Synthetic Data’s Pivotal Role in Enhancing the Efficiency, Efficacy, and Safety of Agricultural Field Trials
Emerging technologies, particularly synthetic data, are delivering significant value and impact across agricultural field trials. This report explores the many benefits of synthetic data, addressing numerous aspects of the agricultural field trial lifecycle, and underscoring our commitment to innovation in digital agriculture.
Introduction
Historically, field trials have been fundamental to the advancement of agricultural development, testing, and validation. Despite their long-standing tradition, the stagnation in these trials’ evolution and improvement over recent decades is unexpected. This contrasts sharply with other fields, such as the medical and life sciences industries, which have embraced technology to drive significant growth and advancement.
The agricultural sector’s hesitation in adopting technology at a similar pace raises questions. Our findings indicate several factors uniquely impacting agricultural field trials:
- The need to operate across diverse geographies and a wide range of organizational types, including academic, commercial, farming, and regulatory bodies
- A heavy reliance on variable manual inputs for trial design, coordination, and testing, which complicates and lengthens timelines and increases complexity.
- Agricultural field trials are often considered “custom,” limiting the reuse or replication of methodologies and components.
- The diverse cultural, demographic, financial, and educational backgrounds and preconceived notions of participants in the agricultural ecosystem influence attitudes towards and expectations of technology.
- Agricultural standards for field trial processes, analysis approaches, and classifying results remain underdeveloped and fragmented. The absence of unified standards for trial processes restricts data mobility and the sharing of insights from one trial to another.
The agricultural industry must, nevertheless, prioritize and expand investments in digital tools and technologies despite these challenges. This journey towards digitization and digital transformation, while requiring patience and diligence, offers substantial business benefits:
- Digital Data Collection and Reporting: These tools enhance speed, accuracy, and information retention, fostering trust in data and elevating its organizational role.
- Accessibility and Sharing of Digital Data: Enhanced accessibility allows data to be more easily shared and utilized by supporting organizations and collaborators, including achrochemical producers, CROs, agronomists, crop input suppliers, and equipment dealers. This improved data availability transcends process or departmental silos, amplifying its potential to deliver business value.
- Data Integrity and Standardization: Digital tools facilitate data standardization across diverse input types, variations, and sources. Despite the variability of agricultural standards, these tools establish the necessary linkages for accurate comparative analysis.
- Expansion of Technological Access: The digital approach opens the agricultural ecosystem to a wide array of horizontal and vertical software tools and applications, providing agricultural organizations greater technological flexibility and access to new technologies.
Most importantly, adopting a digital-first foundation empowers agricultural organizations to more effectively benefit from innovation from emerging technological breakthroughs, achieving accelerated benefits beyond the current status quo. Synthetic data–based techniques reflect such innovation. Forward-thinking, digital-first agricultural organizations have begun to explore, experiment with, and implement these techniques to enhance the efficiency, efficacy, and safety of their field trials.
Synthetic data, generated algorithmically, can be used in simulations or combined with real-world data for testing or training purposes. This method allows the creation of artificial intelligence (AI) and machine learning (ML) models based on preexisting real-world field data, enabling predictions and conclusions without the need for physical experimentation. The integration of synthetic data–based approaches has grown, providing virtual insights that serve as alternatives or supplements to real-world testing and analysis, contributing to significant advancements in the field.
Moreover, synthetic data–based models, while relatively new in agriculture, have proven their value in critical areas, including medicine. For example, a synthetic data approach was developed to simplify the detection of atrial fibrillation and other cardiac conditions, receiving approval from the U.S. Food and Drug Administration (FDA). This approach demonstrates synthetic data’s potential to overcome challenges associated with obtaining representative samples and reducing sampling bias.
Despite the agricultural industry’s traditional reliance on physical experiments, there’s increasing support for high-quality digital data from sources like hyperlocal weather, remote sensors, and data logging tools. This trend suggests a growing acceptance of synthetic data as a natural extension of digital technological progress, built on a solid value foundation. As the agricultural ecosystem embraces synthetic data’s potential, Agmatix sees an opportunity for all stakeholders to experiment and understand its positive impact on current and future processes and approaches.
Industry and Domain Definitions
- Artificial Intelligence (AI): The technique by which computer-generated models predict or infer outcomes based on inputs. It’s instrumental in analyzing complex agricultural data for informed decision-making.
- AI Model: A framework within AI designed to process inputs and produce relevant outputs. These models utilize specific training and testing data to enhance their predictive accuracy, playing a crucial role in analyzing agricultural trends and outcomes.
- Data Standardization: The process of harmonizing file formats, parameter names, header labels, and measurement units to ensure consistent data comparison. This is crucial for aggregating and analyzing data from diverse sources in agriculture.
- Digital-First Organizations: Businesses or institutions that prioritize technology use to improve outcomes, seeing technology as a driver for efficiency and growth. This approach is essential for agricultural organizations aiming to adopt advanced technologies like synthetic data.
- Digital Twins: Virtual models of real-world entities, processes, or systems, built using real-time and historical data. They are valuable in agriculture for simulating conditions and outcomes without physical trials.
- Machine Learning (ML): A subset of AI that uses large datasets to learn the relationship between inputs and desired outputs, enhancing the application of AI in predicting agricultural conditions and optimizing field trials.
Synthetic Data-Based Approaches Offer Extensive Benefits to Improve Agricultural Field Trials
Vendors developing synthetic data capabilities and platforms are heavily investing in R&D to ensure their products meet agricultural organizations’ requirements. This involves analyzing and standardizing vast stores of historical and real-time field trial data to build robust AI models and end-user functionalities. Emerging synthetic data capabilities provide significant benefits to all participants in agricultural field trials:
- Digital Twin Creation: Synthetic data enables organizations to create digital twins, predicting the most effective trial layout and configuration with confidence. This can narrow the scope for trial conditions, enhance focus, and reduce waste of effort and time.
- Optimization of Field Trials: By helping organizations optimize the size, scope, and complexity of their field trials, synthetic data leads to time and cost savings, as well as reduced environmental impact.
- Insight into Success Parameters: Synthetic data offers insights into field trial parameters likely to lead to success, enabling agricultural organizations to tailor trial designs and outcomes.
- Low-Effort Visualization: Supports visualization, including digital twin-based simulation of field trial environments and outcomes. This helps organizations understand the impact of parameter changes in a low-risk virtual environment.
- Accelerated Results Timeline: Synthetic data accelerates the production of conclusive field trial results, aiding organizations in understanding their timelines and effort levels, which in turn helps allocate resources more efficiently.
- Improved Partner Selection: Synthetic data tools can prescriptively indicate the necessary testing parameters and conditions, eliminating inefficiencies in partner selection.
- Data Correction: Helps counteract, fix, or replace corrupted, incorrect, or missing trial data, avoiding costly redos and salvaging valuable information.
- Plausibility and Repeatability Assessment: Synthetic data can assess a field trial’s results’ plausibility and repeatability, identifying if and where actual outcomes diverge from expectations and whether additional analysis is required.
These advancements in synthetic data not only enhance the efficiency and safety of agricultural field trials but also streamline the path to commercialization by improving clarity, enabling better preparation, and facilitating the effective allocation of resources.
Considering Agmatix
Agmatix is a provider of software solutions designed to assist R&D organizations, agriculture professionals, and agronomists in collecting, analyzing, and leveraging agronomic data effectively. The company’s product suite encompasses a digital platform and tools that enable users to derive actionable insights from their data. By offering standardized tools, Agmatix facilitates more efficient work processes with both real-time and historical agricultural data, simplifying the complexity associated with such data management. The Agmatix portfolio includes:
- Axiom: A technology platform that ingests, aggregates, standardizes, and enriches agronomic data from diverse sources and formats. It generates precise insights through various engines, such as:
- An ontology engine for standardizing data relationships and definitions.
- An anomaly detection and integrity engine that identifies and alerts users to abnormal data items.
- A unit converter engine that harmonizes data measurements and metrics across an organization’s agronomic data spectrum.
- Agronomic Trial Management: This comprehensive solution supports organizations and their partners in planning, operating, managing, and analyzing agricultural field trials. It is designed to be standardized and user-friendly.
- Insights and Models: This tool allows for the advanced analysis of aggregated and standardized field trial data. It features statistical analysis and dynamic machine learning (ML) modeling capabilities, aiding in the interpretation of insights from single or multiple data sets.
- Digital Crop Advisor: Leveraging ML, this tool offers scientific-based crop nutrient recommendations tailored to soil, land topography, irrigation, weather, and crop management conditions. It also assists companies in quantifying sustainability metrics and reducing field-level emissions.
- Sustainability Center: Powered by Regen[IQ], the Agmatix sustainability center offers an adaptive framework to measure regenerative agriculture efforts and outcomes across crop types and regions. Serving as a scalable platform to connect field-level data through the supply chain, the sustainability center allows users to track, monitor, and improve agronomic practices and reduce supply chain risks.
- Open Data: An open database of crop nutrient trials that facilitates secure, flexible collaboration among agricultural organizations. The database is freely accessible to researchers for research into agronomic challenges and for the promotion of sustainable agriculture.
Agmatix is committed to expanding its offerings through an extensive R&D roadmap and pipeline, aiming to provide additional features and capabilities for its clients in the agricultural sector. With a solid foundation in AI/ML technologies, Agmatix seeks to deliver unique value to both existing and future customers.
Challenges in Agricultural Technology Adoption
As agricultural organizations investigate, experiment with, and adopt emerging technology approaches such as synthetic data, it is important to consider the following:
- Acknowledgement of Inefficiencies: The agricultural sector recognizes the significant inefficiencies in current field trial methods. These inefficiencies limit further optimization and innovation within the ecosystem. A major challenge lies in market and vendor fragmentation and a lack of standardization, which slows down efforts to address these inefficiencies and questions the feasibility of such efforts. Synthetic data-based techniques show promise in reducing fragmentation’s negative impacts. However, the sector will need real-world case studies to demonstrate their effectiveness before they are widely adopted. We expect widespread adoption, but, as with any emerging technology, it will take time.
- Investment in Understanding and Trust: Agricultural customers must invest time and resources to better understand, experiment, test, trial, implement, and trust these new synthetic data-based solutions. Initially, organizations will likely focus on functionalities and capabilities that address their most pressing challenges. Fully appreciating the impact and benefits of the advanced features and capabilities of these solutions may take some time.
Conclusion
Agricultural organizations are at a crossroads, recognizing the pressing need to evolve beyond traditional approaches to field trials. The reluctance to embrace change originates from a blend of organizational and domain-specific challenges. However, the emergence of emerging digital technologies presents an unparalleled opportunity for these organizations to significantly enhance field trials efficiency, efficacy, and safety. Over 75% agricultural organizations consider digital technology essential for operational improvements. Yet, a gap exists in forming active partnerships essential for implementing advancements in areas like artificial intelligence (AI).
Among the emerging technologies, those that make use of synthetic data-based techniques and tooling stand out as particularly promising for driving field trial innovation. Synthetic data’s value lies in its transformative potential to influence and enhance various aspects of a field trial’s lifecycle. Agmatix remains at the forefront of this mission, providing solutions and insights that empower the agricultural sector to overcome the innovation stalemate and achieve greater success and sustainability.
