Computer Vision Technology Market for Agriculture to Reach $2,037.5 Million by 2033

14 May 2024

The computer vision technology market for agriculture is currently in a dynamic growth phase, marked by rapid advancements and heightened adoption. The market exhibits a degree of fragmentation, with diverse players contributing to specialized applications within the agriculture sector.

Key companies such as Climate LLC, Granular, IBM Corporation, Ceres Imaging, and Deere & Company hold notable market shares, indicating a mix of technology-focused startups and established giants collectively shaping the landscape of computer vision solutions in agriculture.

As of 2022, significant players held around 30% of market share in the computer vision technology market for agriculture. These percentages underscore the influence of these companies in driving innovation and technology adoption in the agricultural sector, reflecting the evolving landscape of precision farming and broader application of computer vision solutions.


Computer Vision Technology Market for Agriculture

As the market matures, consolidation trends may emerge, influencing the competitive dynamics and further fueling the integration of computer vision technologies into modern farming practices.

The computer vision technology market for agriculture was valued at $432.0 million in 2023, and it is expected to grow at a CAGR of 16.78% and reach $2,037.5 million by 2033.

In the computer vision technology market for agriculture, this study encompasses companies that develop and provide software applications utilizing computer vision techniques for analyzing visual data related to agricultural processes. These software providers specialize in creating algorithms, image processing, object detection, and deep learning models tailored for agricultural applications.

The market is characterized by entities offering innovative solutions designed for the following applications such as biotic stress monitoring, harvest dynamic monitoring, autonomous equipment control, and abiotic stress monitoring in agriculture, with a focus on interpreting and extracting valuable insights from visual data captured through various imaging devices.  


Market Segmentation:

Segmentation 1: by Application

•    Biotic Stress Monitoring
•    Harvest Dynamic Monitoring 
•    Autonomous Equipment Control
•    Abiotic Stress Monitoring


Segmentation 2: by Product
•    Data Processing and Analytics
•    Operational Integration and Automation


Segmentation 3: by Region
•    North America: U.S. and Canada
•    Europe: France, Germany, Spain, Italy, Belgium, Bulgaria, Ukraine, U.K., and Rest-of-Europe
•    Asia-Pacific: China, India, Japan, Australia and Rest-of-Asia-Pacific
•    Latin America: Brazil and Mexico
•    Middle East and Africa: Turkey, South Africa, and Rest-of-Middle East and Africa


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Demand Drivers, Limitations, and Opportunities in Computer Vision Technology Market for Agriculture

Market Drivers: Advancement of Deep Learning Techniques

•    Deep learning (DL), a critical component of artificial intelligence, utilizes artificial neural networks (ANNs) to extract insights from large datasets and perform various tasks. This technology is particularly significant in computer vision (CV), which deals with the interpretation and processing of visual data. Deep learning has widespread applications in the agricultural sector.

•    In agriculture, deep learning aids in numerous operations, including crop monitoring, autonomous harvesting, weather analysis, animal health management, and plant disease detection.

•    For companies providing CV technology in agriculture, deep learning offers multiple benefits. It improves the accuracy and sophistication of models by leveraging insights from substantial data sets, mitigates the risks and costs associated with crop failures through detailed analysis of weather and soil conditions, and enhances crop yield and quality by detecting diseases and optimizing harvesting processes.


Market Challenges: Addressing Data Ownership and Privacy Concerns

•    The adoption of artificial intelligence (AI) and computer vision technologies in agriculture involves extensive data processing from remote sensors, UAVs (unmanned aerial vehicles), and satellites. Although many farmers are moving towards digitalization, a significant number remain reluctant to share their data. This hesitation stems from uncertainties surrounding legal aspects such as data ownership, privacy, cybersecurity, and liability. There have been instances where companies have improperly shared agricultural data, heightening privacy concerns among the farming community.

•    Key stakeholders in agricultural data encompass farmers, agri-tech providers, input suppliers, and government entities. The use of computer vision in agriculture introduces risks related to data breaches and the potential disclosure of personal and operational information to malicious entities. According to a survey by the American Farm Bureau Federation, more than 75% of farmers express concerns about who owns their data and how it might be used by regulators, while 60% are apprehensive about companies using their data to influence market decisions.


Market Opportunities: Advancing Digital Agriculture with Government Support

•    Governments worldwide are taking active steps to advance digital agriculture through initiatives that include public-private partnerships and creating supportive ecosystems for both established companies and startups to launch computer vision-based innovations. For instance, the U.S. Department of Agriculture has initiated various programs that utilize computer vision across different agricultural areas such as production, the food supply chain, sensor development, ecosystem management, and rural community support. These initiatives also encourage the use of robotics in functions like pollination, pesticide spraying, weeding, and harvesting. Additionally, the American Research Service is partnering with firms to incorporate computer vision and AI in applications like livestock monitoring, irrigation assessment, robotic harvesting, and drone-assisted crop health surveillance.

•    In a significant move in India, the state government of Karnataka signed a memorandum of understanding with Microsoft in 2007 to boost agricultural output using innovative technologies, primarily computer vision. Moreover, the Indian government collaborated with IBM to introduce the AI-based Watson Decision Platform for Agriculture, which provides precise weather predictions and soil data to aid local farmers in making better-informed decisions regarding crop and water management. This trend of integrating advanced technologies in agriculture is expected to expand worldwide, creating substantial opportunities in the IoT-enabled agricultural sector.


Key Market Players and Competition Synopsis

Companies that swiftly embrace technological advancements and foster effective collaborations are poised for significant growth and a leading position in this transformative market.


Some of the prominent companies in this market are:

•    Climate LLC
•    Granular Inc.
•    IBM Corporation
•    Ceres Imaging
•    Deere & Company
•    Prospera Technologies
•    AgEagle Aerial Systems Inc (MicaSense)
•    Taranis
•    PrecisionHawk, Inc.
•    Gamaya
•    Plantix
•    DJI
•    Iteris, Inc.
•    CropIn Technology Solutions
•    HIPHEN (SlantRange, Inc.)


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Analyst View

According to Debraj Chakraborty, Principal Analyst, BIS Research, “As the agriculture industry continues to embrace digital transformation, computer vision technology emerges as a transformative force, offering unprecedented insights and precision. The integration of computer vision in agriculture not only enhances crop monitoring and yield prediction but also cultivates a sustainable and data-driven approach. This technology is poised to revolutionize farming practices, optimizing resource utilization and empowering farmers to make informed decisions, marking a significant leap toward the future of smart and efficient agriculture.”

 
 
 

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