Evaluation of New Technology to For Foliar Disease Management in Indiana Soybean and Corn

Darcy Telenko and Monica Mizuno

Aerial drone application technology has recently become available and may address limitations by providing greater agility to monitor and apply under conditions where obstacles and poor field conditions limit current pesticide application equipment. As aerial drones are more readily available, there is a great opportunity to develop research-based best practices and training for this new technology. In addition, changes in climate continue to affect the survival, distribution and aggressiveness of plant pathogens. The goal of this research is to determine the feasibility of fungicide application via drones as sustainable disease management tool.

Questions we aim to address:
  • Will aerial drone applications technology is practical and feasible in Indiana soybean and corn?
  • Will the drone applicator provide sufficient spray coverage to for disease protection?
  • Can this equipment be used safely by the applicator while protecting workers and the environment?

 

Objective: Determine the feasibility of aerial drone application for disease management in replicated field-scale research trials. Research trials were established at three Purdue Agricultural Centers (Northeast (NEPAC), Davis (DPAC), and Southeast (SEPAC)) locations where foliar diseases were previously documented. Treatments consisted of three-application methods 1. standard ground rig at 15-20 gallons per acres (GPA), 2. drone application at 2 GPA and 3. drone application at 5 GPA compared to non-treated control. These treatments will be replicated in strips across the field. These will look at efficacy in disease control and use efficiency of the drone technology compared to the standard ground-rig application. Data collection included crop coverage evaluation, disease assessments and yield (see year 1 results in slides below from each farm).

The drones that were utilized in this study in 2023 include:

A DJI Agras T30 UAS with the payload capacity of 7.9 gallons will be used at SEPAC. The drone spray width can be manipulated by adjusting several parameters and dependent on environmental conditions, but generally an accepted maximum width is roughly 30-feet wide with narrower spray swaths possible. The effective spray width of the T30 aligns well with the other agricultural equipment SEPAC will use to plant, manage, and harvest the proposed trial. The T30 is capable of applying a range of 0.75 gallons per acre of product to 18.9 gallons of product per acre although practical application in large scale plots will limit spray volume to 5.0 gallons per acre or less in most instances.

A DJI Agras T10 with a 2.1-gal spray tank with a spray pattern of 16.4-ft wide to cover 15 acres/hour will be used at NEPAC and DPAC. The drones will use a DJI Terra Pro package for programming the flight and spray programs.

In the near-term, this research will provide preliminary data on how drone pesticide application technology will perform in management of foliar diseases in corn and soybean.

In the long-term, this work will provide a baseline for the use of these new aerial spray drones and their utility in row crops as compared to the traditional application technology. Once a standard operating procedure for aerial drone applicator is established, it will aid in developing useful and timely information, resources, and training for agricultural producers and crop advisors, including crop consultants, Extension, and agricultural industry personnel for the successful implementation of this new technology.

Mixing chemicals on DJI Agras T30
DJI Agras T10
Moisture sensitive paper in corn canopy.
Image capture from moisture senor paper
Collecting data.
Drone application in corn.
Figure 1. Trail data summary from Northeast Purdue Ag Center (NEPAC), tar spot and northern corn leaf blight (NCLB) were the most prominent disease in the trial and reached low severity. All application type reduced tar spot severity over nontreated control on 2 Oct, with application using the DJI at 5 GPA resulting in the lowest level of tar spot. There was not significant difference in treatments for corn grain yield.

Figure 1. Trial data summary from Northeast Purdue Ag Center (NEPAC), tar spot and northern corn leaf blight (NCLB) were the most prominent diseases in the trial and reached low severity. All application types reduced tar spot severity over nontreated control on 2 Oct, with an application using the DJI T10 at 5 GPA resulting in the lowest level of tar spot (P-value <0.001). There was no significant difference in treatments for corn grain yield (P-value =0.693).

Figure 1. Trail data summary from Northeast Purdue Ag Center (NEPAC), tar spot and northern corn leaf blight (NCLB) were the most prominent disease in the trial and reached low severity. All application type reduced tar spot severity over nontreated control on 2 Oct, with application using the DJI at 5 GPA resulting in the lowest level of tar spot. There was not significant difference in treatments for corn grain yield.

Figure 2. Trial data summary from Northeast Purdue Ag Center (NEPAC), frogeye leaf spot and Septoria brown spot were the most prominent diseases in the trial and reached low severity. There was no significant effect between application type and nontreated control for frogeye leaf spot on 7 Sep (P-value =0.185). There was no significant difference in treatments for soybean yield (P-value =0.401).

Figure 3. Trial data summary from Davis Purdue Ag Center (DPAC), tar spot and gray leaf spot were the most prominent diseases in the trial and reached low severity. All application type reduced tar spot severity over nontreated control on 20 Sep, except applications using the ground-rig and the DJI T30 at 2 GPA applied at VT/R1 and DJI T30 at 5 GPA applied at R3 (P-value =0.015). There was no significant difference in treatments for corn grain yield (P-value =0.959).

 

Figure 4. Trial data summary from Davis Purdue Ag Center (DPAC), frogeye leaf spot and sudden death syndrome were the most prominent disease in the trial and reached low severity. There was no significant effect between application type and nontreated control for frogeye leaf spot on 7 Sep (P-value =0.244). There was no significant difference in treatments for soybean yield (P-value =0.361).

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Figure 5. Trial data summary from Southeast Purdue Ag Center (SEPAC), tar spot and gray leaf spot were the most prominent diseases in the trial and reached low severity. There was no significant effect on application type on tar spot severity over nontreated control on 14 Sep (P-value =0.399). There was significant difference in treatments for corn grain yield (P-value =0.014). Fungicide sprayed with DJI T30 drone at 2 GPA and ground-rig significantly increased yield over the nontreated control.

Figure 6. Trial data summary from Southeast Purdue Ag Center (SEPAC), frogeye leaf spot, Septoria brown spot, and Cercospora leaf blight were the most prominent diseases in the trial and reached low severity. The ground-rig sprayed at R3 had a higher severity of frogeye leaf spot over other application types (P-value =0.008). There was no significant difference in treatments for soybean yield (P-value =0.693).

Figure 7. Trial data summary from Pinney Purdue Ag Center (PPAC), tar spot was the most prominent disease in the trial and reached moderate severity. All applications of fungicide reduced tar spot over nontreated control, but there was no significant difference between application type  on16 Oct (P-value >0.001). There was no significant difference in treatments for corn grain yield (P-value =0.693).

For more detailed trial information check out the Applied Research in Field Crop Pathology for Indiana - 2023 pages 31, and 47-52.

Slides of Drone Data 2023

Acknowledgements:

Project supported by Indiana Soybean Alliance.

We would like to thank all Telenko lab members, Purdue Ag Center and county Extension staff that assisted with these trials, especially Ivis Miranda, Su Shim, Steve Brand, Alex Helms, Nikky Witkowski, Amy Alka, Joel Wahlman, Jeff Boyer, Steven Boyer, and Chris Lake.