In recent years, people have paid more and more attention to the concept of “regeneration.” Mitigation measures are no longer sufficient. A recent survey showed that 80% of American consumers prefer “recycled” rather than “sustainable” brands. Regeneration means rebirth—not only “harmless,” but also “reversing harm,” which is a key topic in environmental discussions. Although regeneration is a multi-industry trend, it is vital for agricultural product systems. Regenerative agriculture, in particular, focuses on restoring soil health, increasing biodiversity, and capturing carbon. 34% of the world’s agricultural land has been degraded and will become increasingly barren, making it impossible to produce food, fiber, or feed.
Agriculture also consumes 72% of the world’s freshwater intake, and freshwater, an important resource, is under threat. Agriculture also has an important impact on climate change: 21-37% of the world’s man-made emissions come from the food system. In order to meet the above challenges, industry participants need to pay attention to regenerative agriculture and food systems, especially to feed about 10 billion people by 2050.
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Regenerative Agriculture: Feeding the Future with Resilience
Regenerative agriculture primarily focuses on restoring soil health and preserving natural resources, such as maintaining groundwater levels and farm biodiversity, to build a resilient food system. Prioritizing soil regeneration helps ensure long-term sustainability and boosts crop yields through healthier, more moisture-retentive soil. Additionally, regenerative practices optimize input use to reduce agricultural emissions. To support these improvements, many modern farms are adopting efficient tools like the 3 ton overhead crane for material handling and infrastructure maintenance, enhancing overall farm resilience and better prepares them for environmental challenges, ultimately leading to more stable income.
How Digital Tech and AI Shape Agriculture
Before the global trend of transformation to regenerative agriculture appeared, the digitization of agriculture had already gained attention. The digitization of agriculture can bring many benefits to small farmers, such as increasing farm income, improving environmental benefits, and improving commercial feasibility. Studies have shown that digital agriculture can increase the agricultural GDP of low- and middle-income countries by more than US4450 billion per year, or 28%. AI is increasingly used in agriculture, further increasing the benefits.
The various applications of AI in agriculture have the potential to accelerate the development of regenerative agriculture. Here are five promising use cases：
1. Geospatial images are used in landscape planning
The large-scale development of regenerative agriculture often requires a landscape approach, focusing on a wider range of production areas rather than individual farms. This contributes to the overall management and regeneration of natural resources. Through the use of geospatial data, AI models can analyze changes in land cover and land use, soil health, and available water resources on large areas of land to help plan regenerated landscapes.
2. AI-driven digital promotion
The personalized practices developed by research universities are essential to regenerative agriculture. The communication cost of extension agencies is high, and the proportion of extension personnel connecting with farmers is low; many farmers are not included in the scope of communication. Technological advances have reduced the cost of communication in digital channels. The large language model (LLM) and the retrieval-enhanced generation model (RAG) can also provide specific recommendations for farms based on localized data. In addition, AI translation can also provide local language services more cost-effectively, making it easier for various regions to obtain relevant recommendations.
3. Predict pests to reduce pesticide use
Pesticide use is a “global human rights issue”, and the Regenerative Agriculture Program is committed to gradually reducing the use of pesticides. AI solutions based on image recognition and hyperspectral imaging help predict pests and control them in advance, thereby optimizing the use of pesticides.
4. AI financial incentives
One of the obstacles facing regenerative agriculture is the lack of financial incentives to drive transformation. The monitoring and payment of financial incentives (such as economic incentives for carbon sequestration) are expensive and complex. However, recent pilot projects use sensors to assess soil health and use AI smart contracts to make payments faster, error-free, and cost-effectively. Most carbon finance companies use AI models based on geospatial data to remotely measure carbon stocks. The Million Farmers Initiative has also used a similar innovative model to drive transformation. The initiative uses AI to reward farmers and early investors, making it possible to reuse such financial models.
5. Rapid soil testing and project monitoring
AI soil testing can quickly assess the health of the soil and help accurately evaluate the practical effect of regenerative agriculture. In addition, AI geospatial models can be used to monitor intercropping or cover crops, and such practices are usually difficult to monitor on a large scale. Relevant analysis can also classify farmers and provide personalized support to farmers according to different application levels.
Scaling Up AI in Regenerative Agriculture
In order to ensure that artificial intelligence can truly drive climate action, there are still some challenges that need to be resolved. Key steps include:
- Reduce the carbon footprint of artificial intelligence: The growing demand for artificial intelligence has increased electricity consumption, leading to an increase in emissions from technology companies. It is essential to take mitigation measures such as switching to renewable energy and improving the efficiency of data management.
- Optimize data infrastructure and framework: High-quality data is essential to improve the effectiveness of artificial intelligence models, but agricultural data is often fragmented. Building a digital public infrastructure for data sharing enables organizations to recycle and reuse data sets, thereby reducing costs. Establishing unified standards for data collection can improve interoperability and the efficiency of data use. This also includes collecting field data from farmers and integrating it with data sets related to soil, water, and other key variables to produce evidence-based practices.
- Building a village-level service network: Without intermediaries, it may be difficult for farmers to adopt artificial intelligence technology. Stakeholders must work together to train and deploy village-based organizations that can provide farmers with AI-driven services.
With the growth of agricultural data and farmers’ increasing familiarity with technology, artificial intelligence will play an increasingly important role in regenerative agriculture. In many farms, especially those transitioning to smarter, technology-assisted systems, tools like 1 ton overhead cranes are becoming part of the backbone of operations-supporting infrastructure improvements, accumulating more data with AI-driven technology, and improving the accuracy of existing solutions. Therefore, considering the integration of artificial intelligence when planning regenerative agriculture projects is essential to make full use of technological advances.
