AI in Crop Monitoring Market Size, Share, Opportunities, And Trends By Technology (Machine Learning, Computer Vision, Predictive Analytics, Others), By Offering (Hardware, Software, Services), By Application (Disease and Pest Management, Precision Irrigation, Yield Prediction, Soil Health Monitoring, Weed Control), And By Geography - Forecasts From 2025 To 2030

AI in Crop Monitoring Market Size:

The AI in Crop Monitoring Market is projected to grow at a CAGR of 14.80% over the forecast period, increasing from USD 963.456 million in 2025 to USD 1,921.228 million by 2030.

AI in Crop Monitoring Market Key Highlights:

• Precision Agriculture Boost: AI enhances crop monitoring with machine learning, computer vision, and predictive analytics for optimized farming.
• Sustainable Farming Growth: AI-driven solutions promote eco-friendly practices, reducing environmental impact while improving crop health and yield.
• Technological Advancements: Integration of IoT, drones, and satellite imagery with AI improves real-time data analysis and decision-making.
• Global Food Demand: Rising population and limited arable land drive AI adoption to maximize agricultural productivity and efficiency.

The growing global demand for food, coupled with the limitation of available arable land, is a major factor driving the AI in Crop Monitoring market. Additionally, the rising adoption of precision agriculture practices and the increasing emphasis on environmentally sustainable farming are further fueling market growth. More importantly, advancements in AI and related technologies such as machine learning algorithms, computer vision, and cloud computing are playing a pivotal role in accelerating the growth of AI in the Crop Monitoring market.

AI in Crop Monitoring Market Overview & Scope:

The AI in Crop Monitoring Market is segmented by:

• Technology: The AI in Crop Monitoring Market is segmented by technology into machine learning, computer vision, predictive analytics and others. The proliferation of sensors, drones, and images captured from space has greatly increased the availability of data that machine learning algorithms can analyze. This enhances accuracy for better decision-making and optimized results.  Computer vision powers detection systems for identifying plant diseases and weeds. The increased use of drones and satellite imagery provides high-resolution images that can be used for detailed analysis of crop health and field conditions. Predictive analytics provides climate- and market-based insights to farmers, negating adverse decision-making and risks and improving resource use, costs, and the consequences on nature. Other includes, in addition, text analysis using Natural Language Processing (NLP) and the development of robotic devices to facilitate autonomous farming in planting, harvesting, and weeding.
• Offering: The AI in Crop Monitoring Market, by offering, is segmented into hardware, software and services. The increasing adoption of precision agricultural strategies, such as using sensors, drones, and robots to collect information on plant health and soil nutrition, has propelled the hardware segment’s growth. Software is driven by AI algorithms that can process large data and identify patterns to provide more accurate crop monitoring solutions. More data from sources like satellites and drones is fueling these software algorithms.

Services are in demand due to the need for expertise, increasing awareness of AI, and the rise of agritech startups, so farmers can implement AI-powered crop monitoring solutions.

• Application: The AI in Crop Monitoring Market, by application, is segmented into disease and pest management, precision irrigation, yield prediction, soil health monitoring and weed control. The disease and pest management has high growth potential, driven by increasing demand for pest resistance due to climate change. The rising adoption of drones and satellite imagery for the early detection of infestations will drive the market.  Precision irrigation will also be growing due to increasing water scarcity and regulatory pressure over water usage. While yield prediction will be growing at a moderate rate, driven especially in developed countries and large-scale agribusinesses.  Soil health monitoring will be growing at a moderate rate, while weed control is expected to grow.
• Region:  The market is segmented into five major geographic regions, namely North America, South America, Europe, the Middle East and Africa and Asia-Pacific. North America is experiencing growth in investment in precision agriculture, especially in the US and Canada, benefiting from a strong infrastructure and research base. South America is witnessing growth, propelled by the adoption of AI solutions, particularly in Brazil and Argentina, to address climate change and resource depletion. Europe is oriented toward environmental trends by investing in these technologies. Germany, France, and the UK are investing in sustainable and precision farming concepts because of government support for adopting agricultural technology. Middle East and Africa: The Middle East and Africa are new frontiers for AI solutions for agriculture, more so in arid areas, such as Saudi Arabia, UAE, and South Africa, which are key countries of agri-tech in crop monitoring. Asia-Pacific: The Asia-Pacific region is rising because of increasing labor costs, paucity of land, and population, with China, India, and Japan ahead in the adoption curve of AI solutions.

Top Trends Shaping the AI in Crop Monitoring Market:

• Product Developments Driven by Advancement in AI and related technologies
  • The AI in Crop Monitoring market is witnessing significant product developments, driven by advancements in artificial intelligence, machine learning, satellite imaging, and IoT integration. Key players are launching innovative platforms and tools that offer real-time insights, predictive analytics, and automation to enhance agricultural productivity, resource efficiency, and sustainability, shaping the future of smart farming.
  • In July 2024, Cropin launched Sage, the world’s first real-time Gen AI-powered agri-intelligence platform powered by Google Gemini. It converts the world’s agricultural landscape into a proprietary grid-based map with options of 3x3 meters, 10x10 meters, or 5x5 kilometres, delivering data and intelligence with unprecedented scale, accuracy and speed. It uses Generative AI, multi-layered global climate data, a global crop knowledge graph, earth observation data and advanced crop models.
• Increasing Integration of IoT and Sensors with AI for Crop Monitoring
  • There is growing integration of sensors and IoT with AI, which will shape the market in the coming years.
  • In November 2024, CropX launched Actual ET Sensor, which is an above-canopy sensor that connects to the CropX agronomic farm management system to give its users knowledge of crop water use and needs, allowing for data-driven irrigation scheduling and remote management that maximises water efficiency throughout Australasia.

AI in Crop Monitoring Market Growth Drivers vs. Challenges:

Opportunities:

• Increasing Demand for AI for Crop Monitoring: In the crop monitoring market, AI applications such as machine learning, computer vision, data analytics, and other AI techniques are used to improve the accuracy, efficiency, and decision-making in monitoring crop health, growth, and productivity. It enables real-time monitoring and predictive insights, helping farmers optimise their yields by reducing costs and minimizing the environmental impact. Satellite imagery, drone footage, soil sensors and many others are used with AI to find out key data and help in crop health monitoring, yield prediction, pest and disease detection, irrigation optimization, weed detection and in precision agriculture support.
• Rising Global Food Demand: The world's population is projected to continue its upward trajectory. As the global population continues to grow, the demand for food is rising sharply to meet the nutritional needs of billions. However, since arable land is limited and largely fixed, this surge in demand is putting increasing pressure on food production systems, necessitating smarter, more efficient agricultural practices. This is one of the key factors that is driving the market for AI in crop monitoring by addressing these agricultural challenges by enabling smarter and data-driven farming practices through the use of machine learning, computer vision, and predictive analytics, helping in monitoring crop health, predicting yield and detecting diseases and optimizg irrigation. Thus, it enhances productivity as well as makes agriculture more sustainable and environmentally friendly.

According to the World Bank, the global population is expected to reach 8.6 billion by 2030, 9.8 billion by 2050 and 11.2 billion by 2100, with roughly 83 million people being added each year. To feed this growing population, there is increasing pressure on fixed land, driving demand for AI-powered tools that can effectively optimise agricultural processes, for enhancing productivity, reducing waste, and improving overall operational efficiency across the farming value chain.

• Government Initiatives: The increasing initiatives by the government are a key factor that is driving the market. For instance, the Indian government has been proactive towards incorporating AI in agriculture. Union Education Minister Dharmendra Pradhan recently announced the setting up of three Artificial Intelligence Centres of Excellence (CoEs) in the areas of healthcare, agriculture, and sustainable cities.This program, under the "Make AI in India and Make AI Work for India" vision, is aided by an expenditure of Rs. 990 crore from FY 2023-24 to FY 2027-28. One of its major goals includes empowering farmers through AI-based solutions, such as Kisan e-Mitra Chatbot, AI-Based Crop Health Monitoring and National Pest Surveillance System. Also, India's National Pest Surveillance System (NPSS) is a technology-enabled AI-based platform, introduced in August 2024, that aims to improve pest management for farmers. Farmers will be able to connect with agricultural specialists using their mobile phones. Farmers can upload pictures of pests or infested crops and get personalized pest-control recommendations from real-time inputs. The system makes farmers less reliant on pesticide shop owners, supports scientific pest-management practices, and discourages pesticide misuse, enabling sustainable farming.

Challenges:

• Slower Adoption in Developed and Underdeveloped:  One of the key challenges faced by the market is the slower rate of adoption of artificial technologies in monitoring crops in a major chunk of the agricultural world such as more particularly in those underdeveloped areas and developing regions. Lack of infrastructure, technological complexity and lack of adequate incentives from the government there to a low adoption rate in these areas, affecting the market growth.

 

AI in Crop Monitoring Market Regional Analysis:

• North America: North America is experiencing growth in investment in precision agriculture, especially in the US and Canada, benefiting from a strong infrastructure and research base.
• Europe: Europe is oriented toward environmental trends by investing in these technologies. Germany, France, and the UK are investing in sustainable and precision farming concepts because of government support for adopting agricultural technology.
• Asia-Pacific: The Asia-Pacific will be growing at the fastest rate during the forecast period. The Asia-Pacific region is rising because of increasing labor costs, paucity of land, and population, with China, India, and Japan ahead in the adoption curve of AI solutions.

AI in Crop Monitoring Market Competitive Landscape:

Some of the key players in the AI in Crop Monitoring market include A.A.A Taranisnico Ltd., PEAT GmbH (Plantix), Sentera, Inc., Farmonaut Technologies Pvt. Ltd., EOS Data Analytics, Inc., CropX Inc., Cropin Technology Solutions Pvt. Ltd., Deere & Company, IBM Corporation, Bayer AG, Planet Labs PBC, and Granular Inc.

• Product Innovation: In September 2024, Syngenta launched Cropwise AI. It is a GenAI system designed to increase the efficiency of agronomic advisors and growers in determining the best crop management practices.  It uses advanced machine learning algorithms and data analytics to offer deep insights and enhanced decision-making capabilities to optimize crop yields, improve sustainability, and drive profitability.
• Innovation: Bayer, for instance, in 2024, announced a pilot that utilizes generative AI (GenAI) to upskill agronomists and other farmer-facing employees, enabling them to quickly and accurately address questions related to agronomy, farm management, and Bayer agricultural products.  This new domain-specific GenAI model, called E.L.Y. (Expert Learning for You), positions Bayer as an agri-food frontrunner in developing contextually relevant and compliant responses to user prompts.

• Sustainability: IBM, along with Texas A&M AgriLife, launched SWAT VEXA, which is a free AI virtual assistant, marking a significant step forward towards sustainable agriculture.

List of Top AI in Crop Monitoring Companies:

• A.A.A Taranisnico Ltd.
• PEAT GmbH (Plantix)
• Sentera, Inc.
• Farmonaut Technologies Pvt. Ltd.
• EOS Data Analytics, Inc.

 AI in Crop Monitoring Market Scope:

Report Metric	Details
AI in Crop Monitoring Market Size in 2025	US$963.456 million
AI in Crop Monitoring Market Size in 2030	US$1,921.228 million
Growth Rate	CAGR of 14.80%
Study Period	2020 to 2030
Historical Data	2020 to 2023
Base Year	2024
Forecast Period	2025 – 2030
Forecast Unit (Value)	USD Million
Segmentation	Technology Offering Application Geography
Geographical Segmentation	North America, South America, Europe, Middle East and Africa, Asia Pacific
List of Major Companies in the AI in Crop Monitoring Market	A.A.A Taranisnico Ltd. PEAT GmbH (Plantix) Sentera, Inc. Farmonaut Technologies Pvt. Ltd. EOS Data Analytics, Inc.
Customization Scope	Free report customization with purchase

 

AI in Crop Monitoring Market Segmentation:

• By Technology
  • Machine Learning
  • Computer Vision
  • Predictive Analytics
  • Others
• By Offering
  • Hardware
  • Software
  • Services
• By Application
  • Disease and Pest Management
  • Precision Irrigation
  • Yield Prediction
  • Soil Health Monitoring
  • Weed Control
• By Region
  • North America
    • USA
    • Canada
    • Mexico
  • South America
    • Brazil
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Others
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • China
    • India
    • Japan
    • South Korea
    • Others

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1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations

3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. AI IN CROP MONITORING MARKET BY TECHNOLOGY

5.1. Introduction

5.2. Machine Learning

5.3. Computer Vision

5.4. Predictive Analytics

5.5. Others

6. AI IN CROP MONITORING MARKET BY OFFERING

6.1. Introduction

6.2. Hardware

6.3. Software

6.4. Services

7. AI IN CROP MONITORING MARKET BY APPLICATION

7.1. Introduction

7.2. Disease and Pest Management

7.3. Precision Irrigation

7.4. Yield Prediction

7.5. Soil Health Monitoring

7.6. Weed Control

8. AI IN CROP MONITORING MARKET BY GEOGRAPHY

8.1. Introduction

8.2. North America

8.2.1. By Technology

8.2.2. By Offering

8.2.3. By Application

8.2.4. By Country

8.2.4.1. USA

8.2.4.2. Canada

8.2.4.3. Mexico

8.3. South America

8.3.1. By Technology

8.3.2. By Offering

8.3.3. By Application

8.3.4. By Country

8.3.4.1. Brazil

8.3.4.2. Argentina

8.3.4.3. Others

8.4. Europe

8.4.1. By Technology

8.4.2. By Offering

8.4.3. By Application

8.4.4. By Country

8.4.4.1. United Kingdom

8.4.4.2. Germany

8.4.4.3. France

8.4.4.4. Spain

8.4.4.5. Others

8.5. Middle East and Africa

8.5.1. By Technology

8.5.2. By Offering

8.5.3. By Application

8.5.4. By Country

8.5.4.1. Saudi Arabia

8.5.4.2. UAE

8.5.4.3. Others

8.6. Asia Pacific

8.6.1. By Technology

8.6.2. By Offering

8.6.3. By Application

8.6.4. By Country

8.6.4.1. China

8.6.4.2. Japan

8.6.4.3. India

8.6.4.4. South Korea

8.6.4.5. Taiwan

8.6.4.6. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. A.A.A Taranisnico Ltd.

10.2. PEAT GmbH (Plantix)

10.3. Sentera, Inc.

10.4. Farmonaut Technologies Pvt. Ltd.

10.5. EOS Data Analytics, Inc.

10.6. CropX Inc.

10.7. Cropin Technology Solutions Pvt. Ltd.

10.8. Deere & Company

10.9. IBM Corporation

10.10. Bayer AG

10.11. Planet Labs PBC

10.12. Granular Inc.

11. APPENDIX

11.1. Currency

11.2. Assumptions

11.3. Base and Forecast Years Timeline

11.4. Key benefits for the stakeholders

11.5. Research Methodology

11.6. Abbreviations 

A.A.A Taranisnico Ltd.

PEAT GmbH (Plantix)

Sentera, Inc.

Farmonaut Technologies Pvt. Ltd.

EOS Data Analytics, Inc.

CropX Inc.

Cropin Technology Solutions Pvt. Ltd.

Deere & Company

IBM Corporation

Bayer AG

Planet Labs PBC

Granular Inc.