From precision agriculture to AI: How can modern technologies simultaneously improve agricultural yields and land use efficiency?

Introduction

Against the backdrop of a global population projected to reach 9.7 billion by 2050 and a 70% increase in food demand, how to ensure food supply without continuing to reclaim large areas of land has become a core issue for the global agricultural industry. A systematic review published in *Frontiers in Plant Science* in September 2025 comprehensively assessed technologies such as precision agriculture, biotechnology, smart irrigation, automation, vertical farming, and artificial intelligence, concluding that modern technology can provide a viable path for "sustainable intensification."

Background: Agriculture under dual pressure

The review points out that extreme weather and rising temperatures caused by climate change have already reduced crop yields by up to 30% in some vulnerable regions. At the same time, about 33% of the world's soil has degraded, reducing productivity, while urbanization occupies approximately 1.5 million hectares of farmland each year. In developing countries, smallholder farmers, who supply about 80% of food, are the most vulnerable due to limited access to resources and technology. In this context, the traditional model of "expanding planting area plus increasing chemical inputs" is increasingly unsustainable.

Core technologies: From data-driven approaches to biological frontiers

The review views modern agricultural technologies as a complementary technological system:

• Precision agriculture: Using GPS, drones, and IoT sensors to monitor crop health, soil, and water conditions in real time, and through variable-rate application technology, it can reduce overuse of chemical fertilizers and pesticides by 40%–60% while increasing yields by 20%–30%.
• Biotechnology: Tools such as CRISPR and genetic modification have been used to develop drought-resistant and pest-resistant crops. For example, the promotion of Bt cotton in India has reduced pesticide use by about 50% while stabilizing yields.
• Smart irrigation: Adjusting water use based on real-time data can improve irrigation water efficiency by 40%–60%, alleviating pressure on agricultural water resources.
• Automation and robotics: As labor shortages become increasingly prominent, equipment such as automated harvesting and robotic weeding can reduce production costs by about 25%.
• Vertical farming: Through hydroponics or factory-style cultivation, output per unit area can reach 10–20 times that of traditional open fields, while reducing land use and water consumption by about 95%, providing a supplementary path for urban food supply.
• Agricultural AI: AI can predict disease and pest outbreaks and yield trends, with accuracy exceeding 90% in forecasting and resource allocation decisions, helping farm managers make better decisions in complex environments.

The review argues that these technologies do not operate in isolation but are forming a "data-driven agriculture" system composed of a perception layer, a decision-making layer, and an execution layer.

From yield to land: Real "increasing output without increasing land"The study particularly emphasizes the synergistic relationship between land-use efficiency and yield. As models such as vertical farming and conservation tillage mature, output per unit area can be greatly increased, helping to reduce pressure to reclaim forests and natural ecosystems. More efficient resource use also directly corresponds to lower carbon emissions—deforestation accounts for about 10% of global CO2 emissions, and saving land is itself a form of emission reduction.

The data in the article also show that traditional irrigation methods can lose 50%–60% of water through evaporation and runoff, while precision and smart irrigation technologies can effectively reduce this waste. This means that without increasing water resource consumption, the same parcel of land can support greater food output.

Challenges Concentrate on the 'Last Mile'

Although the technological potential is significant, the review also points to multiple barriers between the laboratory and the field. High upfront investment, farmers' insufficient digital skills, lack of infrastructure, and regulatory uncertainty particularly limit technology adoption among smallholder farmers in developing countries. For example, differences in approval of genetically modified crops across markets, and the lack of stable Internet-of-Things infrastructure in some regions, can slow the pace of technology diffusion.

The study argues that if the threshold for technology use cannot be lowered, modern agricultural technologies may further widen the production gap between large farms and smallholder farmers. Therefore, policy support, public-private partnerships, and farmer training are even more urgent than breakthroughs in individual technologies themselves.

Industry Impact

From an industrial structure perspective, the evidence cited in this review will bring a series of potential impacts:

• Production efficiency: Precision agriculture and automation will shift agricultural production methods from being 'land-dependent' to 'data- and technology-dependent,' allowing a small number of operators to manage larger-scale farms.
• Water and input use: Smart irrigation and variable-rate fertilization will reduce waste of water, fertilizer, and pesticides, lower the pressure of agricultural environmental compliance, and cut input costs.
• Labor structure: Robotics and automation reduce reliance on seasonal physical labor, but create demand for new skills such as agricultural machinery operation, data analysis, and equipment maintenance.
• Land value logic: Vertical farming and facility agriculture become more attractive in land-scarce regions, and peri-urban agriculture and new food supply chains are expected to see more investment opportunities.
• Investment direction: From venture capital to industrial capital, quantifiable resource-saving solutions—such as agricultural AI decision systems, biological breeding, and precision irrigation hardware—are expected to continue attracting attention.
• Global trade: Technological progress is expected to enable regions previously considered unsuitable for farming or with high climate risk to supplement some production, thereby affecting the comparative advantages and trade flows of certain agricultural products.

Future Outlook: The Next 3–5 Years

Looking at the medium-term outlook, the following trends will determine whether agricultural technology can translate laboratory numbers into field yields: 1. Democratization of AI and Data Platforms: As costs for satellites, drones, and ground sensors decline, AI prediction models will become more deeply embedded in irrigation, fertilization, and crop protection decisions, yet data standards and interoperability still need to be established. 2. Market Differentiation in Automation: Large farms may be the first to deploy full-process robotics, while smaller farms may enjoy the dividends of automation through machinery sharing or third-party operation models. 3. Evolution of Biotechnology Regulation: If more markets adopt predictable regulatory frameworks for gene-edited crops, the pace of scaling drought- and stress-resistant varieties is likely to accelerate. 4. Declining Costs in Vertical Farming: Falling prices for artificial lighting and renewable energy may enable vertical farms to move closer to breakeven operations in and around certain cities, but the cost gap with open-field farming will persist over the long term. 5. Convergence of Agtech and Food Tech: Under the pressures of supply chain resilience and food security, the connections between agricultural and food systems will grow tighter—whether yield gains genuinely improve food security still depends on the efficiency of linkages across storage, processing, transportation, and retail.

Conclusion

The core message conveyed by this review is that global agriculture does not lack technological options for "raising yields and saving land"; what may be lacking are the economic and social mechanisms that enable widespread adoption of these technologies. For policymakers and participants across the agricultural value chain, the real challenge lies in designing inclusive extension systems that allow a broader range of farmers to adopt advanced tools such as precision agriculture and AI—thereby completing the dual upgrade of agricultural productivity and land-use efficiency before global population and climate pressures reach critical thresholds.

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