Report Description

Forecast Period


Market Size (2023)

USD 55.17 Million

CAGR (2024-2029)


Fastest Growing Segment

Predictive Analytics

Largest Market

North India

Market Overview

India AI in Agriculture Market has valued at USD 55.17 Million in 2023 and is anticipated to project impressive growth in the forecast period with a CAGR of 12.24% through 2029. Artificial Intelligence (AI) in agriculture represents a collection of technologies and methods designed to enhance productivity and efficiency in farming. It involves the use of machine learning algorithms and data analytics to predict crop yields, automate farming operations, and detect diseases in crops early on. These AI-driven tools can significantly enhance decision-making processes for farmers, leading to increased crop yields and sustainability in agricultural practice.

In India, the market for AI in agriculture is witnessing considerable growth. The integration of AI technologies into the agriculture sector has initiated a new era of farming known as 'precision farming'. Indian farmers are increasingly using AI-driven tools for tasks such as weather prediction, crop monitoring, and supply chain optimization. Companies like CropIn, AgNext, and Fasal are leading the way in the Indian AI agriculture market, offering innovative solutions that serve to increase productivity and profitability. As the government of India continues to encourage digital agriculture, the AI in agriculture market is poised for further expansion.

Key Market Drivers

Increasing Population Pressure & Food Security Concerns

One of the primary drivers propelling the growth of the AI in agriculture market in India is the increasing population pressure and the associated concerns about food security. With a rapidly growing population, there is a heightened demand for food production to ensure an adequate and sustainable food supply. AI technologies offer transformative solutions to address the challenges of increasing agricultural productivity, optimizing resource utilization, and ensuring food security in the face of evolving demographic trends.

AI applications in agriculture, such as precision farming, predictive analytics, and crop monitoring, enable farmers to make data-driven decisions that enhance productivity and efficiency. By leveraging AI-powered tools, farmers can optimize resource allocation, minimize waste, and respond proactively to changing environmental conditions. The imperative to feed a growing population acts as a strong driver for the adoption of AI technologies in agriculture to meet the escalating demands for food production.

Expanding Technological Advancements and Improvements

The ongoing technological advancements and improvements in connectivity infrastructure contribute significantly to the growth of AI in agriculture in India. The availability of high-speed internet, the proliferation of smartphones, and the expansion of network coverage in rural areas enable the seamless integration of AI technologies into agricultural practices.

Farmers can access AI-driven applications and services through mobile devices, making it easier to deploy and utilize AI tools on the field. Cloud computing facilitates the storage and analysis of vast amounts of agricultural data, enabling real-time decision-making. Furthermore, the development of low-cost sensors and Internet of Things (IoT) devices enhances the data collection capabilities essential for AI applications in agriculture. The synergy between technological advancements and connectivity infrastructure creates an environment conducive to the widespread adoption of AI in Indian agriculture, fostering a more digitally connected and technologically empowered farming community.

Rising Need for Precision Farming & Resource Optimization

The need for precision farming and efficient resource management is a significant driver fueling the adoption of AI in Indian agriculture. Traditional farming practices often rely on uniform application of inputs such as water, fertilizers, and pesticides across entire fields. This approach can lead to inefficiencies, wastage of resources, and increased environmental impact. AI-driven precision farming technologies enable farmers to analyze and respond to variability in the field, allowing for precise and targeted application of inputs. Machine learning algorithms analyze data from sensors, satellite imagery, and other sources to provide insights into soil health, crop conditions, and weather patterns. These insights empower farmers to optimize resource allocation, reduce input costs, and enhance overall productivity. As the agricultural sector in India faces increasing resource constraints, the adoption of AI for precision farming becomes crucial for sustainable and efficient agricultural practices.

Increased Climate Change & Weather Variability

The increasing impact of climate change and the resulting weather variability pose significant challenges to agriculture in India. Erratic rainfall patterns, temperature fluctuations, and extreme weather events can have profound effects on crop yields and overall agricultural productivity. This not only affects the livelihoods of farmers but also threatens food security in the country. In response to these challenges, AI technologies are playing a crucial role in mitigating the impact of climate change by providing farmers with advanced tools and solutions to adapt to changing environmental conditions. AI-driven predictive analytics and modeling help farmers anticipate weather patterns, identify optimal planting times, and make informed decisions about crop management practices. By analyzing vast amounts of historical weather data, machine learning algorithms can accurately predict future conditions, enabling farmers to implement adaptive strategies for climate-resilient agriculture.

Furthermore, AI offers actionable insights that can help farmers optimize resource utilization, reduce input costs, and minimize environmental impacts. For instance, AI-powered systems can optimize irrigation scheduling based on soil moisture levels, weather forecasts, and crop water requirements, thus conserving water resources and improving water-use efficiency. The ability of AI to offer these detailed and precise insights in the face of climate uncertainty positions it as a vital driver in enhancing the resilience of Indian agriculture. By empowering farmers with knowledge, AI is helping them navigate the challenges posed by climate change and work towards sustainable and productive agricultural practices.

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Key Market Challenges

Farmer Awareness & Adoption

One of the primary challenges facing the AI in agriculture market in India is the limited awareness and adoption among farmers. Many farmers, particularly in rural areas, may not be familiar with AI technologies and their potential applications in agriculture. The complex nature of AI systems can pose a barrier to adoption, as farmers may perceive these technologies as unfamiliar and challenging to integrate into their traditional farming practices.

Addressing this challenge requires comprehensive awareness campaigns and educational programs aimed at demystifying AI for farmers. These initiatives should focus on showcasing the practical benefits of AI in agriculture, such as improved decision-making, resource optimization, and enhanced productivity. Farmer training programs, workshops, and extension services can play a crucial role in building the capacity of farmers to understand, embrace, and effectively utilize AI technologies on their farms. Collaboration between technology developers, agricultural experts, and government agencies is essential to bridge the awareness gap and create a supportive ecosystem that encourages widespread adoption of AI in Indian agriculture.

Affordability & Accessibility of AI Technologies

The affordability and accessibility of AI technologies present significant challenges in the Indian agriculture sector. Many farmers, especially smallholder farmers with limited resources, may find the upfront costs associated with acquiring and implementing AI solutions prohibitive. The high initial investment in AI hardware, software, and training can act as a deterrent to widespread adoption.

Addressing this challenge requires innovative business models, financing options, and government support to make AI technologies more affordable and accessible to a broader range of farmers. Subsidies, financial incentives, and leasing programs can help reduce the financial burden on farmers and encourage them to invest in AI solutions. Moreover, collaboration between technology providers and financial institutions can facilitate the development of tailored financing packages that cater to the specific needs of the agricultural community. Efforts to enhance affordability and accessibility should consider the scalability of AI solutions, ensuring that they are adaptable to diverse farming sizes and economic conditions prevalent in different regions of India.

Key Market Trends

Automation & Robotics in Farming Operations

The adoption of automation and robotics in farming operations is a notable and transformative trend in the AI in agriculture market in India. With the integration of AI-powered robotics and automation systems, farmers are witnessing a revolution in the way tasks are performed across various stages of farming - from planting and harvesting to weed control and pest management.

These advanced technologies not only address the persistent issue of labor shortages but also offer significant improvements in efficiency and cost-effectiveness. Autonomous tractors, equipped with cutting-edge AI algorithms, are now capable of performing precise and timely operations, resulting in optimized resource utilization and enhanced scalability in farm practices. Drones, equipped with AI-powered cameras, offer real-time monitoring and data collection, enabling farmers to make data-driven decisions for improved crop management. Robotic harvesters, with their speed and precision, ensure consistent and high-quality yield. This trend towards automation reflects the industry's commitment to optimizing the use of resources, improving scalability, and enhancing overall farm productivity in India. By embracing AI-powered robotics and automation, farmers are not only streamlining labor-intensive tasks but also unlocking new possibilities for increased precision, consistency, and sustainability in farm operations.

Rising Smart Irrigation Systems & Water Management

Smart irrigation systems powered by AI are gaining significant traction in the Indian agriculture sector, particularly in regions grappling with water scarcity challenges. These advanced systems leverage AI algorithms to analyze crucial data points such as soil moisture levels, weather forecasts, and crop water requirements. By doing so, they optimize irrigation scheduling and ensure precise water application, thereby curbing water wastage and enhancing overall water-use efficiency.

The implementation of smart irrigation not only conserves water but also delivers additional benefits such as energy savings and cost reduction for farmers. The integration of AI-driven insights into irrigation management plays a pivotal role in addressing the critical issue of water scarcity, promoting sustainable farming practices in India. As water resources continue to be a precious commodity, the adoption of smart irrigation systems is expected to witness sustained growth, emerging as a transformative and indispensable trend in the Indian agriculture landscape.

Segmental Insights

Technology Insights

Based on the Technology, in the realm of Artificial Intelligence (AI) in India's agriculture sector, Predictive Analytics holds a dominant position due to its remarkable capabilities. This cutting-edge technology is being extensively adopted for forecasting weather conditions, analyzing crop yield predictions, and gaining insights into soil health. By leveraging historical data, Predictive Analytics empowers farmers and agricultural experts with actionable insights that help optimize decision-making processes. Its ability to handle the intricacies and challenges of the agriculture sector has led to its increasing prevalence, surpassing even Machine Learning and Computer Vision in this domain. With its potential to revolutionize agricultural practices, Predictive Analytics is poised to drive significant advancements and improvements in India's agriculture sector.

Offering Insights

Based on Offering, in the AI (Artificial Intelligence) in Agriculture market in India, it has been observed that AI-As-A-Service is the dominating segment. This can be attributed to the increasing adoption of cloud-based services, which provide farmers with the opportunity to access sophisticated AI solutions without the need for a substantial upfront investment. By leveraging this service model, even small-scale farmers are able to harness the power of advanced analytics and machine learning models to optimize their farming practices. This not only enhances productivity and efficiency but also contributes to the overall growth and success of the agricultural industry in India. As a result, the popularity of AI-As-A-Service continues to soar in this market, revolutionizing the way farmers approach their agricultural operations.

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Regional Insights

The northern region of India, particularly the states of Punjab and Haryana, has emerged as a frontrunner in the adoption of Artificial Intelligence (AI) in agriculture. With its rich agricultural heritage and vast farming practices, the region has recognized the potential of modern technologies to revolutionize the sector. By embracing advanced farming methods and leveraging digital innovation, the government aims to boost productivity, ensure sustainability, and address the evolving challenges faced by the agricultural community. The adoption of AI in agriculture is not just limited to crop monitoring or yield prediction. It encompasses a wide range of applications, including soil analysis, pest management, irrigation optimization, and livestock monitoring. By integrating AI into these various aspects, farmers in the region are able to make data-driven decisions, optimize resource allocation, and improve overall efficiency.

Moreover, the government's strategic approach extends beyond the implementation of AI technology. It includes the provision of training and support to farmers, ensuring that they have the necessary skills and knowledge to leverage these advanced tools effectively. Additionally, partnerships with research institutions and technology companies foster innovation and drive continuous improvement in the agricultural sector. This comprehensive approach not only strengthens the region's position in the agricultural landscape but also sets an inspiring example for other regions to follow suit. By embracing AI and digital innovation, the northern region of India is paving the way for a more sustainable and productive future in agriculture.

Key Market Players

  • IBM India Private Limited
  • Granular India Limited
  • Microsoft Corporation (I) Private Limited
  • John Deere India Private Limited
  • Intel Technology India Private Limited
  • Climate Agrobotix India Private Limited
  • Fasal Farming Private Limited
  • Aibono Smart Farming Private Limited
  • Intello Labs Private Limited
  • CropIn Technology Solutions Private Limited

By Technology

By Offering

By Application

By Region


  • Machine Learning
  • Predictive Analytics
  • Computer Vision
  • Hardware
  • Software
  • AI-As-A-Service
  • Precision Farming
  • Livestock Monitoring
  • Others
  • North
  • South
  • West
  • East


Report Scope:

In this report, the India AI in Agriculture Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

  • India AI in Agriculture Market, By Technology:

o   Machine Learning

o   Predictive Analytics

o   Computer Vision

  • India AI in Agriculture Market, By Offering:

o   Hardware

o   Software

o   AI-As-A-Service

  • India AI in Agriculture Market, By Application:

o   Precision Farming

o   Livestock Monitoring

o   Others

  • India AI in Agriculture Market, By Region:

o   North

o   South

o   West

o   East

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the India AI in Agriculture Market.

Available Customizations:

India AI in Agriculture Market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

India AI in Agriculture Market is an upcoming report to be released soon. If you wish an early delivery of this report or want to confirm the date of release, please contact us at [email protected]

Table of content

1.    Product Overview

1.1.  Market Definition

1.2.  Scope of the Market

1.2.1.    Markets Covered

1.2.2.    Years Considered for Study

1.2.3.    Key Market Segmentations

2.    Research Methodology

2.1.  Objective of the Study

2.2.  Baseline Methodology

2.3.  Key Industry Partners

2.4.  Major Association and Secondary Sources

2.5.  Forecasting Methodology

2.6.  Data Triangulation & Validations

2.7.  Assumptions and Limitations

3.    Executive Summary

3.1.  Overview of the Market

3.2.  Overview of Key Market Segmentations

3.3.  Overview of Key Market Players

3.4.  Overview of Key Regions/Countries

3.5.  Overview of Market Drivers, Challenges, Trends

4.    Voice of Customer

5.    India AI in Agriculture Market Outlook

5.1.  Market Size & Forecast

5.1.1.    By Value

5.2.  Market Share & Forecast

5.2.1.    By Technology (Machine Learning, Predictive Analytics and Computer Vision)

5.2.2.    By Offering (Hardware, Software and AI-As-A-Service)

5.2.3.    By Application (Precision Farming, Livestock Monitoring, Others)

5.2.4.    By Region        By State (Top 3 States)

5.2.5.    By Company (2023)

5.3.  Market Map

6.    North India AI in Agriculture Market Outlook

6.1.  Market Size & Forecast

6.1.1.    By Value

6.2.  Market Share & Forecast

6.2.1.    By Technology

6.2.2.    By Offering

6.2.3.    By Application

7.    West India AI in Agriculture Market Outlook

7.1.  Market Size & Forecast

7.1.1.    By Value

7.2.  Market Share & Forecast

7.2.1.    By Technology

7.2.2.    By Offering

7.2.3.    By Application

8.    South India AI in Agriculture Market Outlook

8.1.  Market Size & Forecast

8.1.1.    By Value

8.2.  Market Share & Forecast

8.2.1.    By Technology

8.2.2.    By Offering

8.2.3.    By Application

9.    East India AI in Agriculture Market Outlook

9.1.  Market Size & Forecast

9.1.1.    By Value

9.2.  Market Share & Forecast

9.2.1.    By Technology

9.2.2.    By Offering

9.2.3.    By Application

10. Market Dynamics

10.1.             Drivers

10.2.             Challenges

11. Market Trends & Developments

11.1.             Recent Development

11.2.             Mergers & Acquisitions

11.3.             Product Launches

12. India AI in Agriculture Market: SWOT Analysis

13. Porter’s Five Forces Analysis

13.1.             Competition in the Industry

13.2.             Potential of New Entrants

13.3.             Power of Suppliers

13.4.             Power of Customers

13.5.             Threat of Substitute Products

14. Competitive Landscape

14.1.             IBM India Private Limited

14.1.1. Business Overview

14.1.2. Service Offerings

14.1.3. Recent Developments

14.1.4. Key Personnel

14.1.5. SWOT Analysis

14.2.             Granular India Limited

14.3.             Microsoft Corporation (I) Private Limited

14.4.             John Deere India Private Limited

14.5.             Intel Technology India Private Limited

14.6.             Climate Agrobotix India Private Limited

14.7.             Fasal Farming Private Limited

14.8.             Aibono Smart Farming Private Limited

14.9.             Intello Labs Private Limited

14.10.          CropIn Technology Solutions Private Limited

15.  Strategic Recommendations

About Us & Disclaimer

Figures and Tables

Frequently asked questions


The market size of the India AI in Agriculture Market was estimated to be USD 55.17 Million in 2023.


IBM India Private Limited, Granular India Limited, Microsoft Corporation (I) Private Limited, John Deere India Private Limited, Intel Technology India Private Limited, are among the top market players operating in India AI in Agriculture Market.


The northern region of India, particularly Punjab and Haryana, leads in adopting AI in agriculture. With advanced farming methods and digital innovation, the region aims to boost productivity, sustainability, and address agricultural challenges. This sets an inspiring example for other regions to follow.


Rising need for precision farming & resource optimization and expanding technological advancements are the major drivers for the India AI in Agriculture Market.


Sakshi Bajaal

Business Consultant
Press Release

Precision Farming to Dominate India AI in Agriculture Market until 2025

Apr, 2020

Growing initiatives by the government for supporting adoption of modern agricultural techniques to drive the growth of India AI in agriculture market