Operationalizing Drone Imaging Technology to Detect Nutrient Deficiencies in Fruit Orchards

Project Overview

Project Type: Research Only
Funds awarded in 2023: $200,000.00
Projected End Date: 02/28/2026
Grant Recipient: University of Connecticut
Region: Northeast
State: Connecticut
Project Leader:
Dr. Chandi Witharana
University of Connecticut


  • Fruits: apples, berries (blueberries), grapes, peaches


  • Crop Production: nutrient management

    Proposal abstract:

    Our proposed project seeks to operationalize a novel technology that has been developed over the last three years, which has tied drone imaging to the detection of plant nutrient statuses on a whole-farm scale. The technology relies on a number of predictive models, based on correlations found between two drone imagery derived vegetative indices (NDVI, NDRE) and individual internal plant macro- and micronutrient levels, to accurately determine the need for supplemental nutrient applications within fruit orchards. This project has three goals, together which would allow for its adoption by our local agricultural community: 1) determine if corrective action based solely on the drone models affects fruit yield and/or fruit quality, 2) determine if the inclusion of super- or suboptimal data points from less experienced growers extend the effective range of the models, and 3) if the technology is economically feasible, affording cost savings onto farmers. Over three years, 7 farms will participate in the research process as actively contributing members of the research team. Growers will be engaged with on-farm demonstrations and will be responsible for applying recommended corrective actions as well as participating in the evaluation of the treatments on fruit yield and quality. We have designed this study to include and engage historically underserved farmers in our community in an effort to pursue social justice. 

    Project objectives from proposal:

    Validate and operationalize predictive models, which utilize drone images/derived vegetation indices (NDVI, NDRE) to quickly predict plant tissue nutrient levels on a whole-farm level instead of relying on traditional, limiting, and costly tissue analysis in hopes of impacting current year’s crop. Farmer’s corrective action (e.g., foliar sprays) will rely solely on model-based predictions, with control plots for comparison. Models have been developed for apple, peach, blueberry, and grape with regards to both macro- and micronutrients, comprised of three years of data across three locations in Connecticut. Once verified with growers, this technology will be available to replace existing nutrient-testing practices. 

    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.