The 4th Industrial Revolution

Text Transcript with Description of Visuals

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Dr. Alex Kirkpatrick: Hello, Dr. Alex Kirkpatrick here from the Center for Sustaining Agriculture and Natural Resources at Washington State University. In this video, we’ll take a step back for a global view on artificial intelligence and its potential impacts on politics, industry, and agriculture through the lens of the so-called Fourth Industrial Revolution. We’ll look at the race for AI supremacy on the global scale and the competition between global powerhouses like China and the US to design, develop, and diffuse AI across industries and nations. We’ll also explore some more possible use cases for AI in agriculture. Let’s explore.	A slide show presentation. The speaker appears on screen. Text in a ribbon along the bottom of the screen reads, Alex Kirkpatrick PhD.
[Music]	A combine harvester moves, while a woman at the edge of the field controls it by tapping buttons on a tablet. In an industrial greenhouse, crops are watered by an automatic arm with sprinklers. In another field, a different woman holds up a sprig of a crop, viewing it through the lens of a tablet in her other hand. Text on screen reads, “The fourth industrial revolution, AI and Ag 4.0.” The logos for Western SARE (Sustainable Agriculture Research and Education), Washington State University, USDA National Institute of Food and Agriculture, US Department of Agriculture, and Ag AID Institute, (National Agricultural AI Institute for Transforming Workforce and Decision Support) line the top of the screen.
Dr. Alex Kirkpatrick: The Fourth Industrial Revolution was a term coined by the World Economic Forum less than a decade ago to describe the near ubiquitous impacts that autonomous, smart, increasingly AI-backed technologies are projected to have on global industry. In the words of Klaus Schwab, “It is a technological revolution that is blurring the lines between the physical, digital, and biological spheres.” The impacts of this revolution are likely to be felt by all workers across industries, but perhaps even more so by those working in white-collar, knowledge-based roles, such as those related to communication, journalism, healthcare, education, and even legal professions.	A drone with sprinklers flies over a field with crops. Then a robotic arm moves in a factory. Surgeons stand next to an operating table with many robotic arms over a patient. A man sits at a computer doing programming. Server cables flash. A chat bot asks, “hello can I help you?” A man uses a VR headset while operating a robotic arm in a factory, a person practices walking using robotic limbs that are strapped to their body. Text is reflected into a human eye. A man wearing a hard hat and a hi-vis jacket works on a construction site.
While the true socio-economic impacts of the so-called Fourth Industrial Revolution are far from resolved or certain, the potential for mass automation has many people worried. But on the other hand, the potential for enhanced efficiency has dollar signs spinning in a lot of the world’s most powerful leaders and businesses. The potential that other nations might leap ahead in terms of technological advancement has created a space for competition on a truly planetary scale. The Fourth Industrial Revolution might equally be viewed as the great AI arms race.	A robotic arm picks up a potted plant. Alex appears on screen.
While just about every nation in the world is contending with the impacts of AI across their societies, three main players have emerged. Unsurprisingly, the United States is seeking to maintain its position as arguably the most powerful economy on Earth through pioneering artificial intelligence. The US approach has so far been free market, innovation first, prioritizing venture capital and Silicon Valley leadership with minimal federal intervention. Companies like IBM, Google, OpenAI, Microsoft, Meta, and NVIDIA are already the world leaders in AI development, and their influence extends across the planet. The US approach so far can be seen as supporting its broader aim of maintaining global techno-scientific leadership and countering the economic and even militaristic challenges of competitors like China. The current US administration, as of 2025, appears committed to loosening AI regulations, allowing private industry to lead the way in terms of providing their own guardrails and ethics. If the US represent the free-wheeling market capitalist approach, the European Union’s approach seems to be that of a regulator. All signs point to the European Union aiming to become the global leader in AI ethics, risk mitigation, and regulation. It’s hoped that regulation will create a space for more European-based AI companies to thrive with an emphasis on human-centric design. Meanwhile, if Silicon Valley giants like Google and others want to operate in lucrative EU markets, which they undoubtedly do, then they’ll have to abide by EU rules and regulations. This may have an indirect influence on the way AI is developed and diffused here in the US. AI developed in the US may be designed in accordance with the EU AI Act of 2020. This powerful legislation is a first-of-its-kind wholesale attempt to provide a comprehensive, legally binding regulatory framework for AI risk management and ethical oversight. The same goes for AI companies in China, the US’s primary competition. If they want to play in Europe, they must abide by European legislation. China’s overall strategy is centralized, state-driven, and backed by massive state investment, government oversight, and military ambition. China’s unique approach gives it significant power to deploy AI at scale, sometimes at the cost of privacy, human rights, and personal freedoms. China’s next-generation AI development plan of 2017 aims to make China the dominant AI superpower by 2030, and there’s evidence to suggest that they’re making some headway to that aim. According to the Paulson Institute, nearly half of the globe’s top AI researchers are Chinese nationals (compared to just 18% who are American). While US institutions like Stanford University maintain their place at the top in terms of employers of such talent, China-based universities and tech giants are fast catching up. According to Forbes, about three out of every five global AI patents granted in 2022 were generated by China. That’s three times as many as those generated in that same period by the US.	Text on a slide reads, “There are 3 main players in the 4th Industrial Revolution.” Text below the main heading reads, “USA.” Bullet point, “Venture-capital approach.” Bullet point, “Silicon Valley tech giants.” Bullet point, “maintaining global dominance.” Bullet point, “loosening AI regulations.” Below the USA information, an EU flag appears and text, “European Union.” Bullet point, “regulation approach.” Bullet point, “reducing foreign influence and dependency.” Bullet point, “the pioneering EU AI act 2020.” Below the European Union text, the Chinese flag appears. Text, “China.” Bullet point, “state control, surveillance and dominance.” Bullet point, “next generation AI development plan 2017.” Bullet point, “45 percent of the world’s top AI researchers.” Bullet point, “60 percent of global AI patents in 2022.”
It’s difficult to predict what the global landscape for AI will look like in the near future, who will emerge as dominant if any one nation, and what impacts this will have on the social, economic, and environmental aspects of sustainability. But we can look at some possible use cases for AI in agriculture to better appreciate what impacts the Fourth Industrial Revolution might have on farming in the US.	Alex appears on screen.
[Music in background] Of course, one of the most significant challenges in agriculture is optimizing the use of water, fertilizers, and pesticides. AI-driven deep learning systems making use of neural networks of monitors can potentially monitor soil moisture, nutrient levels, weather, and plant health in real time, assisting decisions about water allocation. Plant health and environmental data then could be used to support variable rate applications of fertilizers, allowing farmers to apply inputs precisely when and where they’re needed, reducing waste and losses from the farm to other environments. In greenhouse applications, AI-based energy management systems are being developed to improve the use of heating, lighting, and ventilation, and adjust these parameters in response to real-time conditions and data. For the scholarly-minded, the paper titled “Precision Irrigation Management using Machine Learning and Digital Farming Solutions” provides a great overview of how machine learning is being applied to enhance water management in particular.	Text on screen: “Possible use cases for Ag AI.” Text on screen, “AI might enhance resource efficiency.” Bullet point, “water allocation.” Bullet point, “precision fertilizer and pesticide application.” Bullet point, “greenhouse management.” Bullet point, “E.g., Abioye et al 2022, precision irrigation management using machine learning and digital farming solutions, agri engineering, 4, 1, pages 70 to 103.”
Land managers may be able to enhance their decision-making through deep learning models utilizing neural networks of sensors enabled through the Internet of Things. But machine learning may also be useful in synthesizing pre-existing data sets sourced by traditional manual analysis procedures over time. Again, where there exists a lot of historical data on factors such as pH levels and organic matter content, for example, machine learning might be a route to give those unwieldy data sets meaning to decision makers. AI is also useful for simulation, of course, particularly for modeling the impacts of different environmental factors and operations on production. It could be used to explore future conditions that differ from past experiences and minimize the negative impacts of extreme weather and changing temperature, moisture, and pH levels. We glimpsed some of this potential when I introduced the concept of digital twins in another video. Farmers might be able to better pair crop selection of varieties with the environment and manage livestock to avoid animal stress. In the article, “Enhancing Crop Recommendation Systems with Explainable Artificial Intelligence,” researchers explore an application called XAI Crop, which equips farmers with personalized crop recommendations using soil type information, regional weather patterns, and historical crop yield data. Take a look if you’re interested.	A new slide. Text reads “AI could assist land use and planning.” Bullet point, “topography, soil quality and weather.” Bullet point, “climate change modelling.” Bullet point, “optimized crop selection and practices.” Bullet point, “E.g., Shams et al, 2024. Enhancing crop recommendation systems with explainable artificial intelligence, a study on agricultural decision-making. Neural Computing and Applications, 36 (11), pages 5695 to 5714.”
AI may also underpin future smart farm operations. Drones with AI-driven imaging systems like hyperspectral, multispectral, and thermal cameras can capture high volumes of data and are then analyzed with AI-driven machine learning software to provide information on crop health, soil conditions, and other variables. Better autonomous driving systems combined with imaging, robotic hardware, and machine learning techniques could also help to address one of the other key challenges to sustainability: skilled workforce availability. We’ve seen some examples of laser weeders and harvesters. Autonomous planters, pickers, even blossom thinners are in the works around the world. Such automation could boost productivity. There are also other solutions under development to image and determine maturity through size, shape, and color of different crops, allowing, potentially, for more precise timing of fruit and vegetable harvest. Such innovations can be, but doesn’t have to be, paired with an autonomous platform, further reducing reliance on labor. See the example of the SARDOG for an example of how researchers are applying these concepts.	A new slide titled “AI may drive smart farm operations.” Bullet point, “enhancing aerial drone and imaging.” Bullet point, “autonomous farm robotics.” Bullet point, “enhanced productivity.” Bullet point, “reduced waste and reliance on variable labor markets.” Bullet point, “E.g., Kulhandjian et al, 2024. Design and Implementation of a Smart Agricultural Robot bullDOG, (SARDOG). International Conference on Computing, Networking and Communications, 2024, pages 767 to 771.”
As shown in other videos, one of the more useful applications of machine learning comes in the form of identifying plant diseases and pests. Weather and environmental conditions drive pest and disease development. As the environment changes and conditions become hotter, cooler, drier, more humid, it complicates pest and disease management. Early detection of diseases through drones and ground-based sensors can help treatment, stop disease spread, and support steps to mitigate disease impacts. For example, some AI models have accurately detected and classified rice diseases like rice blast with an accuracy of about 95%, enabling timely interventions that support sustainable farming practices. Check out the review article listed for more insights and examples.	The speaker appears on screen. Text on the right reads, “AI could enhance pest and disease management.” Bullet point, “Shifting environmental conditions complicate management.” Bullet point, “Machine learning to model and identify pests and diseases.” Bullet point, “E.g., Yuan, et al, 2022. Advanced agricultural disease image recognition technologies, a review. Information Processing in Agriculture, 9, (1), pages 48 to 59.”
AI could also support optimization of supply chains, as AI is used in logistics, transport, and the market more generally. Better integrating market data with planting and harvesting schedules might help farmers to develop more targeted marketing strategies for different crop varieties. With improved analytics online and generative AI, agricultural businesses have the opportunity to better reach their target audiences, design websites, automate social media, and plan strategic communication. Machine learning can potentially make sense of historical market trends and monitor live trends to enhance economic decision-making for packers and distributors. More reliable price prediction, optimized through machine learning, could help ensure a stable and secure food supply and reduce waste. AI and advanced computing is already helping to optimize the transport and storage of agricultural products. Future advances could further optimize the supply chain. See this helpful review of price forecasting for more.	A new slide titled “AI could optimize marketing and supply chains.” Bullet point, “Integration with market data.” Bullet point, “improved marketing and strategic communication.” Bullet point, “price prediction.” Bullet point, “logistics, transport, storage, et cetera.” Bullet point, “E.g. Sun et al, 2023. Agricultural product price forecasting methods, A review. Agriculture, 13, (9), 1 to 20.”
So, as you’d likely already assumed, agriculture is probably going to be reshaped by the Fourth Industrial Revolution, much like every other industry on the planet. What’s less clear is the extent to which AI will actually help or hinder attempts at sustainability.	The speaker appears on screen.
[Music] In this video, we’ve taken a look at the Fourth Industrial Revolution, a term coined by the World Economic Forum to describe the far-reaching impacts of AI, robotics, and a blending of the biological and the artificial. We also introduced the so-called AI arms race that exists between the two main competitors, China and the US. It’s a race to leverage intelligent machines to enhance national GDPs and global market dominance, which includes optimizing agricultural industry. Within that, we’ve seen that the US government currently aims to reduce the regulatory barriers to AI development and adoption. Meanwhile, China’s approach is one of government control. The EU might not be leading in terms of technological development, but it might still have some significant influence considering how desirable the free European market is to both China and the US. With their focus on trustworthiness and transparency, the EU could indirectly steer both US and Chinese tech output if they hope to pass stringent EU safety checks. What all this looks like for agriculture is as yet unclear. However, the focus seems to be on improving productivity and profitability and optimizing agricultural decision-making. These developments could improve sustainability by minimizing inputs and reducing waste or losses to the environment.	Text on screen, “The takeaways.” A new slide with an illustration of a lightbulb with leaves growing around it. Bullet point, “4th Industrial Revolution = increased use of robotics, AI and human-machine interaction.” Bullet point, “AI arms race: Bullet point, US = free-market pro-AI approach.” Bullet point, “China = government control and surveillance.” Bullet point, “EU = transparency, trustworthiness and safety regulation.” Bullet point, “4th agricultural revolution.” Bullet point, “optimization and sustainability.”
[Music] Any of the potential positives that AI could have in terms of achieving the economic, social, and environmental components of sustainability must be weighed against the materials and energy needed to create and maintain AI models and any associated hardware, not to mention the impact on the individual worker. But the risks and ethics of AI are covered more in other videos. I hope this video has given you a more concrete idea of why AI is increasingly important to agriculture and society as a whole and how the Fourth Industrial Revolution might affect ag. Thanks for your energy and your engagement.	A new slide. Text on screen reads “The closer.” The speaker appears on screen.
[Music]	A new slide. Text on screen “Take care.”
	A new slide. The logo for the USDA, National Institute of Food and Agriculture US Department of Agriculture. The logo for Western SARE, Sustainable Agriculture Research and Education. Text. “This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2023-38640-39571 through the Western Sustainable Agriculture Research and Education program under project number WPDP 24-013. USDA is an equal opportunity employer and service provider. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author or authors, and do not necessarily reflect the view of the U.S. Department of Agriculture. The logo for Ag AID Institute. Text. This material is based upon work supported by the AI Research Institutes program supported by NSF and USDA-NIFA under the AI Institute, Agricultural AI for Transforming Workforce and Decision Support, (Ag AID). Award number 2021-67021-35344.”