label and performance data from research and extension trials the University of Georgia.

On the insect side, North Carolina State University offers its Thrips Infestation Predictor for Cotton, an online tool that uses weather data to make predictions on the intersection of thrips dispersal and the development of susceptible seedlings, allowing for optimum timing of insecticide application.

There are also resources available through the Cotton Incorporated website, Cotton Cultivated, “with connections to state level cotton sites that will keep you current with the latest recommendations specific to your area,” notes Barnes.

Sensing Problems

The recent wave of technology investment in ag features extensive work in the area of sensing technology. And while a lot of stand-alone systems have come and gone, the next wave of systems and concepts are more targeted and collaborative.

Barnes notes that unmanned aerial systems (UAS) are continuing to “demonstrate their value for crop scouting,” in particular for taking plant counts. “Research at the University of Tennessee and at North Carolina State University have demonstrated that UAV images can provide very robust stand counts to help in cotton replant decisions,” he explains. “More and more tools are coming to turn these UAS images into information.”

Farmwave’s on-the-go crop damage recognition system on soybeans.

Farmwave’s on-the-go crop damage recognition system on soybeans.

A bit farther down the path but very focused technology is Farmwave’s employment of artificial intelligence to detect disease issues on cotton. Using a camera mounted to a piece of field equipment to capture images in real time, the Farmwave system is able to “see” and “diagnose” disease issues while mounted on a rig travelling up to 20 miles per hour. The algorithm is powered by soybean and corn disease data collected over eight years and validated by scientists, and the results have been very positive, says Chris Palczynski, Farmwave’s Chief Sales and Marketing Officer.

SpadeGeo is a recently launched company looking to expand machine learning technology to the broader agriculture industry, with particular interest in cotton. Cofounder Bobby Vick, who left a UAV company to start the new venture, sees opportunity to collaborate with existing ag companies and help farmers make gains in difficult but essential activities such as irrigation control, pest monitoring, harvest timing, and stand counts.