Duration: 10/01/2026 to 09/30/2031

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Unmanned Aircraft Systems (UAS), or drones, are transforming how we monitor and manage U.S. agriculture and natural resources. UAS can rapidly collect detailed information on crops, livestock, forests, and aquatic systems, helping producers and researchers make better decisions for boosting yields, detecting stresses early, and improving sustainability. New advances, such as drones that spot-spray or spread materials, promise even greater precision and efficiency in management. However, several barriers limit broad adoption. Many producers and researchers struggle with handling large volumes of drone data and converting it into useful recommendations. Concerns over equipment cost, regulatory uncertainty, workflow complexity, and a lack of clear return on investment persist. Additionally, there is a pressing need for accessible data management systems and workforce training, including for extension personnel who connect research to end users. The S1069 multistate project addresses these challenges by uniting experts across disciplines and regions. Together, we are advancing sensing technologies, refining data workflows, evaluating new applications, and developing educational resources. Renewal of this project will broaden the scientific foundation for UAS, accelerate technology transfer, and expand coordinated training for producers, students, and extension professionals. Ultimately, this effort will drive wider, more effective use of drones in agriculture and resource management, supporting increased productivity and sustainability nationally.

Statement of Issues and Justification

Unmanned/Unoccupied/Uncrewed Aircraft Systems (UAS, also known as drones) have rapidly emerged as transformative tools for agriculture and natural resource management. Equipped with diverse sensors, UAS can collect high-resolution data at spatial and temporal scales that were nearly impossible a decade ago. The resulting data enables crop and livestock discovery, prediction, and improvement. UAS, plot, plant and biological characterization for agricultural goals like stress detection, and growth monitoring, enables breeders, researchers, and producers to make rapid and robust decisions. Another key use of UAS is for scouting, providing fast, cost-effective assessment and early identification of problems across crops, animals, forests, pastures, and rangelands. More recently, high-capacity UAS, particularly those equipped with spot-spraying and granular spreading capabilities, have shown promise as precision agriculture tools. Since its initiation, the S1069 multistate project has demonstrated the value of coordinated research and extension by advancing sensing technologies, improving data workflows, and testing applications across crop production, livestock systems, forestry, and rangelands.

Despite significant advances, challenges remain. Challenges especially exist in the adoption of UAS as a routine tool across research/breeding programs and production systems, despite researchers desiring to use these tools more frequently (Lachowiec et al. 2024). For catalyzing and synthesizing research, having an easily accessible centralized UAS data management system is still lacking. On the production side, major issues include spatial coverage, and needs of computational capacities for processing large volumes of data. Additionally, while UAS technology produces high-resolution data, many producers and land managers struggle to translate UAS imagery and sensor outputs into actionable insights that directly help decisions. Adoption of UAS also varies widely due to concerns over cost, workflow complexity, regulatory uncertainty, and unclear return on investment. To increase accessibility, research must focus on streamlining operations, reducing costs, and quantifying the economic benefits of UAS adoption. Broader adoption requires continuous evaluation of UAS-based tools across multiple crops, fields, time periods, and regions. Individual researchers at single institutions often lack sufficient crop diversity and geographic variability to adequately demonstrate broad UAS utility. Having a formal mechanism, such as this multistate project, will minimize this limitation through leveraging shared expertise and resources. Regulatory frameworks and public perception also influence the adoption of UAS. As the Federal Aviation Administration and state agencies evolve rules governing UAS equipment and operations, stakeholders require clear guidance and protocols to ensure safe, efficient, and responsible use of these technologies. In this context, Renewing the S1069 project will provide a platform to engage across states and agencies, evaluate policy impacts, and help reduce regulatory challenges. There is increasing demand for a workforce skilled in UAS operation, data processing, and analytics. Training must innovate and expand to go beyond students and professionals to include extension and outreach personnel, who play a key role in connecting research with producers. By equipping these intermediaries with UAS expertise, the project can speed up technology transfer, boost adoption, and build producer confidence, greatly expanding its impact across agricultural systems and regions. This S1069 multistate project will continue to help organize training programs and strengthen the development of a skilled workforce.

Upon renewal, this project will continue to bring together multidisciplinary expertise in engineering, agronomy, natural resources, geospatial science, and extension. The project’s growth has expanded beyond the region it was conceived in, as evidenced by membership. The project’s continuation will expand the scientific basis for UAS applications, accelerate technology transfer, and strengthen outreach through coordinated extension programming and producer demonstrations. It will also provide vital workforce training opportunities for students, professionals, and extension personnel, ensuring that the next generation of agricultural and natural resource leaders, along with those who serve them, are fully prepared to use these technologies effectively.

Related, Current and Previous Work

The S1069 multi-state project has established itself as a collaborative leader in the advancement and application of UAS for agriculture, natural resources management, and precision breeding. The collective efforts of S1069 have already catalyzed substantial progress across research, extension, education, and industry engagement, as recognized by the 2022 National Excellence in Multistate Research Award from APLU. Importantly, the S1069 team has begun addressing the challenge of managing increasingly complex and voluminous datasets by building robust data management frameworks and user-friendly web interfaces for UAS data analysis, storage, and sharing within certain areas. The example led by Bhandari for the wheat community provides infrastructure allowing scalable research and facilitates practical adoption by stakeholders. The scientific progress achieved through these collaborations has enabled the generation of valuable datasets, software tools (Killian et al., 2025, Chatterjee et al., 2024), and patentable technologies (Murray et al., 2025), equipping researchers and practitioners with advanced methodologies to further push the boundaries of UAS applications in agriculture. The following sections outline various research efforts and include key examples of progress achieved by S1069 members.

 

UAS-based High-Throughput Phenotyping Pipelines

Working together in both small and large groups, S1069 members have pioneered new approaches of phenotyping by using UAS platforms and sensors including RGB, LiDAR, Hyperspectral, and multispectral. The use of multispectral, hyperspectral, and thermal sensors further enhances the precision and scope of phenotyping, providing robust datasets that support advanced breeding strategies and management tools. By refining the UAS data collection, georeferencing and radiometric calibration procedures researchers can collect reliable and repeatable data across small plots and large-scale fields (Quino et al., 2022; Quino et al., 2021; Sumner et al., 2021; Sinha et al., 2021; Molaei et al., 2022; Molaei et al., 2023; Bagnal et al., 2023; Czarnecki et al., 2023, Sumnal et al., 2024; Siegfried et al., 2024). Additionally, with the development of software and programs, efficient pipelines are developed to generate orthomosaics and digital surface models from raw images enabling the extraction of key plant phenotypic features including height, canopy cover, and several vegetation indices (VIs) (Matial et al., 2021; Wilber et al., 2022, Killian et al., 2025). Due, in part, to user-friendly tools, researchers are routinely obtaining 40-50 different VIs that can be evaluated to explain physiological responses in growth of the plants and genetics.

UAS Applications in Agronomy and Crop Management

UAS-derived data provides valuable insights into crop growth, development, and stress assessments, enabling a more comprehensive understanding of plant responses throughout the growing season. The ability of UAS platforms to repeatedly monitor fields throughout the season enables detailed tracking of canopy development, plant growth, and spatial variability in crop performance (Khuimphukhieo et al., 2025; Sarkar et al., 2025; Pal et al., 2025). These temporal observations can be used to estimate biomass accumulation (Sinha et al., 2021; Sarkar et al., 2021; Sharma et al., 2022; Dhakal et al., 2023) and predict crop yield across multiple cropping systems, including groundnut (Sie et al., 2022), maize (Adak et al., 2021; Khan et al., 2023; Maimaitijiang et al., 2023), sugarcane (Khuimphukhieo et al., 2025), and cotton (Reddy et al., 2024; Lei et al., 2024; Siegfried et al., 2024). Increasingly, UAS applications are expanding beyond annual row crops to perennial horticultural and fruit production systems. For example, multispectral imagery has been used to quantify apple canopy attributes such as photosynthetically active radiation interception and leaf area index (Chandel et al., 2022), while vegetation indices including NDVI and SAVI have proven useful for assessing canopy vigor and spatial variability for precision input management. Similarly, UAS platforms have been used for growth monitoring and feature quantification in blackberry production systems (Tagoe et al., 2024). In addition to these applications, UAS-based imagery and derived vegetation indices have been widely used to detect and quantify biotic stresses such as nematode infestation in soybean (Jjagwe et al., 2024; Jjagwe et al., 2023), leaf rust in wheat (Raman et al., 2025; Bhandari et al., 2021), wheat stem sawfly damage (Ermatinger et al., 2024), late leaf spot and groundnut rosette virus in peanut breeding programs (Chapu et al., 2022; Jigawe et al., 2023), tomato leaf diseases (Li et al., 2023), rust and senescence in maize (DeSalvio et al., 2022), powdery mildew in apple orchards (Chandel et al., 2021), and leaf wilting phenotypes in peanut (Vennam et al., 2026). UAS measurements have also been used to assess abiotic stresses, including drought stress in St. Augustine grass and winter wheat (Rockstad et al., 2023; Bhandari et al., 2022). Collectively, these applications demonstrate the potential of UAS technologies to capture crop growth dynamics and stress responses at high spatial and temporal resolution, providing actionable information to support data-driven management decisions.

 

Plant Breeding

 

Seeing the potential of UAS tools to advance crop improvement goals, many plant breeding programs have begun collecting imagery routinely over their breeding plots, even if they currently lack the tools to make full use of this data. Similarly, UAS has transformed integration of phenomics and genomics into crop improvement, overcoming a major limitation in genomic dissection and prediction: the scarcity of high-quality, temporally rich phenotypic data. The ability to collect high spatio-temporal resolution imagery throughout the growing season enables repeated, non-destructive measurement of canopy structure, spectral reflectance, growth dynamics, and senescence across varieties. These temporally resolved traits provide biologically meaningful intermediate phenotypes that strengthen the connection between genotypes and final agronomic performance (e.g. yield). Recent studies illustrate the value of this integration. Adak et al. (2025) developed a computational framework combining UAV-based phenotyping, logistic growth modeling, and genomic information to predict maize senescence. By modeling genotype-specific growth trajectories derived from repeated UAV measurements, the study demonstrated improved prediction of senescence timing and performance. Similarly, Adak et al. (2024) showed that incorporating UAS-derived traits such as canopy height, vegetation indices, and growth rates significantly enhanced phenomic and genomic predictions for grain yield and plant height across years in maize, improving prediction stability under varying environmental conditions. In winter wheat, Kaushal et al. (2024) demonstrated that integrating high-throughput phenotyping with deep learning further strengthened phenomic and genomic prediction accuracy by extracting complex canopy and stress-response features from multi-temporal UAV imagery. A central advancement enabled by UAS is the ability to capture dynamic (temporal, overtime) trait expression rather than relying on single end-of-season measurements. Temporal trajectories such as vegetation index curves, canopy height progression, and senescence patterns can be summarized into growth parameters and incorporated as covariates in genomic prediction models or by themselves where genomic information is not available. This integration improves modeling of genotype × environment interactions, enhances early-season prediction of end-of-season traits, and increases prediction robustness across environments and years. Collectively, these studies demonstrate that UAS-enabled phenotyping enhances not only throughput but also the dimensionality and biological relevance of phenotypic data. Especially intriguing are opportunities to develop new phenotypes of biological relevance that have never been conceived or possible. This includes latent phenotypes (Ubbens et al. 2020), texture analysis (Mohan and Peeples, 2024) and other attempts to saturate the phenome (Murray et al. 2022). Importantly, these novel features can be applied retroactively to old image data to improve predictions. We are beginning to see that deep phenomic features may complement or even replace genomic prediction in some research programs (Adak et al. 2023).  As with the founding of genetics (Mendel) and statistics (Fisher ), this work to develop a new area positions agricultural plant sciences to lead to new understandings of biology and the ability to predict of end-of-life traits, with fish now providing a validation that such approaches, as in plants, will work for vertebrates (Bedbrook et al. 2026).

 

Weed science and management

 

UAS are rapidly transforming weed science by enabling high-resolution, timely monitoring of weed populations and supporting precision management strategies. Equipped with RGB, multispectral, or hyperspectral sensors, UAS can capture detailed imagery that allows for the detection, classification, and mapping of weeds within crop fields at fine spatial scales (Etienne et al., 2021; Ahmad et al., 2021; Betitame et al., 2025). Efforts have also been made to develop imagery databases with easy access and development of AI algorithms (GC et al., 2024) Advances in machine learning and computer vision have further enhanced the ability to distinguish weeds from crops and quantify infestation levels across large areas (Tosin et al., 2025). These spatial weed maps can inform site-specific management approaches, such as targeted herbicide applications, mechanical control, or early intervention in emerging patches (Upadhyay et al., 2024). However, real-time detection and spraying remain technically challenging and require further development. Additional research is needed to improve real-time sensing and decision-making capabilities, as well as to evaluate spray drift, droplet deposition, and payload limitations of UAV sprayers. Addressing these factors will be critical for improving application accuracy, ensuring environmental safety, and accelerating the broader adoption of UAS-based weed management technologies.

 

Livestock, Aquaculture and Forestry

The versatility of UAS extends beyond high-throughput phenotyping (HTP) and weed management, offering impactful applications in livestock monitoring, aquaculture, and forestry. In the case of livestock management, UAS platforms have enabled real-time, remote monitoring of livestock, offering improvements in herd management, animal health assessment, and rapid response to grazing patterns and environmental threats.

Commercial aquaculture systems operate in hypereutrophic (enriched in nutrients and characterized by frequent and persistent algal blooms ), lentic environments that are highly susceptible to cyanobacterial blooms. These blooms can produce off‑flavor compounds and cyanotoxins that negatively affect cultured species, and even sublethal toxin exposure may compromise immune function, increasing vulnerability to opportunistic pathogens (Marchant 2022). Seasonal patterns of cyanobacterial bloom development have been linked to higher disease incidence, emphasizing the importance of monitoring bloom dynamics across production cycles (Marchant 2022). Yet managing these blooms remains challenging: water quality can vary widely from pond to pond, making uniform mitigation strategies unreliable, while traditional interventions such as water exchange or chemical treatments are costly, labor‑intensive, and often inadequate for long‑term control. As a result, aquaculture operations increasingly require site‑specific monitoring frameworks that allow producers to detect emerging bloom conditions early, apply targeted interventions, and reduce bloom‑related health and production losses. To address these challenges, UAS equipped with multi‑ and hyperspectral imaging has become a promising tool for precision aquaculture. UAS‑based hyperspectral imagery (HSI) provides rapid, high‑resolution assessments of water quality, enabling producers to detect cyanobacterial pigments, track bloom progression, and identify ponds at greatest risk for toxin production or associated disease outbreaks (Olivetti et al. 2023). This technology supports proactive, pond‑specific decision‑making by highlighting bloom hotspots and revealing spatial and temporal changes that cannot be easily captured through conventional sampling. Recent studies further demonstrate that UAS remote sensing reliably detects harmful algal blooms (HABs) in aquaculture ponds and other small waterbodies, greatly strengthening the ability of producers and managers to implement timely, targeted, and cost‑effective bloom mitigation strategies (Lekki et al. 2019; Hong et al. 2021).

Training, Extension, and Outreach:

The project has produced comprehensive UAS training materials and organized hands-on workshops for graduate students, growers, and government personnel, including international collaborations. To date, more than 100 students have been trained, and thousands of stakeholders have been engaged through field days, presentations, and media coverage. Results and innovations are widely disseminated via peer-reviewed publications, industry magazines, and conferences, achieving substantial citation rates and international readership. Core outreach efforts have empowered stakeholders across multiple states to appropriately adopt UAS technologies, effectively bridging research and real-world applications. Media coverage, including ASABE Resource Magazine, has extended the project’s impact to audiences in over 100 countries. One key opportunity was supporting ten graduate students and early-career scientists to attend the 2023 S1069 annual meeting in Virginia, funded by an Agriculture Genome to Phenome Initiative (AG2PI) grant. Led by Drs. Seth Murray and Mahendra Bhandari (Texas A&M), S1069 members submitted a proposal titled “Facilitating community unoccupied aerial systems (UAS) knowledge, communication, and data processing.” The initiative organized discussions on major unsolved topics including UAS data organization and storage, data lifecycle, extension applications, open-source software, and best practices for data ownership and sharing to advance technical standards for the community. Additional work includes the development of curricula and professional development programs, including graduate-level courses at Purdue University (BYSE551: UAS in Agriculture) and university-wide robotics team projects, ensuring that academic institutions continue to produce skilled personnel equipped to lead in the evolving landscape of agricultural automation and data science. Texas A&M has developed a UAS manual with step-by-step guidance on UAS calibration, georeferencing, flight mission planning, image processing, phenotypic feature extraction, and GIS-based visualization. This manual is being used by researchers, extension specialists, and consultants as a guide to use UAS in agriculture.

 

Impact on Research Directions, Funding, and Adoption

S1069’s collaborative efforts have driven the adoption of new methods, inspired multi-state collaborations, and enabled competitive grant proposals. Highlights include over $10 million in awarded grants from USDA-NIFA, NSF, USAID, and industry partners, supporting research in precision agriculture, phenotyping, and drone applications for multiple crops. The project has produced numerous peer-reviewed publications, presentations, and patent filings. Notable contributions include the ArcGIS UAV Toolbox for image analysis, patented NDVI/NDRE models, weather mapping systems, and unmanned vehicle designs. Practical impacts are evident in the commercialization of drone-centric crops and water management solutions. Industry adoption is robust, with growers integrating UAV imaging, spraying, and AI-driven decision tools into daily operations.

Objectives

1. 1. To research and develop UAS-based remote sensing systems and methods for field phenotyping and characterization in crops, livestock, forests, and aquaculture.
   Comments: Foundation: How to collect data
2. 2. To develop robust UAS data storage, access, and management systems to support efficient use and sharing in agriculture and natural resources.
   Comments: Infrastructure: How to handle/share data
3. 3. To research and develop UAS-based systems and methods for precise economic and environmental management of inputs in production of crops, livestock, forests, and aquaculture
   Comments: Application: How to use data in practical ways
4. 4. To assess stakeholder needs and produce classroom and extension education and training resources for UAS-based monitoring and management technologies for agriculture and natural resources
   Comments: Outreach: Ensure adoption and skill development

Methods

Objective 1: To research and develop UAS-based remote sensing systems and methods for field phenotyping and characterization in crops, livestock, forests, and aquaculture.

Over the past several years, advances in UAS technologies have greatly enhanced field-based phenotyping and characterization of crops and forests, with emerging applications in livestock and aquaculture. Future development of streamlined procedures, robust protocols, and scalable tools will enable new applications and make UAS deployment routine and widely scalable.

Members of the S1069 project will collaboratively refine and expand UAS-based remote sensing systems and analytical methods to ensure that phenotyping and characterization in crops, livestock, forests, and aquaculture are reproducible, scalable, and transferable. Given recent foreign manufacturer restrictions in the U.S. affecting widely used UAS platforms and the increasing reliance on U.S.-manufactured drones, the group will evaluate alternative hardware configurations, sensor integrations, and autopilot systems to ensure continuity of high-quality data collection. This effort will include comparative assessments of image quality, payload flexibility, reliability, and cost-effectiveness, thereby producing adaptable and resilient recommendations for public research programs. Engagement with hardware and software manufacturers will be pursued where appropriate. In addition to refining and advancing our data collection efforts, we will continue to make significant improvements to data processing workflows to transform raw imagery into analysis-ready datasets. Barriers to standardization and solutions will be explored. Emphasis will be placed on developing shared, open, and well-documented pipelines that can be deployed across institutions to the extent possible, thereby facilitating consistent extraction of phenotypic traits and feature characteristics to characterize crops, livestock, forestry, and aquaculture. As the volume and quality of UAS-derived data continue to grow rapidly, artificial intelligence (AI) and machine learning (ML), including foundational models, present transformative opportunities to advance high-throughput phenotyping, predictive analytics, and decision support across diverse agricultural and natural resource systems. Members of this project will help lead the development of interoperable data standards and shared repositories to support model development and reproducibility. Efforts will focus on multimodal data integration, combining UAS-derived phenotypic traits with genomic marker data, weather records, and soil characteristics to understand genotype by environment interactions and support crop breeding. Additionally, these approaches will extend to livestock, forestry, and aquaculture systems, incorporating integration of water quality parameters, environmental data, and management information to enable precise assessment of forage quality and quantity for grazing systems, livestock growth and health monitoring (e.g., via thermal imaging for stress or behavioral phenotyping), key forestry metrics (e.g., tree height, canopy cover, biomass estimation, and early stress detection), and harmful algal bloom (HAB) monitoring and forecasting in aquaculture ponds and natural water bodies (e.g., deriving proxies such as chlorophyll-a and phycocyanin concentrations from multispectral imagery to predict bloom risks and toxin dynamics). Throughout the project period, members will develop and publish calibration protocols, standard operating procedures, and benchmarking datasets to ensure transparency and reproducibility. Research teams, while forming organically around specific systems or questions, will incorporate broad disciplinary expertise, including agronomy, animal science, aquaculture, economics, engineering,  forestry, genetics, horticulture, information technology, legal considerations modeling, pest management, plant breeding, and statistics related to UAS deployment. Engagement with hardware and software manufacturers will be pursued where appropriate to accelerate innovation and facilitate technology transfer. Results will be disseminated through peer-reviewed publications, presentations at national and international professional meetings, and collaboration with related multistate projects to promote synergy and avoid duplication of effort, and through on‑farm implementation and aquaculture producer engagement to ensure practical adoption in the field.

           

2. To develop robust UAS data storage, access, and management systems to support efficient use and sharing in agriculture and natural resources.

The rapid advancement of UAS-based sensing technologies across crops, livestock, forests, and aquaculture/mariculture has generated unprecedented volumes of high-resolution spatial and temporal data. These datasets span the full processing pipeline, from raw imagery and point clouds, through intermediate photogrammetric products, to extracted features and interoperable analysis-ready maps and derived phenotypic traits. Many are AI ready; many others need to be AI ready. As multi-state collaborations expand and datasets become larger and more complex, the absence of coordinated data storage, access, and management systems poses a major limitation to reproducibility, interoperability, and long-term scientific value. Under this objective, members of S-1069 will design and implement robust, scalable, and interoperable data infrastructures that enable efficient storage, standardized documentation, secure sharing, and long-term stewardship of UAS-derived datasets for agriculture and natural resource systems. A central motivation for this effort is the recognition that while UAS technologies are widely viewed as valuable, structural barriers limit broader adoption and efficient use. A survey conducted by our members reported that more than 80% consider UAS-based phenotyping valuable, yet fewer than half actively use UAS systems in their research (Lachowiec et al., 2024). Primary barriers included high costs for software/instrumentation and complexities in data processing and management. To address these needs, S-1069 members will collaboratively establish standardized data architectures that define how UAS data are organized, annotated, versioned, and archived. Common data schemas will be developed to structure raw imagery, intermediate photogrammetric products, derived features, and model outputs in a consistent and machine-readable format. These metadata standards will ensure traceability and reproducibility across years, sites, and institutions. Need for data interoperability will be a guiding principle in system design. Data management frameworks will be constructed to integrate seamlessly with complementary datasets, for example genomics data, weather and climate archives, soil databases, and management records. This integration will enable cross-domain analyses and support emerging artificial intelligence and multimodal modeling approaches. Members will evaluate both centralized and federated repository models to balance accessibility, institutional autonomy, and data security. Cloud-based storage and high-performance computing environments will be leveraged where appropriate to facilitate large-scale processing and collaborative access while maintaining compliance with institutional and federal data governance policies. Reproducibility and workflow transparency will be strengthened through development of documented, version-controlled data pipelines. Given the increasing scale of UAS-derived datasets, attention will also be directed toward efficient data indexing, compression, and retrieval strategies. Methods for tiling, hierarchical storage management, and automated quality-control checks will be evaluated to optimize performance while minimizing storage costs. Where appropriate, machine-readable APIs will be developed to allow secure programmatic access to datasets for modeling and analytics applications. These systems will be designed to support both human interpretation and AI-driven analyses, ensuring that curated datasets are structured in ways that facilitate development of predictive and foundational models. Data governance and access policies will be established collaboratively among participating institutions to define permissions, attribution standards, and data-sharing agreements. These policies will encourage responsible sharing while protecting sensitive information and respecting institutional and stakeholder constraints. Engagement with other multistate projects will be initiated to promote interoperability and alignment of standards, reducing fragmentation across national UAS research efforts. Teams within S-1069 will work across disciplinary boundaries, incorporating expertise in information technology, computer science, engineering, agronomy, forestry, aquaculture, modeling, and data science to ensure that data systems are technically robust and biologically meaningful. Collaborations with cloud data management companies, software developers, data analytics providers, and agricultural technology companies will be actively explored.

 

3. To research and develop UAS-based systems and methods for precise economic and environmental management of inputs in production of crops, livestock, forests, and aquaculture

The research under this objective will advance UAS-based systems from monitoring tools to fully integrated management technologies that deliver measurable economic returns and environmental benefits across crops, livestock, forests, and aquaculture. Despite progress in sensing and stress detection, producers often question whether UAS technologies justify their cost in real-world production systems, including challenges such as quantifying return on investment for crop monitoring and detecting off-flavor toxins in aquaculture affecting fish health. This objective will explicitly link UAS sensing capabilities to actionable, site-specific management interventions and rigorous economic evaluation to determine when and where UAS integration is financially and environmentally justified. A central component of this work will involve developing and validating use protocols and best practices that connect UAS-derived information to precise input management decisions. Through synthesis of ongoing and newly initiated collaborative projects across participating states, members will evaluate the suitability, scalability, and system-level impacts of UAS-based technologies in diverse production environments, including row crops, specialty crops, livestock systems, freshwater aquaculture, mariculture, forests, and other managed ecosystems. Protocols will be customized for specific applications such as crop stress quantification, nutrient deficiency detection, weed mapping, pest and disease identification, irrigation management, livestock heat stress mitigation, forest health monitoring, and monitoring and predicting off-flavor or toxic algal blooms in aquaculture. Emphasis will be placed on spray drone technologies and their integration into site-specific management systems. Members will investigate operational performance, deposition efficiency, and environmental safety of UAS-based spray applications for foliar nutrients, plant growth regulators, pesticides, and herbicides. Experimental and modeling approaches will examine how platform attributes, including rotor number and configuration, payload weight, flight speed, and downwash airflow interact with canopy structure to influence droplet deposition, coverage uniformity, and drift potential. Spray applicator characteristics, such as nozzle type, droplet size, pressure, and boom configuration, will be optimized to maximize efficacy while minimizing off-target losses. Weed management applications, including precision spot-spraying based on high-resolution weed maps, will be assessed for their potential to reduce chemical use and mitigate herbicide resistance.

            Aquaculture systems face similarly complex production constraints. Although cyanobacterial blooms are common in pond‑based operations during the summer, the underlying algal community is highly diverse and undergoes rapid shifts in composition and density (Paerl and Tucker 1995; Bassey et al., 2025). These dynamics make it difficult to quantify the multifactorial effects of algae on water quality, fish health, and overall production performance. The capacity to anticipate the off‑flavor toxin–producing blooms would enable more timely and targeted remediation strategies, improving both fish health, quality, and production efficiency during critical phases of commercial aquaculture operations. These biological and water quality challenges underscore the need for advanced UAS based sensing, modeling, and decision‑support tools capable of supporting timely management decisions while accounting for pond‑level variability to reduce the risk of production loss.

             Multi-state field trials will compare conventional uniform input applications with UAS-guided site-specific strategies to quantify differences in yield, input use, labor efficiency, and operational costs. Economic analyses including capital, maintenance, software, regulatory, and labor costs will determine return on investment and identify scenarios where UAS integration is advantageous. Environmental outcomes such as reductions in nutrient losses, pesticide drift, water use, and non-target impacts will be evaluated alongside economic performance. Interdisciplinary collaboration will integrate expertise from agricultural engineering, agronomy, horticulture, forestry, aquaculture, pest management, atmospheric modeling, economics, and regulatory policy. Efforts will strengthen representation from agricultural economists within S-1069 to ensure rigorous economic modeling. Engagement with UAS hardware manufacturers, spray system developers, and software engineers will facilitate iterative improvements in platform and applicator design. Collectively, these efforts aim to generate broadly applicable evidence demonstrating that well-designed UAS practices can enhance profitability while reducing environmental externalities.

 

Objective 4: To assess stakeholder needs and produce classroom and extension education and training resources for UAS-based monitoring and management technologies for agriculture and natural resources.

To assess stakeholder needs and develop effective classroom and extension education and training resources for UAS-based monitoring and management technologies in agriculture and natural resources, project members will actively engage a broad range of stakeholders, including producers, industry representatives, technology developers, researchers, educators, and extension professionals. Multiple approaches will be used to collect stakeholder input, including structured online surveys (Lachowiec et al. (2024), Ibrahim et al. (2021)), in-person surveys conducted during extension and outreach events, focus groups, and feedback obtained through workshops and professional meetings. These efforts will help identify current levels of UAS awareness and adoption, key knowledge gaps, training priorities, and barriers to the effective use of UAS technologies for agricultural and natural resource monitoring and management. Information gathered through these stakeholder engagement activities will guide the development of targeted education and training materials that address both foundational and advanced aspects of UAS applications. Project members will develop a suite of adaptable educational resources designed for use in university classrooms, extension programming, and professional training settings. These resources will include modular course materials, editable lecture presentations, laboratory and hands-on training exercises, extension fact sheets, technical guides, and instructional videos. Educational content will cover topics such as fundamentals of UAS platforms and flight operations, sensor technologies and sensing principles, sensor integration and calibration, data acquisition protocols, image processing and data management workflows, and the interpretation of UAS-derived information for decision-making. Additional modules will address practical management applications across cropping systems, forestry, rangelands, and aquatic systems, as well as relevant regulatory considerations, including Federal Aviation Administration (FAA) Part 107 rules, operational safety, and data governance. To ensure broad accessibility and long-term utility, educational materials will be designed to be customizable and adaptable across regions, cropping systems, and institutional contexts. Project members will collaborate with extension educators and teaching faculty to pilot and refine these resources in classroom instruction, extension workshops, and field demonstration activities. Materials will be disseminated through the S1069 project website in NIMMS, participating in university extension platforms, regional extension networks, and professional training programs. The project will also foster teaching collaborations and learning cohorts among participating institutions to support the continued development and sharing of UAS-focused educational content. Through these coordinated efforts, the project will strengthen workforce development, improve stakeholder capacity to effectively use UAS technologies, and support the broader adoption of data-driven monitoring and management practices in agriculture and natural resource systems

Measurement of Progress and Results

Outputs

• New software and/or computer code for analyses of UAS data for agriculture, aquaculture, and natural resources Comments: The number of released new software packages/code and presentations/publications using new software or code are both measures of progress in this area, the latter needing greater resources to measure. Efforts will be made to coordinate code development, validation and usage across institutions; and where possible, software will be made open and accessible.
• New hardware/software systems and protocols (best practices) for consistently efficient and effective use of UAS for agriculture, aquaculture, and natural resources. Comments: The number of released new hardware/software systems and protocols can be measured through presentations and publications. Efforts will be made to coordinate protocols/best practices development, joint hardware/software system prototyping and usage across institutions and use cases.
• New applications for efficient and effective use of UAS for agriculture, aquaculture, and natural resources Comments: Sharing of research/education efforts by members will occur during multi-state annual meetings. This activity will stimulate inquiry regarding explored UAS technology (platform, sensors) and associate data analysis protocols with new unexplored applications.
• New findable, accessible, interoperable, and reusable datasets to support the development and validation of novel software and pipelines Comments: The number of publicly available datasets with associated digital object identifiers will be a measure of progress in this area.
• Framework for consistent data and metadata formats to be adopted for data sharing to enable collaborative research Comments: Efforts will be made to coordinate data usage across institutions to the extent possible for these activities. Multiple individuals and efforts have worked towards developing consistent data and metadata formats on UAS already, but this is a huge undertaking for the larger community, and this project can only provide guidelines for its members. Progress towards this will be measured by the completion of a focused discussion of current approaches for data sharing during one annual meeting. An additional measure will be the collection of community responses to survey questions (contingent on funding) on data sharing to establish baseline actions.
• Scientific publications validating the utility of UAS and specific protocols for applications Comments: Efforts will be made to encourage coordinated development of multi-state research and extension projects, implementation, and co-authorship of peer-reviewed publications. These can be measured as counts and citations, although additional resources might be needed for adequate tracking.
• Teaching modules and training materials for extension education/outreach, including on‑farm implementation on UAS in agriculture, aquaculture, and natural resources Comments: Efforts will be made to coordinate training material development and usage across institutions. To ensure practical adoption in their respective fields, these materials will be reported and shared at our multi-state annual meeting and implemented by our members. These are less likely to be published or communicated than research accomplishments, so additional effort will be needed to encourage members to report on this activity.

Outcomes or Projected Impacts

• Using the outputs above by the greater community of UAS users in agriculture will be an important outcome and realize the largest impacts of the project Usage can be tracked by proxy through downloads, workshops held, or social media. Ultimately citations will be among the most reliable measures, but citations can lag by years and recognizing future authors do not always appropriately attribute software, teaching resources or ideas developed by a project like this. Citations of scientific publications associated with this project will be tracked. Efforts will be made to collect and track meaningful metrics of all outputs.
• Lowering the UAS technology adoption barriers to entry for UAS in agriculture and natural resources In the previous project cycle, we completed a survey of adoption of UAS by agricultural researchers (Lachowiec et al., 2024). We will relaunch this survey to use for assessing changes in adoption
• Accelerated use of UAS in research in agriculture and natural resources, resulting in faster progress in the improvement of plants and animals. Efforts will be made to collect and track meaningful metrics.
• Improved decision making by researchers, improved profitability and sustainability for growers, catalyzing additional new software, hardware and technologies Efforts will be made to collect and track meaningful metrics.

Milestones

(2027):Establish at least two small, focused multistate extension teams that deliver measurable outputs, including at least two grower training workshops, one extension fact sheet per team, and recorded training materials disseminated via YouTube and the project website.

(2027):Establish at least two small, focused multistate research teams within S1069 (and/or partner projects) on UAS applications in agriculture, each producing at least one coordinated output (e.g., a joint grant proposal, multi-location dataset, or co-authored publication) focused on remote sensing and precision input application.

(2027):Submit a proposal to fund a needs assessment for research based on survey of researchers and practitioners in the field of UAS in agriculture and natural resources. An example would be an SCRI planning grant to fund the needs assessment in specialty crops.

(2028):Completed needs assessment for formal classroom instruction and extension/outreach training materials based on survey of extension personnel and practitioners in the field of UAS in agriculture and natural resources.

(2029):At least one submitted project proposal for collaborative development of extension/outreach training materials from corporate, state, and/or federal sources.

(2029):Complete the needs assessment for research based on survey of researchers and practitioners in the field of UAS in agriculture and natural resources and compare to the previous.

(2029):At least one set of extension/outreach training material to meet the needs elucidated from the needs assessment conducted in earlier years.

(2030):At least three submitted multi-university research proposals to address needs elucidated from the assessment conducted in earlier years.

(2030):At least one customizable course curriculum (up to 3 credits) and pertinent education material/modules development to meet the needs elucidated from the assessment conducted in earlier years.

Projected Participation

View Appendix E: Participation

Outreach Plan

The group meets annually to exchange achievements, share information, and discuss plans, including outreach activities. These meetings are typically held independently of scientific conferences, as the multidisciplinary nature of the group does not align well with any single existing conference. This activity will continue to serve as a catalyst for disseminating objective-specific results through multiple channels, including peer-reviewed and extension publications, CRIS reports, web platforms, and the organization of targeted outreach activities. The group’s membership is diverse, not only in terms of contributions to project objectives but also in expertise and professional roles, including research, teaching, extension, or integrated appointments. As a result, outreach efforts under this proposal will take multiple forms, tailored to effectively reach a broad range of stakeholders, including producers, students, researchers, extension personnel, and industry partners. For example, in 2025, three workshops were conducted in Virginia and Texas focusing on UAS applications in agriculture and high-throughput phenotyping. Additionally, several high and middle school programs titled “Drones in Agriculture” were delivered in both states. Similar activities will be implemented across participating locations to further strengthen outreach efforts and promote the adoption of UAS technologies in agriculture.

Organization/Governance

The project governance structure will consist of a Chair, Chair-elect, and Secretary, each serving a one-year term. At the conclusion of each annual meeting, the Chair-elect will assume the role of Chair, the Secretary will become Chair-elect, and a new Secretary will be selected by the project membership in attendance. These three officers together will form the project Executive Committee. At the first annual meeting, project members will determine whether Objective Coordinators are needed for each of the four objectives. If selected, Objective Coordinators will serve a minimum two-year term and will also join the Executive Committee. All project members will meet face-to-face at least once per year, either as a stand-alone meeting or alongside a relevant conference. Members working on individual objectives may hold periodic conference calls to coordinate activities, and the Executive Committee may schedule additional calls as needed.

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Attachments

Land Grant Participating States/Institutions

Non Land Grant Participating States/Institutions