농업 분야 인공지능(AI) 시장 - 세계 및 지역별 분석 : 용도별, 도입 형태별, 제품 유형별, 지역별 - 분석 및 예측(2024-2034년)

Global Artificial Intelligence in Agriculture Market Overview

The global artificial intelligence in agriculture market was valued at $1,820.2 million in 2023 and is expected to reach $12,478.6 million by 2034, with a CAGR of 18.95% from 2024 to 2034. The market is in a growth phase and is projected to experience significant expansion. Market players investing in research, infrastructure development, and strategic partnerships are anticipated to capitalize on the increasing demand for artificial intelligence in agriculture, driven by advancements in technology and development in AI, over the forecast period from 2024 to 2034.

Introduction of Artificial Intelligence in Agriculture

Artificial intelligence (AI) in agriculture represents a transformative shift in how farming operations are conducted across the globe. By integrating advanced algorithms and machine learning models with traditional agricultural practices, AI enables a more precise and efficient approach to farming. This technology harnesses vast amounts of data from drones, satellites, ground sensors, and IoT devices to optimize crop yields, reduce waste, and increase efficiency. AI applications in agriculture include predictive analytics for weather and crop health, automated equipment for planting and harvesting, and intelligent systems for pest and disease management. The adoption of AI in agriculture promises to enhance the productivity of farms, enable sustainable practices, and reshape the agricultural landscape by making it more data-driven and technologically equipped. This pivotal integration of AI not only caters to increasing global food demands but also addresses critical challenges such as resource conservation and climate change mitigation, making agriculture more adaptable and resilient for the future.

Market Introduction

The artificial intelligence (AI) in agriculture market is rapidly evolving, driven by the demand for enhanced efficiency, productivity, and sustainability in farming. AI technologies are transforming agriculture by integrating data analytics, machine learning, and automation into traditional practices. This innovation allows farmers to make data-driven decisions, optimize resource use, and improve crop yields.

Applications of AI in agriculture include precision farming, where AI analyzes data from satellites, drones, and sensors to provide insights into soil conditions, weather, and crop health. Predictive analytics forecast crop yields and identify pest and disease risks, enabling timely interventions. Automated machinery, such as autonomous tractors and harvesters, increase efficiency and reduce labor costs.

The adoption of AI is supported by advancements in IoT and connectivity, facilitating data collection and transmission. Significant investments in AI research and development aim to support sustainable farming practices and ensure global food security, addressing challenges such as climate change and resource scarcity.

Industrial Impact

The integration of artificial intelligence (AI) in agriculture is revolutionizing the industry by enhancing efficiency, productivity, and sustainability. AI technologies enable precise crop monitoring and management through real-time data from drones, satellite imagery, and sensors. This data-driven approach optimizes resource utilization, minimizing waste and costs. Predictive analytics provide advanced warnings about pests, diseases, and extreme weather, allowing proactive risk management. AI-driven automation, such as autonomous tractors and robotic harvesters, increases labor efficiency and addresses labor shortages. The vast data collected and analyzed by AI empowers farmers to make informed decisions, improving overall farm management. Additionally, AI supports sustainable agriculture by promoting efficient resource use and conservation practices, contributing to better soil health and reduced environmental impact. Overall, AI's transformative impact on agriculture ensures enhanced productivity and sustainability, meeting the growing global food demand while addressing critical environmental challenges.

Market Segmentation:

Segmentation 1: by Application

• Yield Optimization
• Data Analytics
• Livestock Monitoring
• Aquaculture Management

Yield Optimization Segment to Dominate Global Artificial Intelligence in Agriculture Market (by Application)

Yield optimization is a dominant application of artificial intelligence (AI) in agriculture due to its direct impact on maximizing crop production and improving farm efficiency, which are critical to profitability and sustainability.

With the global population growing and urbanization reducing arable land, there is significant pressure to produce more food from less land. AI-driven yield optimization helps identify the best crop choices, planting patterns, and resource allocation to maximize output.

AI enables precision agriculture, which involves the precise application of water, fertilizers, and pesticides using data collected from drones, IoT devices, and sensors. This targeted approach not only improves yield but also reduces resource waste and environmental impact.

In essence, yield optimization through AI not only addresses the economic bottom line by maximizing output and reducing costs but also aligns with sustainable farming practices that are increasingly vital in a world facing environmental challenges. This dual benefit is why yield optimization is a leading application of AI in agriculture.

Segmentation 2: by Mode of Deployment

• On-Cloud Deployment
• On-Premises Deployment
• Edge Deployment
• Hybrid Deployment

On-Cloud Deployment to Dominate Global Artificial Intelligence in Agriculture Market (by Mode of Deployment)

On-cloud deployment is gaining prominence in the AI-driven agriculture sector due to its scalability and flexibility. Cloud platforms allow farmers to adjust their AI resources as per seasonal demands or expansion needs without heavy upfront investments in infrastructure. This mode of deployment is not only cost-effective, minimizing the need for costly hardware and ongoing maintenance, but also caters to the financial realities of small- to mid-sized farms.

Furthermore, cloud services enhance AI applications in agriculture by integrating diverse data sources, such as satellite imagery and IoT sensors, which are essential for developing effective AI models. These platforms support advanced analytics for real-time decision-making, such as weather prediction or pest detection. They also offer automatic software updates, robust security measures, and data backup systems, ensuring that agricultural stakeholders can collaborate efficiently and manage operations remotely, securely, and without interruption.

Segmentation 3: by Product Type

• Application Programming Interface (API)
• User Interface (UI)

Segmentation 4: by Region

• North America: U.S., Canada, and Mexico
• Europe: Germany, France, Italy, U.K., and Rest-of-Europe
• Asia-Pacific: China, Japan, India, Australia, and Rest-of-Asia-Pacific
• Rest-of-the-World: Middle East and Africa and South America

North America leads the market for artificial intelligence (AI) in agriculture due to a combination of technological advancements, strong infrastructure, and substantial investment. The region is home to some of the world's leading tech companies, research institutions, and startups focused on developing AI solutions. This rich ecosystem fosters innovation and the rapid adoption of AI technologies in farming. Additionally, North American farms often have access to capital and government support, allowing them to integrate advanced technologies such as AI more swiftly than those in many other regions.

The widespread adoption of AI in North American agriculture is also driven by the high demand for precision farming techniques aimed at boosting productivity and sustainability. Farmers in this region leverage AI to optimize everything from planting and irrigation to pest management and harvest schedules, improving yields and reducing environmental impacts. Moreover, the region's robust digital infrastructure supports seamless data collection and analysis, which is crucial for effective AI deployment. These factors collectively ensure North America's leading position in the global AI in agriculture market.

Recent Developments in the Global Artificial Intelligence in Agriculture Market

• In March 2024, AgriTech Solutions partnered with a major Midwestern university in the U.S. to leverage AI in combating crop diseases. This initiative, supported by a $4.5 million grant from the USDA, focuses on using AI-driven analytics to detect early signs of disease in major crops such as corn and soybeans. The project aims to develop predictive models that can forecast disease outbreaks, enabling farmers to implement preventative measures more efficiently and reduce crop losses.
• In April 2024, Green Insights, a leading European agri-tech firm, launched a new AI platform designed to optimize water usage in agriculture. By utilizing real-time data from sensors and satellite imagery, the AI system provides precise irrigation recommendations that adjust to weather patterns and soil conditions. This technology not only aims to conserve water but also to increase crop yields by ensuring optimal irrigation levels, demonstrating a significant advancement in resource management within the agriculture sector.
• In May 2024, CropMax Technologies, based in Canada, introduced an innovative AI-powered drone system tailored for pest management. The system uses advanced image recognition and machine learning algorithms to identify pest infestations at an early stage. Supported by a $3 million investment from technology venture funds, this solution aims to significantly reduce pesticide use while enhancing the effectiveness of pest control measures, offering a sustainable approach to maintaining crop health and productivity.
• In June 2024, TerraFirma Analytics, a startup in California, debuted a groundbreaking AI application designed to optimize harvest timings and logistics. By analyzing data from multiple sources, including weather stations, soil sensors, and market trends, the AI tool provides farmers with precise harvesting schedules. This maximizes crop quality and market value while minimizing waste. The initiative has already attracted interest from several large agricultural cooperatives looking to streamline operations and boost profitability through data-driven strategies.

Demand - Drivers, Limitations, and Opportunities

Market Demand: Need for Innovation in Farming Practices

The market demand for innovation in farming practices is increasingly being met by the integration of artificial intelligence (AI) in the agriculture sector. This surge is driven by the necessity to enhance productivity and sustainability in response to global challenges such as population growth, climate change, and limited natural resources. AI technologies are pivotal in transforming traditional farming into precision agriculture, which optimizes the use of resources such as water, fertilizers, and pesticides through smarter, data-driven decision-making. By leveraging AI, farmers can achieve more predictable and enhanced crop yields, improve the quality of produce, and reduce environmental footprints, all of which are crucial for modern agricultural demands.

AI-driven innovations in agriculture also address the need for operational efficiency and solutions to labor shortages. Technologies such as robotic automation for harvesting, planting, and weeding and AI-powered drones for crop monitoring and spraying are revolutionizing farm operations. These innovations not only help reduce the reliance on human labor but also increase the accuracy and speed of agricultural tasks. As the agricultural landscape evolves, AI is becoming essential for farmers who must adapt quickly to changing market demands and climatic conditions, ensuring that farming practices are not only economically viable but also sustainable for future generations.

Market Challenge: Constraints in Land Availability and Farming Inputs

One of the significant challenges facing the artificial intelligence (AI) in agriculture market is the constraint on land availability. As urbanization continues to expand, the amount of arable land decreases, which poses a challenge for deploying AI technologies that are designed to optimize land use. While AI can enhance crop yields and land management, the effectiveness of such technologies is limited when there is simply less land to cultivate. This reduction in available farmland can hinder the widespread adoption of advanced AI solutions, as the initial costs and setup may not justify the returns on increasingly smaller plots of land.

Furthermore, the limitation in farming inputs such as water, seeds, and fertilizers can also stifle the growth of the AI in agriculture sector. Many AI applications depend on precise input management to maximize efficiency and yield; however, in regions facing shortages of these critical resources, the potential benefits of AI are curtailed. These shortages are exacerbated by environmental changes and resource mismanagement, leading to challenges in scaling AI solutions across different geographic areas. Consequently, while AI holds the promise of optimizing resource use and improving crop management, these input constraints represent a significant barrier to realizing the full potential of AI in global agriculture.

Market Opportunity: Addressing Labor Shortages with AI and Robotics

The growing labor shortages in agriculture present a significant market opportunity for the adoption of AI and robotics. As the agricultural workforce declines, driven by factors such as aging populations and urban migration, the need for automation becomes increasingly critical. AI-powered robotics can perform a wide range of tasks that traditionally require manual labor, such as planting, weeding, and harvesting. These technologies not only fill the gap left by the shrinking labor force but also enhance productivity and efficiency on farms. By integrating AI, these robotic systems can adapt to varying crop types and conditions, ensuring precision and minimizing waste.

Furthermore, AI and robotics offer a solution to the unpredictability and seasonal nature of agricultural labor demands. Automated systems can operate around the clock, unaffected by weather conditions or labor availability, thus ensuring consistent and timely agricultural practices. This reliability is crucial for optimizing crop yields and meeting market demands. As AI and robotics technologies continue to advance, their cost-effectiveness improves, making them accessible to a broader range of farming operations, from large-scale agribusinesses to small family farms. This shift not only addresses labor shortages but also propels the agricultural sector towards a more sustainable and technologically advanced future.

How can this report add value to an organization?

Product/Innovation Strategy: The product segment helps the reader understand the different products of global artificial intelligence in agriculture based on their use in agribusiness companies, agricultural cooperatives, government and private research institutes, and others. The technology types include drone scouting, mobile scouting, satellite imagery, and other advanced methods. The market is poised for significant expansion with ongoing technological advancements, increased investments, and growing awareness of the importance of precision agriculture. Therefore, the digital soil mapping business is a high-investment and high-revenue generating model.

Growth/Marketing Strategy: The global artificial intelligence in agriculture market has been growing rapidly. The market offers enormous opportunities for both existing and emerging players. Strategies covered in this segment include mergers and acquisitions, product launches, partnerships, collaborations, business expansions, and investments. Companies primarily focus on product development to maintain and strengthen their market positions.

Competitive Strategy: Key players in the global artificial intelligence in agriculture market analyzed and profiled in the study include technology developers and solution providers. Additionally, a comprehensive competitive landscape, including partnerships, agreements, and collaborations, is expected to aid the reader in understanding the untapped revenue pockets in the market.

Research Methodology

Factors for Data Prediction and Modeling

• The scope of this report has been focused on artificial intelligence in agriculture applications and products.
• The base currency considered for the market analysis is US$. Currencies other than the US$ have been converted to the US$ for all statistical calculations, considering the average conversion rate for that particular year.
• The currency conversion rate has been taken from the historical exchange rate of the Oando website.
• Nearly all the recent developments from January 2020 to June 2024 have been considered in this research study.
• The information rendered in the report is a result of in-depth primary interviews, surveys, and secondary analysis.
• Where relevant information was not available, proxy indicators and extrapolation were employed.
• Any economic downturn in the future has not been taken into consideration for the market estimation and forecast.
• Technologies currently used are expected to persist through the forecast with no major breakthroughs in technology.

Market Estimation and Forecast

This research study involves the usage of extensive secondary sources, such as certified publications, articles from recognized authors, white papers, annual reports of companies, directories, and major databases to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of global artificial intelligence in agriculture market.

The process of market engineering involves the calculation of the market statistics, market size estimation, market forecast, market crackdown, and data triangulation (the methodology for such quantitative data processes is explained in further sections). The primary research study has been undertaken to gather information and validate the market numbers for segmentation types and industry trends of the key players in the market.

Primary Research

The primary sources involve industry experts from the global artificial intelligence in agriculture market and various stakeholders in the ecosystem. Respondents such as CEOs, vice presidents, marketing directors, and technology and innovation directors have been interviewed to obtain and verify both qualitative and quantitative aspects of this research study.

The key data points taken from primary sources include:

• validation and triangulation of all the numbers and graphs
• validation of reports segmentation and key qualitative findings
• understanding the competitive landscape
• validation of the numbers of various markets for market type
• percentage split of individual markets for geographical analysis

Secondary Research

This research study of the global artificial intelligence in agriculture market involves the usage of extensive secondary research, directories, company websites, and annual reports. It also makes use of databases, such as Hoovers, Bloomberg, Businessweek, and Factiva, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the global market. In addition to the aforementioned data sources, the study has been undertaken with the help of other data sources and websites.

Secondary research was done in order to obtain crucial information about the industry's value chain, revenue models, the market's monetary chain, the total pool of key players, and the current and potential use cases and applications.

The key data points taken from secondary research include:

• segmentations and percentage shares
• data for market value
• key industry trends of the top players of the market
• qualitative insights into various aspects of the market, key trends, and emerging areas of innovation
• quantitative data for mathematical and statistical calculations

Key Market Players and Competition Synopsis

The companies that are profiled in the global artificial intelligence in agriculture market have been selected based on inputs gathered from primary experts and analyzing company coverage, product portfolio, and market penetration.

Some of the prominent names in this market are:

• Deere & Company
• IBM Corporation
• Microsoft Corporation
• Climate LLC
• Alibaba Group Holding Limited
• Aquabyte
• Ceres Imaging
• CNH Industrial N.V.
• Connecterra B.V.
• Cropin Technology Solutions Private Limited
• DJI
• Plantix
• Blue River Technology
• Farmers Edge

Companies that are not a part of the aforementioned pool have been well represented across different sections of the report (wherever applicable).

Table of Contents

Executive Summary

Scope and Definition

1 Markets

• 1.1 Trends: Current and Future Impact Assessment
  • 1.1.1 Trends: Overview
  • 1.1.2 Adoption of AI-Driven Precision Agriculture
  • 1.1.3 Utilization of AI for Predictive Analytics in Farm Management
    • 1.1.3.1 Case Study: IBM Watson Decision Platform for Agriculture
• 1.2 Supply Chain Overview
  • 1.2.1 Value Chain Analysis
  • 1.2.2 Market Map
• 1.3 Research and Development Review
  • 1.3.1 Patent Filing Trend (by Number of Patents, Country)
• 1.4 Regulatory Landscape
• 1.5 Impact Analysis for Key Global Events
• 1.6 Market Dynamics Overview
  • 1.6.1 Market Drivers
    • 1.6.1.1 Need for Innovation in Farming Practices
    • 1.6.1.2 Growing Adoption of AI Due to Enhanced Data Collection and Analysis
  • 1.6.2 Market Restraints
    • 1.6.2.1 Constraints in Land Availability and Farming Inputs
    • 1.6.2.2 Multifaceted Barriers to AI Adoption
  • 1.6.3 Market Opportunities
    • 1.6.3.1 Addressing Labor Shortages with AI and Robotics
    • 1.6.3.2 Enhanced AI Accuracy with Expanded Datasets

2 Application

• 2.1 Application Segmentation
• 2.2 Application Summary
• 2.3 Global Artificial Intelligence in Agriculture Market (by Application)
  • 2.3.1 Application
    • 2.3.1.1 Yield Optimization
      • 2.3.1.1.1 AI for Pest and Disease Detection
      • 2.3.1.1.2 AI for Yield Prediction
      • 2.3.1.1.3 AI for Space and Resource Utilization
    • 2.3.1.2 Data Analytics
    • 2.3.1.3 Livestock Monitoring
    • 2.3.1.4 Aquaculture Management

3 Products

• 3.1 Product Segmentation
• 3.2 Product Summary
• 3.3 Artificial Intelligence in Agriculture Market(by Mode of Deployment)
  • 3.3.1 Product (by Mode of Deployment)
    • 3.3.1.1 On-Cloud Deployment
    • 3.3.1.2 On-Premises Deployment
    • 3.3.1.3 Edge Deployment
    • 3.3.1.4 Hybrid Deployment
• 3.4 Artificial Intelligence in Agriculture Market (by Product Type)
  • 3.4.1 Product (by Product Type)
    • 3.4.1.1 Application Programming Interface (API)
    • 3.4.1.2 User Interface (UI)

4 Regions

• 4.1 Regional Summary
• 4.2 Drivers and Restraints
• 4.3 North America
  • 4.3.1 Regional Overview
  • 4.3.2 Driving Factors for Market Growth
  • 4.3.3 Factors Challenging the Market
  • 4.3.4 Application
  • 4.3.5 Product
  • 4.3.6 U.S.
  • 4.3.7 Application
  • 4.3.8 Product
  • 4.3.9 Canada
  • 4.3.10 Application
  • 4.3.11 Product
  • 4.3.12 Mexico
  • 4.3.13 Application
  • 4.3.14 Product
• 4.4 Europe
  • 4.4.1 Regional Overview
  • 4.4.2 Driving Factors for Market Growth
  • 4.4.3 Factors Challenging the Market
  • 4.4.4 Application
  • 4.4.5 Product
  • 4.4.6 Germany
  • 4.4.7 Application
  • 4.4.8 Product
  • 4.4.9 France
  • 4.4.10 Application
  • 4.4.11 Product
  • 4.4.12 Italy
  • 4.4.13 Application
  • 4.4.14 Product
  • 4.4.15 U.K.
  • 4.4.16 Application
  • 4.4.17 Product
  • 4.4.18 Rest-of-Europe
  • 4.4.19 Application
  • 4.4.20 Product
• 4.5 Asia-Pacific
  • 4.5.1 Regional Overview
  • 4.5.2 Driving Factors for Market Growth
  • 4.5.3 Factors Challenging the Market
  • 4.5.4 Application
  • 4.5.5 Product
  • 4.5.6 China
  • 4.5.7 Application
  • 4.5.8 Product
  • 4.5.9 Japan
  • 4.5.10 Application
  • 4.5.11 Product
  • 4.5.12 Australia
  • 4.5.13 Application
  • 4.5.14 Product
  • 4.5.15 India
  • 4.5.16 Application
  • 4.5.17 Product
  • 4.5.18 Rest-of-Asia-Pacific
  • 4.5.19 Application
  • 4.5.20 Product
• 4.6 Rest-of-the-World
  • 4.6.1 Regional Overview
  • 4.6.2 Driving Factors for Market Growth
  • 4.6.3 Factors Challenging the Market
  • 4.6.4 Application
  • 4.6.5 Product
  • 4.6.6 South America
  • 4.6.7 Application
  • 4.6.8 Product
  • 4.6.9 Middle East and Africa
  • 4.6.10 Application
  • 4.6.11 Product

5 Markets - Competitive Benchmarking & Company Profiles

• 5.1 Next Frontiers
• 5.2 Geographic Assessment
  • 5.2.1 Deere & Company
    • 5.2.1.1 Overview
    • 5.2.1.2 Top Products/Product Portfolio
    • 5.2.1.3 Top Competitors
    • 5.2.1.4 Target Customers
    • 5.2.1.5 Key Personnel
    • 5.2.1.6 Analyst View
    • 5.2.1.7 Market Share
  • 5.2.2 IBM Corporation
    • 5.2.2.1 Overview
    • 5.2.2.2 Top Products/Product Portfolio
    • 5.2.2.3 Top Competitors
    • 5.2.2.4 Target Customers
    • 5.2.2.5 Key Personnel
    • 5.2.2.6 Analyst View
    • 5.2.2.7 Market Share
  • 5.2.3 Microsoft Corporation
    • 5.2.3.1 Overview
    • 5.2.3.2 Top Products/Product Portfolio
    • 5.2.3.3 Top Competitors
    • 5.2.3.4 Target Customers
    • 5.2.3.5 Key Personnel
    • 5.2.3.6 Analyst View
    • 5.2.3.7 Market Share
  • 5.2.4 Climate LLC
    • 5.2.4.1 Overview
    • 5.2.4.2 Top Products/Product Portfolio
    • 5.2.4.3 Top Competitors
    • 5.2.4.4 Target Customers
    • 5.2.4.5 Key Personnel
    • 5.2.4.6 Analyst View
    • 5.2.4.7 Market Share
  • 5.2.5 Alibaba Group Holding Limited
    • 5.2.5.1 Overview
    • 5.2.5.2 Top Products/Product Portfolio
    • 5.2.5.3 Top Competitors
    • 5.2.5.4 Target Customers
    • 5.2.5.5 Key Personnel
    • 5.2.5.6 Analyst View
    • 5.2.5.7 Market Share
  • 5.2.6 Aquabyte
    • 5.2.6.1 Overview
    • 5.2.6.2 Top Products/Product Portfolio
    • 5.2.6.3 Top Competitors
    • 5.2.6.4 Target Customers
    • 5.2.6.5 Key Personnel
    • 5.2.6.6 Analyst View
  • 5.2.7 Ceres Imaging
    • 5.2.7.1 Overview
    • 5.2.7.2 Top Products/Product Portfolio
    • 5.2.7.3 Top Competitors
    • 5.2.7.4 Target Customers
    • 5.2.7.5 Key Personnel
    • 5.2.7.6 Analyst View
  • 5.2.8 CNH Industrial N.V.
    • 5.2.8.1 Overview
    • 5.2.8.2 Top Products/Product Portfolio
    • 5.2.8.3 Top Competitors
    • 5.2.8.4 Target Customers
    • 5.2.8.5 Key Personnel
    • 5.2.8.6 Analyst View
    • 5.2.8.7 Market Share
  • 5.2.9 Connecterra B.V.
    • 5.2.9.1 Overview
    • 5.2.9.2 Top Products/Product Portfolio
    • 5.2.9.3 Top Competitors
    • 5.2.9.4 Target Customers
    • 5.2.9.5 Key Personnel
    • 5.2.9.6 Analyst View
  • 5.2.10 Cropin Technology Solutions Private Limited
    • 5.2.10.1 Overview
    • 5.2.10.2 Top Products/Product Portfolio
    • 5.2.10.3 Top Competitors
    • 5.2.10.4 Target Customers
    • 5.2.10.5 Key Personnel
    • 5.2.10.6 Analyst View
  • 5.2.11 DJI
    • 5.2.11.1 Overview
    • 5.2.11.2 Top Products/Product Portfolio
    • 5.2.11.3 Top Competitors
    • 5.2.11.4 Target Customers
    • 5.2.11.5 Key Personnel
    • 5.2.11.6 Analyst View
  • 5.2.12 Nedap Livestock Management
    • 5.2.12.1 Overview
    • 5.2.12.2 Top Products/Product Portfolio
    • 5.2.12.3 Top Competitors
    • 5.2.12.4 Target Customers
    • 5.2.12.5 Key Personnel
    • 5.2.12.6 Analyst View
    • 5.2.12.7 Market Share
  • 5.2.13 Plantix
    • 5.2.13.1 Overview
    • 5.2.13.2 Top Products/Product Portfolio
    • 5.2.13.3 Top Competitors
    • 5.2.13.4 Target Customers
    • 5.2.13.5 Key Personnel
    • 5.2.13.6 Analyst View
  • 5.2.14 Blue River Technology
    • 5.2.14.1 Overview
    • 5.2.14.2 Top Products/Product Portfolio
    • 5.2.14.3 Top Competitors
    • 5.2.14.4 Target Customers
    • 5.2.14.5 Key Personnel
    • 5.2.14.6 Analyst View
  • 5.2.15 Farmers Edge
    • 5.2.15.1 Overview
    • 5.2.15.2 Top Products/Product Portfolio
    • 5.2.15.3 Top Competitors
    • 5.2.15.4 Target Customers
    • 5.2.15.5 Key Personnel
    • 5.2.15.6 Analyst View

6 Research Methodology

• 6.1 Data Sources
  • 6.1.1 Primary Data Sources
  • 6.1.2 Secondary Data Sources
  • 6.1.3 Data Triangulation
• 6.2 Market Estimation and Forecast