Progress report for LNE23-482R
Project Information
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.
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.
Current plant nutrient testing protocols rely on two types of tests: soil analysis and tissue analysis. Soil analysis is typically done every two years as soil nutrient levels can be slow to change. Tissue analysis is required because adequate soil nutrition does not always equate to adequate plant nutrition. Tissue analysis will always provide the best insight into the health of a plant. However, tissue analysis relies on skills and services that may not be readily available or accessible to all farmers. The cost of a single plant tissue analysis at the UConn Soil Nutrient Analysis Laboratory is $30.00 and the turn-around time for results is at least several weeks. Typically, farmers do not submit a single tissue sample for analysis and costs can accumulate quickly. This analysis also relies on proper sample collection techniques, 100 of the most recently matured leaves, to give an accurate depiction. Typically, growers receive results at the end of the growing season and may rely on interpretations and corrective action recommendations from Extension professionals. This corrective action is always taken in hopes of impacting the next year’s crop. This current process does nothing to address corrective action for the current year’s fruit crop which may suffer or fail.
Our novel approach utilizes 62 drone-image-driven predictive models developed in our previous study to accurately predict internal plant nutrient levels. The technology utilizes drones to capture multispectral data on a whole farm scale which is then run through numerous crop and nutrient specific equations to determine plant nutrient levels without the need for the traditional testing processes. This eliminates the weeks long turn-around time, providing insight into plant health the same day. Our hope is that this technology will also provide cost savings for farmers and impact their current year’s crop.
Growers have shown a high interest in plant nutrition beginning with a 2015 plant nutrition intensive short course, 5-year study with fruit growers utilizing a combination of tissue and soil analysis, crop load and environmental conditions to develop fine-tuned fertilizer programs, to the recent drone study developing a model to detect nutrient deficiencies early in the season. CT fruit growers were surveyed in June and July 2022 to gauge interest in using drones (# surveys sent out =398, # responses =41). Respondents were from across CT with farm sizes ranging from 1 to over 100 acres. 90.24% expressed interest in learning how drones can be used to detect nutritional issues; 22% presently use drones primarily for farm marketing and advertising; and 22% would like to learn how to use drones in farming. Barriers to drone use identified were ‘have not seen a need yet’, ‘do not understand how they will help my business’, ‘cost’, and ‘not tech savvy enough’. This work will show drones can be used to effectively identify plant nutrition issues. It will be beneficial to growers as it can provide actionable crop analytics in near-real time to take corrective actions. All farms in the northeast could benefit no matter the size.
Research
Hypothesis 1 [H1]: Corrective actions to improve plant nutrition based solely on our proprietary drone-imagery driven predictive models will positively influence the yield and quality of the current year’s fruit crop in participating orchards.
Hypothesis 2 [H2]: Including a broader grower group to include those of varying skill levels and expertise will provide valuable data to extend the effective range of the predictive models.
Hypothesis 3 [H3]: The economic analysis will prove that the technology is affordable and accessible to all fruit growers in CT, despite background, acreage or experience level.
In Year-1 we executed the drone remote sensing and field sampling based on proposed randomized block design. We started drone imaging in June and continued until late at bi-weekly intervals. We established permanent ground control points (GCPs) in each study block in each farm to locate reflectance targets during drone data collection missions. Using RTK GPS receiver, we collected the coordinates of the GCPs and later stages used as inputs for image orthorectification and co-registration processes. Drone images were acquired using Micasense RedEdge-MX dual camera system. We followed pre- and post-sensor calibration steps using spectral reflectance panel in each mission to make sure the radiometric consistency of acquired data. The acquired multispectral images consist of 10 spectral bands in visible and near-infrared wavelengths. We used Agisoft MetaShape software to process drone images. Main steps involved stitching, orthorectification, and radiometric calibration based on GCPs and reflectance panel data. We further analyzed bi-weekly orthomosaics using ArcGIS Pro software. Using orthomosaic we segmented out individual canopies and derived necessary vegetation indices. We are in the process of combining plant tissue analysis data with drone-based vegetation indices refine nutrient predictive models.
Education & Outreach Activities and Participation Summary
Educational activities:
Participation Summary:
Outreach and education activities for the first year of this project were largely limited to 2 grower events and one guest lecture provided to college students. One presentation on the drone technology was provided to the CT Pomological Society at their Summer Field Day on June 13, 2023. There were approximately 125 attendees including growers, agricultural educators, and service providers. The presentation highlighted the previous work and outlined the proposal for the next three years. Another presentation was given out of state, in MA, to the Massachusetts Cultivated Blueberry Gorwers’ Association Summer meeting. Ther were approximately 20 attendees at this event. All were primarily growers. This presentation highlighted drone technology, outlined the proposal for the next three years, and highlighted its potential impacts on blueberry management practices specifically. Finally, details about this project were provided to the Small Fruit Production class in the spring semester of 2024. This project was highlighted during the introductory nutrient management lecture to showcase up and coming technologies to assist in nutrient management for small fruit. There are 20 students enrolled in this class. This is a production-based course designed to assist those wishing to begin farming.