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US Agricultural Innovation: Leveraging Technology and Artificial Intelligence

On November 14, 2023, the Senate Committee on Agriculture, Nutrition, and Forestry of the United States held a hearing on “American Agricultural Innovation: Leveraging Technology and Artificial Intelligence.”

We have invited five witnesses with diverse backgrounds, including agricultural research, technology, investment, higher education, and law.

Their viewpoints on applying AI technology in training, education, policy assistance, and agriculture are valuable for countries worldwide. The main ideas from their testimony are outlined below:

Table of Contents

Jahmy Hindman

Jahmy Hindman is the Senior Vice President and Chief Technology Officer at Deere & Company. He is also a member of the Engineering College Advisory Council at Iowa State University, the Computer Science Advisory Council at the University of Texas, and the Executive Advisory Council at FIRST.

Perspectives:

● As the continuous migration from rural to urban areas persists, artificial intelligence solutions become increasingly crucial for enhancing agricultural productivity in the United States.

● The future of American agriculture is now being shaped through tools that enable data-driven decision-making among farmers. Artificial intelligence plays a critical role in unlocking the value of data and transforming it into actionable insights in the field.

● U.S. farmers would benefit from incentives that help them access precise technologies. When deliberating on upcoming agricultural legislation, please consider proposals such as the Precision Agriculture Act (PRECISE Act) and the Precision Ag Loan Act.

● Placing these technologies in the hands of American farmers not only enhances the productivity and profitability of growers but also enables them to produce enough food, fuel, housing, and clothing to sustain the growing world population.

Mason Earles

Dr. Mason Earles is an Assistant Professor at the University of California, Davis. Before his academic role, Dr. Earles served as a Data Science Engineer at Apple, where he pioneered the development of new deep learning and computer vision tools in hardware engineering.

Perspectives:

● Unprecedented advances in hardware and software have significantly expanded the ability of artificial intelligence computer programs to learn from complex real-world data in sectors like agriculture and food systems.

● Looking ahead to the future workforce, in 2021 alone, over 161,000 individuals in the United States obtained undergraduate and graduate degrees in computer science. However, only a small percentage of students end up pursuing careers in agriculture and food, and this needs to change.

● To maintain the United States’ position as a global leader in agricultural innovation, I strongly recommend that the committee continue, perhaps even expand, funding for these national artificial intelligence research institutes.

Additionally, other funding sources from the U.S. Department of Agriculture that focus on leveraging artificial intelligence to address agricultural challenges should be sustained.

This is the path to accelerate more research innovation and industry collaboration, inspiring more computer science and engineering students to tackle significant challenges in the agricultural sector through artificial intelligence and new technologies.

Sanjeev Krishnan

Sanjeev Krishnan is the Co-Founder, Chief Investment Officer, and Senior Managing Director of S2G Ventures. He has once worked at CLSA Capital Partners, IFC, Global Environment Fund, and JPMorgan. Additionally, he has served on various advisory committees and company boards.

AI can help modernize agriculture and benefit farmers by utilizing farm data.

● Anticipated changes in almost every industry within the next decade suggest that artificial intelligence will play a transformative role. In agriculture, AI holds the potential to offer groundbreaking solutions, creating significant value by introducing innovative approaches to increase crop yields, optimize resource utilization, and enhance overall farm management.

● At the heart of this development is data. The most robust and efficient AI solutions will be those capable of accessing the best data and developing the most effective methods to transform that data into appropriate training datasets to inform AI model solutions.

● The digitization of agriculture and food systems is in its early stages. On average, a farmer generates around 500,000 data points per day. By 2036, the daily volume of generated data is projected to increase by 800%.

● Despite the rapid growth in data volume, the connection between today’s data and trustworthy decision-making tools is not suboptimal, limiting our ability to access and apply actionable insights based on collected information.

Utilizing AI for analysis, synthesis, and application of agricultural data can have an immediate impact on various aspects of agriculture. Farmers are given the ability to tackle daily changes in numerous dynamic fields by providing them with the necessary tools and support.

According to the United States Department of Agriculture’s 2019 estimates, realizing the full potential of digital agricultural technology on a large scale, including establishing the necessary infrastructure, could generate an additional total income of $47 billion to $65 billion annually for the U.S. economy.

The rapid integration of AI into agricultural technology indicates that the potential benefits are significantly greater than what is currently estimated. AI benefits farmers and society in specific use cases, improving efficiency, productivity, and sustainability.

These include precision agriculture, disease and pest detection, robot harvesting and automation, livestock monitoring and management, predictive analytics and market insights, and AI-enabled agricultural drones.

AI and ag-tech can strengthen communities and build trust.

● AI can enhance the capabilities and effectiveness of existing agricultural technologies, improving and refining technical approaches. Providing clear, accurate, and high-quality data aids in filtering out ineffective methods and optimizing resource allocation.

● Agricultural technology, data, analytics, and artificial intelligence play a role in strengthening relationships and community ties among people, particularly in fostering trust between farmers and advisory networks.

● AI can ingest, analyze, and identify large volumes of data in real time, providing agricultural advisors or agronomists with more precise recommendations and actionable insights, thus alleviating their burden.

● AI-powered chatbots and virtual assistants can provide real-time advice and support for farmers, addressing questions related to crop management, pest control, market trends, and more. This is particularly valuable for farmers in remote areas or those with limited access to agricultural expertise.

AI and ag-tech can strengthen communities and build trust.

The federal government has multiple opportunities to ensure the United States maintains leadership in agricultural technology, solidifying its position as a global leader in agricultural artificial intelligence.

It is crucial to simultaneously safeguard the interests of farmers and stakeholders while supporting the agricultural system.

The key is to ensure an appropriate balance between human control and oversight and harnessing the immense power of artificial intelligence that is reshaping our world.

● Effectively and responsibly harnessing the potential of artificial intelligence in agriculture: AI is data-driven, and the fundamental issue of data ownership in agriculture must be fundamentally addressed.

● Ethical guidelines for deploying AI in agriculture are crucial to ensure that AI systems do not perpetuate biases, that decision-making processes are transparent, and that accountability for their outcomes is maintained.

This may involve establishing industry standards for data collection, use, and transparency to ensure AI systems are reliable, ethically aligned, and do not harm the environment or market dynamics.

Furthermore, a set of benchmarks validating AI-driven service declarations can further support accuracy and transparency, serving as a crucial tool for building trust.

Lastly, engaging in international cooperation to share knowledge, best practices, and regulatory frameworks related to agricultural artificial intelligence can help coordinate standards and promote global food security and farmer profitability.

● Promoting agricultural data sharing standards and initiatives: Encouraging the sharing of agricultural data among farmers, researchers, and AI developers is crucial in harnessing the power of innovative agricultural technologies and AI for the benefit of farmers.

This can be facilitated by establishing agricultural data cooperatives or platforms to aggregate data while simultaneously safeguarding individual privacy.

● Enhancing digital infrastructure and investing in digital literacy, especially for small farmers: Improving rural digital infrastructure can help ensure farmers’ access.

Additionally, projects to train farmers and agricultural workers in the use of AI and related technologies can be facilitated through collaboration with educational institutions, online courses, and on-site training programs.

Specific initiatives to support small and marginalized farmers in adopting AI technology may include subsidies for acquiring AI tools, financial aid, and technical support.

José-Marie Griffiths

José-Marie Griffiths is the President of South Dakota State University in Madison, South Dakota. She has served as a member of the National Security Commission on Artificial Intelligence (NSCAI), the National Science Board, the President’s Information Technology Advisory Committee, and several other federal committees.

Perspectives:

● As the United States advances AI deployment to innovate the agricultural sector, it faces growing cybersecurity risks and challenges. Collaboration among academia, industry, and federal institutions is necessary for the safe, responsible, and effective use of AI.

● While the agricultural sector has been undergoing automation and innovation for decades, the deployment of AI requires a strong emphasis on security and utilizing existing expertise and technologies.

● Both the public and private sectors must collaboratively address cybersecurity risks, leveraging the role of academia in developing research-driven solutions to ensure the safe and effective deployment of AI in agriculture.

● Academia plays a crucial role in nurturing the next generation of AI talent, contributing to the agricultural industry’s entry into the next phase of growth and development.

● Multi-stakeholder collaboration is essential to collectively address cybersecurity risks, harnessing the strengths of academia in formulating research-driven solutions and driving the development of AI in agriculture.

Todd J. Janzen

Todd J. Janzen is the President of Janzen Schroeder Agricultural Law LLC and the Project Administrator for the Ag Data Transparent Organization.

Perspectives:

● Regarding new technology on farms, policies should focus on creating a fair competitive environment rather than stifling innovation.

● Transparency should always be a focal point for any data collection platform, whether operated by private companies or government regulatory agencies.

Transparency does not necessarily mean that such information should be publicly accessible, but rather that farmers should be aware of what information is being collected from them and how that information will be used.

● There is room for improvement in USDA’s data collection. The Agricultural Data Act (S.98) is an example aimed at modernizing USDA’s data collection and utilization.

While USDA has been collecting farm data through various programs for a long time, much of this data is siloed across different agencies, making it less valuable for policymakers and researchers.

The Act envisions updating USDA’s data collection, creating a secure data center, and allowing stakeholders and researchers access to anonymized data collected by USDA.

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