Progress report for FNE25-108
Project Information
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Develop and Test a Dual-Purpose Robotic System for Vineyard Bird Control and Mowing
- Design and build a robotic system capable of autonomously detecting and deterring birds from vineyards while simultaneously mowing vineyard rows.
- Measurable Outcome: The robotic system will successfully complete a full vineyard row with no damage to the vines or soil and deter 80% of bird activity within the test area, as measured by bird counts and crop damage assessments.
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Evaluate the Efficiency and Cost-Effectiveness of the Robotic System for Small to Medium-Sized Vineyards
- Assess the operational efficiency of the robotic system compared to traditional bird control and mowing methods, focusing on labor and fuel savings, and the potential for increased vineyard productivity.
- Measurable Outcome: Demonstrate at least a 30% reduction in labor costs and a 20% decrease in fuel usage compared to conventional methods for bird control and mowing over a three-month test period.
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Adapt the Robotic System to Northeast Vineyard Environments
- Customize the robotic system for the unique conditions of Northeast vineyards, including narrow rows, uneven terrain, and varying grape varieties, to ensure its practical use in these environments.
- Measurable Outcome: The robot will operate effectively in Northeast vineyards with varied terrains, meeting the specifications of vineyard owners and farmers in the region.
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Measure the Environmental Impact of the Robotic System
- Evaluate the environmental benefits of using the robotic system, particularly its ability to reduce carbon emissions and reliance on chemical or fuel-based bird control and vegetation management methods.
- Measurable Outcome: Achieve at least a 15% reduction in carbon emissions and a 25% reduction in the use of chemical deterrents or fossil fuels compared to conventional methods.
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Increase Adoption of Robotic Systems Among Northeast Vineyard Owners
- Increase awareness and adoption of the robotic system by Northeast vineyard owners, particularly small to medium-sized farms, through field demonstrations, workshops, and educational outreach.
- Measurable Outcome: Engage at least 50 vineyard owners through outreach activities, and achieve a 15% adoption rate of the robotic system within the first year after project completion.
Problem Statement
In the Northeast United States, vineyard owners face persistent and growing challenges in managing bird damage and vegetation maintenance. Birds, particularly during harvest time, can cause significant crop loss, severely affecting the profitability and sustainability of vineyards. Traditional methods of bird control, such as netting, scare devices, and even human presence, are often ineffective, expensive, or labor-intensive. Similarly, the task of mowing between vineyard rows to maintain ground health and prevent weed competition is both time-consuming and costly, with labor shortages in agriculture exacerbating the problem.
Additionally, as vineyard management requires substantial resources-such as fuel, labor, and machinery-the environmental impact of traditional farming practices, particularly fossil fuel use and pesticide application, is a growing concern. Sustainable agricultural practices that improve efficiency while reducing environmental impact are increasingly important to farmers who are looking for more sustainable and economically viable farming solutions.
Why the Problem Matters
The challenges outlined above are not isolated to a few farms; they are widespread concerns across the Northeast agricultural landscape, particularly for vineyard owners and small farmers who are committed to adopting more sustainable practices while maintaining profitability. Bird damage alone can result in significant crop losses-up to 30% or more of total yield in some regions, depending on the severity of the bird infestation. At the same time, mowing is a routine and critical task in vineyards. Invasive weeds, excessive vegetation, and poor ground cover management can hinder grape production, reduce soil health, and contribute to pest and disease problems.
The Need: Evidence of Interest and Demand
There is significant interest among Northeast farmers for technologies that can address these persistent challenges. Numerous surveys and conversations with vineyard owners indicate a strong desire for solutions that are labor-saving, cost-efficient, and sustainable. For example, in a recent survey conducted by the Northeast Vineyard Association, over 60% of vineyard owners reported significant issues with bird damage to their crops, and more than 80% expressed interest in automated or robotic solutions to help with pest control and ground maintenance.
At the same time, farmers are feeling the effects of labor shortages-both in terms of the availability of workers and the rising costs of labor. A report by the Northeast Agricultural Labor Association highlighted that nearly 50% of small to mid-sized farms in the region are struggling to find seasonal workers, and more than 70% are seeking innovative technologies to replace or supplement labor-intensive tasks.
Farmers are also increasingly aware of the need to adopt sustainable farming practices. A study by the Cornell Cooperative Extension found that nearly 70% of vineyard owners in the Northeast are actively seeking ways to reduce their environmental footprint by using less fuel, reducing pesticide use, and adopting more energy-efficient systems. Additionally, environmental changes and their associated risks, such as extreme weather and unpredictable growing seasons, have prompted a shift toward more resilient, resource-efficient, and economically viable solutions in vineyard management.
Project Approach:
- Technical Plan of Work
- Robotic Design and Prototyping: The first step in the project will involve the design and prototyping of the robotic system, focusing on ensuring that it is both lightweight and durable, with the ability to withstand the vineyard environment. We will test different sensor technologies for both autonomous navigation and bird deterrence, evaluating their effectiveness in real-world vineyard conditions. The robot will be designed to be easy to maintain and repair, minimizing downtime.
- Integration of Bird Control and Mowing Systems: The next phase of the project will involve integrating the bird deterrent system and the mowing mechanism into a single robotic platform. Researchers will evaluate various deterrent methods, including ultrasonic devices, motion-activated sound emitters, and visual decoys like reflective tape or spinning objects. We will test different mowing systems, such as rotary and flail mowers, to determine the most effective method for vineyard ground maintenance.
- Field Testing and Optimization: Field testing will be conducted in local vineyards to evaluate the robot's effectiveness in both bird control and mowing. We will assess bird damage reduction, mowing efficiency, and the robot's ability to navigate uneven vineyard terrain. Data will be collected on the robot's performance, and adjustments will be made to optimize its operations. Additionally, we will evaluate the robot's power usage and battery life in vineyard conditions.
- Data Analysis and Decision Support: The robot's data collection capabilities will be tested, and algorithms will be developed to help vineyard managers interpret the data. We will evaluate the feasibility of integrating the robot with existing vineyard management software to provide real-time analytics on pest control and vineyard health.
- Outreach Strategy
- Demonstrations and Workshops: To ensure widespread adoption, the project will organize demonstration events where vineyard owners, managers, and agricultural professionals can see the robot in action. These demonstrations will highlight the robot's dual capabilities in both bird control and mowing. Additionally, workshops will be organized to train vineyard operators on how to use the technology and interpret the collected data.
- Partnerships with Industry Organizations: We will partner with agricultural associations, vineyard groups, and cooperative extension offices to promote the project's findings and outcomes. These partnerships will help us reach a larger audience and increase the project's impact.
- Online Platforms and Publications: Findings from the research and field trials will be shared through scientific journals, industry publications, and online platforms. We will also create video content and case studies showcasing the robot's effectiveness, which will be distributed via social media channels and vineyard management forums.
Conclusion
The integrated Robotic Vineyard Bird Control and Mowing System offers a promising solution to two critical challenges facing modern vineyards: bird predation and the labor-intensive task of mowing. By combining autonomous navigation, bird deterrence, and efficient mowing in a single platform, the robot will reduce labor costs, improve operational efficiency, and minimize environmental impact. Through field trials, data collection, and ongoing outreach efforts, this innovative approach has the potential to revolutionize vineyard management, making it more sustainable, cost-effective, and resilient in the face of modern agricultural challenges.
Cooperators
- - Technical Advisor
Research
Project Methods
This project uses a staged deployment and evaluation approach to design, install, and test an autonomous robotic monitoring system in a working vineyard environment. The methods integrate hardware deployment, sensor-based data collection, edge AI processing, and cloud-based analysis to assess feasibility and operational performance under real agricultural conditions
Step 1: System Installation and Local Deployment (Week 1)
The project begins with on-site installation of the AgriBot quadruped robotic platform within the vineyard. During this phase, the robot’s onboard hardware—including high-resolution cameras, LiDAR, depth sensors, thermal sensors, and GPS—are physically mounted and calibrated to ensure accurate perception and positioning. Calibration is performed row-by-row within the vineyard to align sensor fields of view with vine spacing and terrain variability.
A GPS RTK base station is installed at a fixed reference point to provide centimeter-level positional accuracy. This allows the robot to repeatedly traverse identical vineyard routes, enabling consistent spatial data collection over time. An autonomous charging station is installed and tested to support unattended operation cycles. A local Wi-Fi/LTE gateway is configured to provide redundant connectivity between the robot, the local deployment layer, and the cloud environment
Step 2: Data Collection and Edge Processing (Week 2)
Once deployed, the robot conducts autonomous patrols along pre-mapped vineyard routes. During each patrol, sensor data are collected continuously, including visual imagery, LiDAR point clouds, thermal readings, and GPS position logs. Data sampling occurs in real time as the robot moves, with timestamps and geospatial metadata embedded in each data packet.
Initial analysis occurs on the robot using onboard edge AI processors. These models perform real-time tasks such as obstacle detection, terrain navigation, and environmental anomaly identification. Edge processing minimizes latency and allows the system to operate even during periods of limited or intermittent connectivity
Step 3: Cloud-Based Data Aggregation and Analysis
Sensor outputs and system logs are transmitted to cloud servers when connectivity is available. In the cloud environment, data are aggregated for storage and longitudinal analysis. The analysis process includes reviewing patrol consistency, sensor reliability, thermal performance, and system uptime. A web-based dashboard provides visualization of robot paths, system health indicators, and alert logs, allowing remote monitoring by operators.
Cloud analytics are also used to evaluate AI model performance and identify patterns related to environmental conditions, such as temperature-induced hardware stress. These insights inform iterative model updates and hardware refinement
Step 4: Performance Evaluation and Iteration
Operational data collected during deployment are analyzed to assess system robustness, with particular focus on thermal tolerance and sustained runtime in vineyard conditions. Hardware failures and shutdown events are logged, time-stamped, and correlated with environmental data to identify root causes. Findings guide next-step decisions, including engagement with platform manufacturers and evaluation of alternative robotic systems better suited for extended outdoor agricultural use
Measurements, Observations, and Findings
This project evaluated the real-world feasibility of deploying an autonomous quadruped robotic system for vineyard monitoring and deterrence under active farm conditions. Measurements and observations focused on system performance, environmental tolerance, operational reliability, and potential farm-level impacts related to labor, costs, and management efficiency.
What Was Measured and Observed
During deployment, we measured and logged the following system and operational metrics:
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Robot uptime and runtime duration, including continuous operation time before failure or shutdown
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Thermal performance of onboard computing hardware during daytime vineyard conditions
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Sensor functionality and data continuity, including camera, LiDAR, GPS, and thermal sensor outputs
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Navigation accuracy and repeatability, particularly row-to-row traversal using RTK-enabled GPS
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Connectivity stability, measuring data transmission success over Wi-Fi and LTE under rural conditions
In addition to quantitative system logs, qualitative observations were recorded by operators during daily patrols. These included robot mobility on uneven vineyard terrain, response to obstacles, ease of integration into routine vineyard operations, and the degree of human oversight required.
Key Findings
The system successfully demonstrated high positional accuracy and navigation repeatability. When RTK GPS was active, the robot consistently followed predefined vineyard routes with centimeter-level precision. This confirmed that autonomous ground-based robotics can navigate permanent cropping systems such as vineyards with minimal drift or deviation.
Sensor performance was strong during initial operations. Visual, LiDAR, and thermal sensors provided continuous situational awareness, and onboard AI models successfully handled obstacle detection and autonomous path planning without cloud dependence. Data collected were spatially consistent and suitable for aggregation and future longitudinal analysis.
However, a critical finding of the project was the inadequate thermal resilience of the hardware platform under vineyard summer conditions. Despite following manufacturer-recommended operating parameters, the quadruped robot experienced repeated thermal shutdowns after several hours of outdoor use. A replacement computing unit was installed in an attempt to resolve the issue, but the system continued to fail during extended operation. These failures occurred during typical seasonal heat conditions for the region and were not the result of extreme or unusual weather events.
This outcome uncovered a key limitation: while robotic autonomy and sensor integration are technically viable, current commercial quadruped platforms may not yet be sufficiently ruggedized for sustained agricultural field deployment without improved thermal management.
Deviations from Original Methods
Originally, the project planned for extended multi-day autonomous patrols to evaluate long-term data collection and labor substitution potential. Due to repeated hardware failures, the project pivoted from prolonged operation testing to diagnostic evaluation of system resilience, focusing on identifying the root causes of failure rather than accumulating large volumes of operational data.
This adaptive change allowed the project to generate valuable insights into platform limitations, manufacturer claims versus real-world performance, and the engineering gaps that must be addressed before broader farm adoption is feasible.
Site-Specific and Environmental Conditions
The vineyard environment presented several conditions relevant to the study:
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Direct solar exposure across open rows
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Reflective heat from soil and vine canopy
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Uneven terrain typical of cold-climate vineyards
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Rural connectivity with intermittent network coverage
These conditions are representative of many specialty crop operations in the Northeast and therefore increase the relevance of the findings for other growers considering robotic technologies.
Farm-Level Impacts and Projected Outcomes
While full labor substitution was not achieved during this stage, the project generated several measured and projected farm impacts:
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Labor insight: Even limited autonomous patrols demonstrated the potential to replace routine visual inspections, suggesting future time savings once hardware limitations are resolved.
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Cost avoidance (projected): Early identification of unsuitable platforms prevents premature capital investment by growers, reducing financial risk.
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Operational efficiency: The system showed promise for off-hours monitoring, which could reduce overtime labor costs and increase responsiveness to vineyard issues.
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Input optimization (projected): Sensor-based monitoring may enable more targeted interventions (e.g., pest deterrence or crop stress detection), potentially lowering chemical and material inputs.
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Community and neighbor relations: Autonomous, non-invasive monitoring systems offer a quieter and less disruptive alternative to traditional deterrence methods, with potential benefits for neighboring properties.
Overall Lessons Uncovered
The most significant outcome of this project was the identification of a critical readiness gap between robotic research platforms and practical farm deployment. While autonomy, sensing, and cloud integration are technically mature, hardware durability remains a limiting factor. This finding is essential for growers, researchers, and manufacturers alike and underscores the importance of on-farm testing before scaling adoption.
By documenting both successes and failures, this project provides a realistic, replicable assessment framework for future agricultural robotics trials and contributes valuable knowledge to the specialty crop and sustainable agriculture community.
Project Results Summary
The objective of this project was to evaluate whether an autonomous quadruped robotic system could be reliably deployed in a working vineyard to perform routine monitoring and patrol tasks, reduce labor demands, and support data-driven vineyard management. Specifically, the project sought to test real-world feasibility, system durability, and operational value under typical Northeast vineyard conditions.
To meet this objective, we deployed an AgriBot quadruped robotic platform equipped with visual, LiDAR, thermal, and GPS sensors, supported by edge AI processing and cloud-based data aggregation. The system was installed on-site, calibrated for vineyard row spacing, and configured to run autonomous patrol routes using RTK-enabled GPS for high positional accuracy. Data were collected during repeated patrols and analyzed both locally on the robot and remotely through a cloud dashboard. System uptime, navigation accuracy, sensor performance, connectivity reliability, and thermal behavior were monitored throughout deployment.
The project successfully demonstrated that autonomous ground-based robotics can navigate vineyard terrain with high spatial accuracy and minimal human intervention. The robot consistently followed pre-mapped routes with centimeter-level precision, and onboard AI models effectively handled obstacle avoidance and real-time decision-making without requiring continuous internet connectivity. Sensor data collected during operation were consistent and suitable for vineyard monitoring and future longitudinal analysis.
However, the project did not fully meet its objective of sustained autonomous operation due to hardware limitations. The robotic platform experienced repeated thermal shutdowns during extended daytime use under normal seasonal heat conditions. Despite replacement of onboard computing components, these failures persisted and limited continuous operation to short-duration patrols. As a result, the project’s primary research question—whether current commercial quadruped robots are ready for sustained vineyard deployment—was answered clearly: while autonomy and sensing capabilities are technically viable, the hardware platform tested is not yet sufficiently ruggedized for extended agricultural field use.
Although a permanent operational change was not adopted at this stage, the results generated important practical value. The project prevented premature capital investment in unsuitable hardware and clarified performance requirements for future adoption. Based on limited patrols, it is projected that a reliable autonomous system could reduce routine vineyard monitoring labor by 2–4 hours per week during the growing season, representing an estimated annual labor savings of 100–200 hours. At prevailing farm labor rates, this equates to a potential cost reduction of approximately $1,500–$3,500 per season, once appropriate hardware is available.
In conclusion, the project achieved its evaluative objective by clearly identifying both the promise and current limitations of autonomous vineyard robotics. The findings provide a realistic foundation for future system selection, manufacturer engagement, and next-phase testing, positioning the farm to adopt proven automation technologies as they become field-ready.
Learning Outcomes
Key Areas of Change in Knowledge, Attitudes, Skills, and Awareness
Participation in this project led to meaningful changes in farmer knowledge, attitudes, skills, and overall awareness related to agricultural automation and on-farm technology adoption.
Knowledge:
Farmers involved in the project gained a clearer understanding of the current capabilities and limitations of autonomous robotic systems in specialty crop production. The project increased knowledge of how sensor fusion (visual, LiDAR, thermal, and GPS data) supports autonomous navigation and monitoring, as well as how edge AI and cloud systems interact in real-world farm environments. Importantly, farmers learned how environmental factors—particularly heat and sustained outdoor exposure—directly affect robotic hardware performance, a consideration not always evident from manufacturer specifications.
Attitudes:
The project shifted farmer attitudes from cautious optimism to informed realism regarding agricultural robotics. While confidence increased in the long-term potential of autonomous systems to reduce labor and improve monitoring efficiency, expectations became more grounded about near-term readiness. Farmers reported greater willingness to adopt automation incrementally and to prioritize durability, serviceability, and field testing over novelty or advertised performance.
Skills:
Farmers developed practical skills related to deploying, calibrating, and troubleshooting autonomous systems. These included mapping vineyard routes, calibrating GPS and sensors, interpreting system logs and dashboards, and diagnosing operational failures. Participants also improved their ability to evaluate vendor claims, ask informed technical questions, and assess whether emerging technologies align with their farm’s scale, climate, and labor structure.
Awareness:
The project increased awareness of the broader ecosystem required to support autonomous farming tools, including connectivity infrastructure, thermal management, maintenance demands, and data management workflows. Farmers also became more aware of the importance of on-farm pilot testing before full adoption and of the role growers can play in shaping technology development by providing real-world feedback to manufacturers.
Overall, the project strengthened farmers’ capacity to make informed, strategic decisions about automation, reducing risk while positioning them to adopt proven technologies as they become field-ready.
Project Outcomes
Changes in Practice or Behavior and Resulting Benefits
As a result of this project, meaningful changes occurred in how technology is evaluated, tested, and integrated into vineyard operations. Rather than adopting new tools based on manufacturer claims or demonstrations alone, the farm now follows a pilot-first, data-driven adoption approach. Any emerging technology is tested under real operating conditions before being considered for full integration. This change in practice reduces financial risk, improves decision-making, and ensures that new investments align with actual farm needs and environmental conditions.
For the farm operator, this shift has improved confidence and clarity in management decisions. The project reinforced the importance of matching technology readiness to farm scale and climate, preventing time-consuming troubleshooting during peak production periods. As a result, management time is used more efficiently, contributing to improved quality of life and reduced operational stress.
Operationally, the project influenced how routine vineyard monitoring is approached. While the robotic system was not adopted for continuous use, the farm identified specific tasks—such as repeatable row inspections and off-hours monitoring—that are well suited to automation once appropriate hardware becomes available. This has led to changes in labor planning, with greater emphasis on strategic labor use rather than repetitive manual scouting. In future adoption scenarios, this is expected to reduce weekly monitoring labor by several hours during the growing season.
The project also changed how input decisions are considered. Exposure to sensor-based monitoring increased awareness of how precision data could enable more targeted interventions, potentially reducing unnecessary passes, fuel use, and chemical inputs. While input reductions were not realized during this phase, the farm is now positioned to adopt technologies that support lower-input, higher-efficiency management.
Beyond the individual farm, the project benefited other growers by providing a realistic assessment of autonomous robotics in vineyard settings. By openly sharing both successes and failures, the project helped peers avoid premature investment in unsuitable platforms and encouraged more thoughtful, staged adoption. This transparency strengthened peer relationships and reinforced the value of farmer-led research.
A key success of the project was reframing expectations around innovation. As one participant noted, “This project showed us that the technology is promising, but the real value comes from knowing what actually works on our farm—not what’s claimed to work.” This perspective has empowered growers to engage more confidently with technology providers and researchers.
Overall, the project resulted in improved decision-making, reduced adoption risk, stronger peer learning, and a more sustainable pathway toward future automation—benefits that extend beyond this farm to the broader agricultural community.
Assessment of Approach, Methods, and Lessons Learned
Overall, the project’s approach—deploying an autonomous robotic system directly into a working vineyard—was appropriate and essential to answering the study’s core question. Testing the technology under real operating conditions, rather than in a controlled or demonstration setting, proved to be one of the most important factors in the project’s success. This method allowed the study to uncover practical limitations that would not have been evident through lab testing or vendor demonstrations alone.
A key strength of the methodology was its phased deployment structure, which separated system installation, calibration, autonomous operation, and evaluation. This structure made it possible to identify which components performed as expected (navigation, sensing, and AI decision-making) and which did not (thermal durability and sustained runtime). The use of repeatable GPS-guided routes and system logging also provided clear, objective performance data rather than relying solely on anecdotal observation.
The primary challenge encountered was the hardware platform’s inability to withstand normal vineyard heat conditions for extended periods. While the original methods anticipated multi-day autonomous operation, repeated thermal shutdowns forced a methodological pivot toward diagnosing system failure rather than long-term performance. This challenge was not due to unusual weather or misuse but rather highlighted a mismatch between current commercial robotic platforms and agricultural field demands.
In response, the methodology was revised mid-project to focus on failure analysis and platform readiness assessment. This included tracking shutdown timing, correlating failures with ambient conditions, and evaluating alternative hardware pathways. Although this reduced the volume of operational data collected, it increased the study’s relevance by answering a more foundational question about real-world feasibility.
The project did answer the primary research question. It confirmed that autonomous navigation, sensing, and edge AI processing are viable in vineyard systems, but it also demonstrated that hardware ruggedization remains a critical barrier to adoption. In that sense, the project succeeded by clearly defining both the potential and the current limitations of the practice under study.
Based on these findings, the farm does not plan to continue using the specific robotic platform tested. However, the practice of autonomous monitoring remains compelling, and the farm intends to continue evaluating and promoting robotic solutions once hardware durability improves. The project reinforced the value of incremental, evidence-based adoption rather than abandonment of innovation due to early setbacks.
Additional work is needed in the areas of thermal management, outdoor-rated computing, and long-duration field testing across seasons. Collaboration between growers, manufacturers, and researchers will be essential to bridge the gap between prototype systems and farm-ready tools.
The results of this project will be most beneficial to specialty crop growers, particularly vineyard and orchard operators in the Northeast and other temperate regions with similar climatic conditions. Technology developers and extension educators would also benefit from these findings, as they highlight the importance of farmer-led, on-farm testing in advancing practical agricultural innovation.