Will Artificial Intelligence Be the 'Master' and Farmers the 'Slave' in Agriculture?

Ironically, however, the true comprehension of existence does not belong to the master but rather to the slave. While the master maintains control over the slave, the master fails to recognize his own limitations. In contrast, the slave, through subjugation, becomes aware of his own dependence, limitations, and, ultimately, his role in shaping the master's world. Over time, the slave realizes that the master also depends on him for validation, leading to what Hegel describes as "mutual self-consciousness." This interdependence ultimately reveals that the supposed master is, in fact, reliant on the labor and recognition of the slave.

Understanding Hegel’s dialectic is crucial in comprehending how Artificial Intelligence (AI) is set to reshape our lives and the conflicts that may arise in the process. For this reason, I chose to introduce my article on "Artificial Intelligence Applications in Agriculture" with a summary of this dialectic.

Consider this: Since the Neolithic era, agricultural production has played a fundamental role in shaping human civilization. As Yale University professor James C. Scott argues in his book Against the Grain, it was not humans who created the state but rather the cultivation of grain that did. Unlike root crops such as potatoes and beets, which remain underground and lack a predictable harvest period, grains, being annual crops with visible canopies and well-defined harvesting schedules, allowed for predictable taxation and state formation. Thus, during the manpower and land-power eras of agriculture, the dominant actor was not the farmer but the crop itself.

Subsequently, with the Industrial Revolution, agriculture entered the hard-power phase, characterized by machinery and mechanization. Over time, precision agriculture, GPS integration, and data-driven farming ushered in the smart-power phase. However, the rapid rise of AI, especially post-pandemic, is now driving us into the AI-power era, bringing about disruptive innovations across many industries, including agriculture.

Artificial Intelligence, by definition, enables machines to think and learn like humans. In agriculture, AI is being used to increase efficiency, optimize resource usage, and enhance sustainability. The term “Artificial Intelligence” was first introduced in 1956 at a Dartmouth College conference in New Hampshire, with John McCarthy recognized as one of its pioneers. The first recorded AI application in agriculture occurred in 1985 when McKinion and Lemmon developed a crop simulation model for cotton, integrating irrigation, fertilization, weed control, and climatic factors.

Despite its 70-year history, AI remains in its infancy. Futurist Yuval Noah Harari famously described AI as an "amoeba today but a T-Rex in the future," underscoring that we are only at the beginning of this transformation.

Today, agriculture faces immense challenges such as climate change, population growth, and resource scarcity. AI offers solutions to these issues, making agriculture more sustainable. Until the early 2000s, conventional farming methods dominated, with the Green Revolution increasing crop yields by 70% since the 1960s. However, as we transition from manpower, land-power, hard-power, and smart-power phases, we now find ourselves firmly in the AI-power era.

Agriculture is no longer about fertilizer as the primary input—it is now about data. We are shifting from Veni, Vidi, Vici to Veni, Vidi, Data. Data, particularly Big Data, is the new driver of agricultural decision-making. By analyzing vast datasets, AI enables better forecasting, precision farming, and predictive analytics. Yet, for AI to reach its full potential, it must be supported by advancements in quantum computing, which currently faces limitations due to high energy consumption. Experts predict that within the next decade, AI software and hardware capabilities will converge, unlocking unprecedented efficiency.

Despite these advancements, the role of farmers will not be eradicated. Currently, 75% of global agricultural production comes from family farms. Farming is not merely about spending ten days a year in the field—it is about living in rural communities and sustaining an ecosystem. AI’s most significant impact will likely be integrating the 30 million farms already connected to supply chains, out of the 500 million farms worldwide.

With AI-driven data analysis, agricultural planning will shift to long-term, macro-strategies, incorporating crop rotation and predictive modeling for 5–10 years. AI will accelerate the development of superior crop varieties, enhance agro-logistics, and optimize irrigation and fertilization systems. Climate-adaptive pest control strategies will become more precise, and autonomous agro-robots will take center stage in harvesting and farm operations.

A crucial factor in this transformation is the evolution of agricultural education. To prevent the Master-Slave dynamic from positioning AI as the master and farmers as slaves, we must ensure that agricultural professionals and farmers remain in control. AI and data should serve as tools, not rulers. Just as Hegel’s dialectic emphasizes mutual dependence, AI should enhance agricultural expertise rather than replace it. Universities must modernize curricula, integrating AI with agronomy in a holistic manner, ensuring that farmers and agricultural engineers master these technologies rather than be subjugated by them.

By 2030, the global economy is projected to reach $100 trillion, with AI-driven technologies contributing $30 trillion. According to Oxford Insights' AI readiness index, Turkey has made significant progress in the last five years, ranking 47th in 2023. Meanwhile, China, which has prioritized AI in agriculture, holds 61% of global AI patents. The recent emergence of China’s DeepSeek AI, a formidable competitor to ChatGPT, has already triggered a $1 trillion drop in U.S. tech stocks.

The AI landscape is evolving at breakneck speed. As the saying goes, “Yesterday was yesterday, today is today, and in AI, even 24 hours is a long time.” Our focus must be on ensuring high-quality data input, advancing cybersecurity, establishing ethical frameworks, and—most importantly—remaining the masters rather than the slaves of AI.

Just as grain shaped the formation of states 6,000 years ago and still dictates global trade today, AI is poised to revolutionize agriculture. We must prepare for this transformation now by investing in human capital, fostering AI literacy among agricultural professionals, and ensuring that farmers leverage AI as a decision-support tool rather than surrendering control.

As we advance into the AI-driven future of agriculture, our priority must be clear: AI should serve us—we should not serve AI.

Emrah İNCE
IE. (MBA), Founder of YeniÇiftçi Platform