AI in Agriculture: How Farmers Are Increasing Yields and Reducing Risks

Andy Paterson • August 22nd, 2025.

With climate change, water scarcity, and increasing operational costs all threatening farmers’ ability to produce crops and make a living, could AI in agriculture bring another agricultural revolution?

Technologies have been used in agriculture for thousands of years to increase yields, reduce costs, and stave off pests and disease. The adoption of Artificial Intelligence into farming practices promises to be the next leap in efficiencies and yield increases.

This article will introduce what AI in agriculture is, how farmers, agronomists, and agro-businesses are using it today, and its potential in the future.

What Is AI in Agriculture?

AI in agriculture involves integrating advanced machine learning data analytics models to enable better decision-making on varietals, when to plant, harvest, and water, and to optimize operations across the value chain. 

There is a range of different AI applications in agriculture, some of which include:

• Weather modeling: Through advanced weather intelligence, farmers can get a highly granular understanding of exactly when specific weather conditions will happen and how climate trends will evolve. These insights allow better decision-making, capital investments, and resource efficiency. 
• AI agents: Increasingly, agricultural AI is being delivered through conversational or task-based “AI agents” that automate insights and actions for farmers. For example, ClimateAi uses AI agents to generate specific climate risk forecasts for specific times and crops, providing accurate adaptation insights. ITC MAARS provides smallholder farmers in India with personalized agronomic advice via digital agents, and Bayer is investing in AI-driven platforms to deliver tailored crop protection and seed recommendations. Agents reduce the complexity of using AI by translating vast datasets into simple, actionable guidance.
• Growing Degree Day (GDD) Tracking: A GDD is a unit of heat measurement. They are the most accurate way of tracking key plant growth timing (flowering, harvesting, etc.). With AI-driven, hyper-local, accurate weather forecasts, growers can get a precise count of GDDS and streamline operations and logistics, and harvest crops at the optimum time. 
• Resource optimization: AI can help growers determine the best timing and amount of pesticide or other pest control and fertilizer to use, reducing input costs. 

To put the potential for AI in Agriculture in perspective, a McKenzie analysis found that the combined on-farm yields and input and labor cost reductions, as well as increased sales and productivity, could create an additional $250 billion a year for US farmers today.  

Why AI Is Key to the Future of Agriculture

Farmers and the enterprises buying from them are facing more and more disruptions. From droughts to extreme weather, soil depletion, control-resistant pests and diseases, and inconsistencies in yield and quality, AI has the potential to alleviate all of these impacts.

The agricultural sector is facing three main challenges. This is how AI can solve these problems, to ensure yields are maintained and costs are kept down:

• Weather Challenges: Climate change is already impacting global food yields and increasing prices. However, as temperatures rise, precipitation patterns change, and extreme weather events become more frequent, farmers are expected to see further yield losses. AI enables both short-term reaction (e.g., drought, frost alerts) and long-term adaptation (e.g., crop viability modeling) much more accurately than previous forecasting technologies.
• Operational Decision Making: Applying fertilizers and pest control too early, or organizing logistics and workers when the crop is not ready, are very costly mistakes. AI-enabled sensors can be added to soil to optimize irrigation, and other sensing data can be used to apply pesticides only when and where necessary. Additionally, through tracking GDDs, growers can determine the exact harvest date for their crop.
• Supply Chain: Farmers and supply chain managers face challenges in knowing the optimal harvest date, ensuring consistent quality, forecasting commodity prices, and managing weather-related risks. AI helps address these issues by pinpointing the best harvest timing, automating quality grading, predicting price volatility through weather-yield models, and enabling better insurance premiums based on quantified farm-level risk.
  

How Farmers Can Integrate AI in Agriculture

There are two main avenues where AI can support agriculture: on-farm and enterprise (food and beverage, cosmetics, etc.) supply chains. Here is how each application can impact those two parts of the value chain:

How Growers Are Already Using AI in Agriculture

This is not something happening in the future. There are hundreds of examples of farmers and agribusinesses using AI today to increase yields and reduce costs substantially. 

Here are five case studies of AI in agriculture today:

Precision Farming:

• An experiment in California utilized AI to manage nitrogen and irrigation applications in grape cultivation. The move resulted in a 25% increase in production while using 20% fewer water resources.
• Researchers developed a causal inference framework to evaluate the real-world impact of digital agriculture tools. In field trials with a cotton farmers’ cooperative, it was found that their AI-driven sowing recommendations delivered a 12–17% statistically significant yield increase, demonstrating the potential of AI adoption in sowing timing decisions.

AI Weather Modeling:

• Global seed company Advanta Seeds reduced climate-driven revenue volatility (historically 30–50% swings per year) by using AI-enabled, accurate seasonal and long-term forecasts. This prevented hundreds of thousands to millions in losses through early harvest decisions and enabled a 5–10% sales boost by predicting an extreme precipitation event two months before competitors.
• A ClimateAi multinational agricultural client cut seed-trial costs by 90% and reduced multi-year R&D timelines to just hours, while also finding that rising heat could drive a 30% output loss for tomato seeds in India within 20 years. ClimateAi’s analysis showed that 50% of their supply production sites would reach climate “tipping points” within four years, enabling the company to diversify geographically, secure supply stability, and boost margins through more efficient site selection.

Supply Chain:

• ThroughPut AI helped Church Brothers Farms boost short-term demand forecasting accuracy by up to 40%, enabling smarter planting schedules, order fulfillment, and inventory management. By shifting from a make-to-stock to a make-to-order approach, the company minimized excess stock and carrying costs.
  

While the adoption of AI has been slow, especially in traditional farming cultures, the cost of AI software and hardware is expected to come down the cost curve, and adoption will inevitably increase. 

Just as farming communities in the past have adopted mechanized tractors, fertilizers, and GMOs to increase yields and reduce risks, AI will eventually be fully integrated across the agricultural value chain. Companies at every part of the value chain stand to benefit from first-mover advantage if they adopt these technologies early. 

To get started on a simple way to integrate AI weather intelligence into your agriculture business, reach out to become an early adopter.

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