AI in Precision Farming Market By Crop Type (Cereals & Grains, Fruits & Vegetables, Oilseeds & Pulses, Commercial Crops, Others), By Application (Crop Monitoring & Management, Soil Health Monitoring, Weather Forecasting, Yield Prediction, Irrigation Management, Fertilization Management, Pest & Disease Detection, Livestock Monitoring, Others), By Technology (Machine Learning (ML), Computer Vision, Predictive Analytics, Robotics & Automation, IoT Integration with AI, Big Data Analytics, Others), By Deployment Mode (Cloud-Based, On-Premise, Hybrid), and By End User (Farmers, Agricultural Cooperatives, Agri-tech Companies, Research Institutions, Government Agencies, Others), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles And Market Forecast, 2025 – 2035

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What trends will shape AI in Precision Farming Market in the coming years?

The AI in Precision Farming Market accounted for USD 842.74 Million in 2024 and USD 946.99 Million in 2025 is expected to reach USD 3039.82 Million by 2035, growing at a CAGR of around 12.37% between 2025 and 2035. The AI in the precision farming market signifies the combination of the technology driven by artificial intelligence in the context of contemporary farming applications. To achieve better crop performance, minimise the consumption of resources, and improve farm runs. Through the application of machine learning, computer vision, and data analytics with the help of AI-powered tools, farmers can make data-prompted decisions in the sphere of planting, irrigation, fertilisation, and pest control. These technologies use real-time data provided by sensors and the use of drones and satellite images to analyse soil health and weather conditions and the performance of crops. This is aimed at making them more efficient and cost-effective and greener in the long run. The market is currently expanding fast since the world demands sustainable ways to agricultural solutions.

What do industry experts say about the AI in Precision Farming market trends?

“AI systems in precision farming can reduce water use by 20–60%, deliver nutrients directly to root zones, and boost profitability by optimizing irrigation and fertilizer. The insights stem from real-world IoT+AI deployments.”

• Muhammad Yamin, Faculty, Agricultural Engineering & Technology, University of Agriculture Faisalabad

Which segments and geographies does the report analyze?

What are the key drivers and challenges shaping the AI in Precision Farming market?

Does AI improve crop yields through advanced data-driven decisions?

AI plays a big role in increasing the production of crops by using sophisticated data decisions in precision farming. Utilising large volumes of data gathered by soil sensors, weather predictions, satellite pictures, and crop health management, AI allows farmers to irrigate, fertilise, and treat disease- or pest-affected crops with maximum precision. In a researched scientific paper by the United States Department of Agriculture (USDA) released in the year 2020 on the field level, using precision agriculture techniques based on AI is found to improve productivity of the land up to 15% as compared to the conventional mechanisms.

A study by Iowa State University revealed that AI algorithms were in a position to eliminate 20% of fertiliser and generate cost savings and positive environmental advantages without cutting down on yield. Such insights based on data enable farmers to make decisions that are apt and location-specific, allowing them to make optimal decisions with information that increases productivity and observes resource conservation, an encouraging indication of the transformative role of AI in increasing agricultural output in a sustainable way.

Can smart sensors reduce resource usage in farm management systems?

Resource consumption in farm management systems may be greatly decreased, as smart sensors will provide the possibility to monitor and control all resources like water, fertilisers, and pesticides quite exactly. These sensors help farmers in measuring real-time moisture, fertility, and health of crops, and they will be able to apply resources in those specific areas when necessary, hence reducing waste.

The U.S. Department of Agriculture (USDA) believes that precision agriculture technologies with an element of smart sensors can minimise the use of water by up to 30% and fertilisers by 20-25%, making farming practices more sustainable. Furthermore, research conducted at the University of Nebraska points out that sensor-driven irrigation management techniques can enhance the efficiencies in water use by 15-40% based on the crop category and location.

Smart sensors have the potential to decrease the operational cost of an agricultural facility due to environmental implications like nutrient leakage and missing soil as well, since through better resource usage, smart sensors can manage to reduce the production costs of precision farming technologies in general.

Are farmers concerned about data privacy in smart agriculture?

Data privacy is one of the concerns that farmers are showing as the use of precision farming technologies advances. The access and utilisation of the immense volumes of data, including soil condition, crop production, and weather patterns, among many others, leads to the question of ownership and safety of storage and transmission of data. A 2022 survey by the U.S. Department of Agriculture (USDA) stated that approximately 60% of farmers anticipated concern regarding their personal information and the concept of their farm data being used inappropriately by third parties.

The USDA notes the importance of addressing the lack of data protection regulations, due to which people are less willing to use digital tools, which restricts technological progress. Educational organisations like Purdue University insist that, in the absence of explicit rules and the transparent governance of data, farmers might be unwilling to make full use of precision-agriculture solutions, affecting the market development in a negative way. This issue highlights the importance of strong privacy systems that will allow farmers to control their information enjoy technology improvement.

Will government incentives encourage AI-based agricultural technology development?

The decision to use government incentives is important to drive the advancements of the AI-based technologies in the agricultural sector, especially the precision farming industry. After installing subsidies, grants, and tax incentives, the governments reduce the cost for startups and existing businesses to invest in highly complex AI solutions that can manage crop returns, resource utilisation, and control of pests.

For instance, in 2021, the U.S. Department of Agriculture (USDA) has spent more than $50 million on innovative digital agriculture projects with a particular focus on the integration of AI to increase sustainability and efficiency. Further, the European Union provides some of the required investments in agri-tech research with its Horizon 2020 program, investing nearly 80 million euros in precise farming based on AI. The incentives will enhance technological advances but also encourage adoption of technology among farmers by making them cheaper and less risky.

Institutions of learning such as Purdue University have claimed that through its AI research that is promoted by the government to be used in the agricultural sector, the efficiency in crop management has improved by 15-20 percent in pilot programs. These facts bring into the spotlight the importance of government backing in determining the performance and development of AI in precision farming to enhance food security and environmental sustainability.

Does real-time soil analysis improve sustainability in precision agriculture?

Real-time soil analysis can be a game-changer when it comes to sustainability in precision agriculture because farmers can record and calculate data to make informed decisions that will allow them to effectively use resources and reduce their environmental impact. Farmers will be able to work more efficiently, eliminating wastage and runoff by constantly checking surface conditions like moisture, nutrients, and the pH levels of the agricultural land. The United States Department of Agriculture (USDA) has determined that reduction of fertiliser application of up to 20% can be achieved when precision agriculture practices such as on-time monitoring of soil are used to maintain or even increase the crop yields.

The experiments conducted in land-grant institutions, such as Iowa State University, report that the outcome of the implementation of soil sensors is enhanced nutrient management, healthier soils, and minimised greenhouse emissions. Throughout these developments, sustainable agriculture is encouraged since water is saved, the soil is improved, and the use of chemical products is reduced, facilitating the achievement of economic and environmental agriculture targets.

What are the key market segments in the AI in Precision Farming industry?

Based on the Crop type, the AI in Precision Farming Market is classified into Cereals & Grains, Fruits & Vegetables, Oilseeds & Pulses, Commercial Crops, Others. The segment that holds the most and most prominent position in the AI in Precision Farming market is the Cereals & Grains. This is so because cereals such as wheat, rice, and maize are some of the most significant crops that are used as staple crops all over the world, hence the mainstream use of AI technology to enhance efficiency in crop production, crop health, and resource management.

Direct forms of precision farming are mostly the AI-powered equipment, like the drone imaging and predictive analytics used in cereal and grain production, which are applied to mass farming problems. The strength of this segment in terms of the dominant demand is also possible due to the demand of food security and the sustainability of farming practices, which means that AI-based solutions are essential on the way to more productive cereal and grain farming globally.

Based on the application, the AI in Precision Farming Market is classified into Crop Monitoring & Management, Soil Health Monitoring, Weather Forecasting, Yield Prediction, Irrigation Management, Fertilization Management, Pest & Disease Detection, Livestock Monitoring, Others. Among all application segments of AI in the precision farming market, crop monitoring & management is the most prominent one. The rationale behind this is the fact that the real-time, constant monitoring of crops with the help of AI-enabled drones and various sensors, and imaging technology, allows the farmers to spot a stress factor, nutrient lacks, and growth patterns in good time.

An AI-based crop management system can reduce wastage and maximise the effectiveness of the input to enhance the quality of the total yield. As this aspect of AI application has the most direct effect on the productivity of crops and the relative reduction of losses, crop monitoring is the most prevalent area of focus on AI introduction in precision farming internationally.

Which regions are leading the AI in Precision Farming market, and why?

The North American AI in the precision farming market is leading because it was among the first to embrace advanced technology, it has excellent infrastructure, and it has invested in agritech to a large extent. It is booming, with the big players and agritech startups paving the way through fast-paced use and innovation of AI-derived solutions like predictive analytics, drone-enforced surveillance, and automated equipment. The market is also fuelled by high market awareness by farmers of the benefits of AI and subsidies and other policies undertaken by the government.

Massive agricultural production in such countries as the U.S. and Canada can offer the perfect setting to set AI instruments and customise the growing process and the use of resources. This fast growth and implementation of AI in the agricultural sector are also promoted by a high level of cooperation between research centres and corporate stakeholders of the industry. Besides, the abundance of data supplied by the IoT devices and sensors can aid in more effective and precise decision-making in the region.

The Asia Pacific AI in the precision farming market is growing because of a rapidly high population base, soaring food demand, dwindling arable land, and a demand in feeding its population. Other major agricultural countries such as China, India, and Japan are investing a lot in agricultural technologies to improve productivity and sustainability.

Another factor that is driving rapid adoption of AI is the availability of a good support system of the government, smart farming programs, and a growing number of agritech startups. Further advantage of the region is the development of drone technologies and remote sensing and IoT in food production. There is also a high mobile and internet penetration, which makes it easy to collect data and make real-time decisions for farmers.

Each region of the world has unique climatic conditions, which increases the necessity of customising the AI-related solutions and making them efficient in their role of optimising the crop yield and management of resources, to an extent too. These conditions, together with the increased aggravation of the population about climate-smart farming, are gearing Asia Pacific to lead AI agriculture innovation.

What does the competitive landscape of the AI in Precision Farming market look like?

The AI in the precision farming market is highly competitive with established players in the agriculture business, and pioneer startups aim to break the ceiling of technological advancements in agriculture. Such major stakeholders as Deere & Company, Bayer AG, and Microsoft Corporation use AI to optimise crop observation, automatise equipment, and make data-based decisions. FarmWise Labs and Prospera technology are startups that introduce new robotics and computer vision solutions, which boost automation and precision.

The new emerging development is the acquisition of Blue River Technology by Deere to enhance its AI-based spraying technologies, and Microsoft is going to keep growing its services in AI-based cloud activities in agriculture. Further, other organisations, like Yara International, are combining AI with nutrient management with the aim of maximising the crop yields in a sustainable manner. This dynamic environment is an indication of the fast integration of artificial intelligence, robotics, and big data, which will make farming more intelligent and more efficient globally.

AI in Precision Farming Market, Company Shares Analysis, 2024

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Which recent mergers, acquisitions, or product launches are shaping the AI in Precision Farming industry?

• In July 2025, Punjab Agricultural University (PAU) in Ludhiana demonstrated a new AI-enabled auto-steering system for tractors. The retrofit technology used GPS and sensors to auto-till fields, reducing farmer labor and fuel consumption while improving precision. The system was planned for public rollout through upcoming farmer events.
• In June 2025, Corteva Agriscience officially launched its new fungicide Forcivo, which combined flutriafol, azoxystrobin, and fluindapyr. The AI-informed formulation targeted corn and soy diseases such as tar spot and southern rust. It provided improved protection and helped maintain consistent yields.

Report Coverage:

By Crop Type

• Cereals & Grains
• Fruits & Vegetables
• Oilseeds & Pulses
• Commercial Crops
• Others

By Application

• Crop Monitoring & Management
• Soil Health Monitoring
• Weather Forecasting
• Yield Prediction
• Irrigation Management
• Fertilization Management
• Pest & Disease Detection
• Livestock Monitoring
• Others

By Technology

• Machine Learning (ML)
• Computer Vision
• Predictive Analytics
• Robotics & Automation
• IoT Integration with AI
• Big Data Analytics
• Others

By Deployment Mode

• Cloud-Based
• On-Premise
• Hybrid

By End User

• Farmers
• Agricultural Cooperatives
• Agri-tech Companies
• Research Institutions
• Government Agencies
• Others

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of Middle East & Africa

List of Companies:

• Microsoft Corporation
• Robert Bosch GmbH
• Intel Corporation
• International Business Machines Corporation (IBM)
• Bayer AG
• Deere & Company
• SAP SE
• Yara International ASA
• DTN LLC
• FarmWise Labs, Inc.
• Taranis Visual Ltd.
• Blue River Technology Inc.
• Prospera Technologies Ltd.
• Ceres Imaging Inc.
• PrecisionHawk Inc.

Frequently Asked Questions (FAQs)