Market Context — Why This Technology, Why Now

The global agricultural sector faces immense pressure to increase yields and efficiency while reducing environmental impact. This drives significant investment into precision agriculture and automation. Reliable data collection via drones and autonomous vehicles is paramount. This technology supports this trend by ensuring data integrity in challenging environments, enabling more precise resource management and fostering sustainable farming practices worldwide.

Key Competitive Advantages
01

Enhances Recognition in Harsh Environments: Absorbent sponge surfaces absorb water and mud, significantly improving image recognition in rain or challenging terrain.

02

Enables Multi-Angle Data Acquisition: The thick 3D shape and side indicators allow for identification data, such as thickness information, to be acquired from different angles even if the top surface is obscured.

03

Offers Superior Durability and Versatility: Absorbent sponge is lightweight, flexible, and impact-resistant, ensuring long-term use and easy installation across diverse agricultural environments.

Market Opportunity
Smart Agriculture Solutions
$0.5B–$1.5B globally (AI est.)
Growing demand for labor-saving and productivity-enhancing solutions in agriculture is accelerating investment in AI and IoT-driven precision farming. This technology is essential for improving the accuracy and reliability of data collection, forming a critical foundation for these solutions.
Smart farming platform providers Agricultural IoT solution developers Data analytics firms for agriculture
Agricultural Machinery Manufacturers
$1B–$2B globally (AI est.)
As autonomous farm machinery and drones become more prevalent, there is increasing demand for visual identification technology that ensures accurate operation even in adverse weather. This technology offers a differentiating factor that enhances the value of next-generation agricultural machinery.
Autonomous tractor and drone manufacturers Agricultural equipment OEMs Farm robotics developers
Precision Agriculture Data Services
$5B–$10B globally (AI est.)
Services that collect and analyze detailed field data to recommend optimal farming practices are expanding. This technology plays a crucial role in improving the quality of data collection, thereby enhancing the reliability and value of these services.
Satellite and drone imaging service providers Agricultural data management platforms Crop monitoring and analytics companies
Construction and Civil Engineering Surveying
$250M–$450M globally (AI est.)
As indicated by the G01C15/06 IPC classification, reliable marker identification in adverse conditions is also critical in surveying. Applying this technology to drone surveying and autonomous heavy equipment guidance could improve operational efficiency and safety.
Construction equipment manufacturers Drone surveying service providers Civil engineering firms
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent provides robust protection for identification markers featuring an absorbent sponge display surface, a thick 3D shape, and side identification information. The claims are strong and comprehensive, having been granted after the examiner cited eight prior art documents, demonstrating clear differentiation and technical superiority over existing solutions.

Competitive White Space

The patent primarily covers physical marker design for image recognition. Licensees could develop additional IP in advanced image processing algorithms, AI-driven recognition software, or integrated sensor fusion systems that leverage these markers for enhanced autonomy.

Economic Impact
~$100K/year estimated operational cost reduction per large farm (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Implementing this technology could reduce image recognition failure rates from 20% to 5% for autonomous farm machinery and drones, cutting re-imaging and manual data entry/verification tasks by ~15% annually. For a large farm investing $650K (AI est.) annually in image recognition-related labor, this could result in an annual operational cost reduction of ~$100K (AI est.). Additional economic benefits may arise from increased harvest yields due to improved data collection accuracy.

Speed to Market
6× faster than in-house development
This technology's patent claims clearly define specific physical components like absorbent sponge and 3D shapes, reducing uncertainty in new development. It can be implemented using common materials and existing manufacturing processes, significantly shortening design and prototyping phases. While developing a similar adverse-environment marker in-house would require at least 3 years for material selection, design, and field testing, licensing this patent allows companies to focus on integration into existing production lines and optimizing material procurement, potentially reducing market entry time to approximately six months.
Competitive Positioning

X: Recognition Stability in Harsh Environments
Y: Versatility of Information Acquisition

Business Models & Applications
🚜 Product Integration Licensing
A model offering licenses to agricultural machinery manufacturers for integrating this technology as identification markers in image recognition systems for autonomous farm machinery and drones, enhancing product value.
📊 Solution Provision Licensing
A model where smart agriculture solution providers integrate this technology into their precision farming platforms and offer it to client farms, improving data collection reliability.
🏷️ Marker Sales and Rental Model
A model for manufacturing, selling, or renting high-performance markers utilizing this technology, directly providing stable identification in harsh environments to customers in agriculture and construction.
Adjacent Application Opportunities
🚧 Construction and Civil Engineering
Markers for Autonomous Heavy Equipment and Drone Surveying
Construction and civil engineering sites face challenges in surveying and guiding autonomous heavy equipment in rain or muddy conditions. Applying this technology could enable high-precision positioning and work instructions for drone-based progress monitoring and heavy equipment guidance in areas without GNSS, improving operational efficiency and safety.
🌲 Forestry and Environmental Surveys
Markers for Forest Management and Ecosystem Monitoring
Traditional markers in vast forests and natural environments have struggled with visibility and durability. This technology could enable stable identification even in wet conditions or obscured by vegetation, contributing to efficient and accurate data collection for drone-based tree management, wildlife habitat monitoring, and tracking environmental changes.
🚨 Disaster Sites and Infrastructure Inspection
Markers for Drone-Based Disaster Assessment
In disaster response and aging infrastructure inspection, drone-based image recognition is crucial for rapid situation assessment. This technology's markers could allow drones to accurately identify targets even when obscured by landslides or floods, efficiently pinpointing affected areas and diagnosing structural deterioration, potentially accelerating recovery efforts.
Integration Roadmap — Estimated 17-Month Deployment
Phase 1: Technology Validation and Requirements Definition
Duration: 3 months
Validate compatibility with the licensee's existing image recognition systems and operational environment, then define specific implementation requirements and target performance.
Phase 2: Prototype Development and Field Testing
Duration: 8 months
Develop a prototype marker based on this technology and conduct field trials in the licensee's actual farm or work environment to evaluate recognition accuracy, durability, and ease of installation.
Phase 3: Mass Production Design and Market Launch
Duration: 6 months
Incorporate feedback from field trials to establish a design and manufacturing process suitable for mass production. Subsequently, initiate market deployment as part of the licensee's products or services.
Technical Feasibility
This technology's patent claims clearly define specific physical components such as absorbent sponge, 3D shapes, and side identification information. This makes integration into existing marker manufacturing processes and material supply chains relatively straightforward, potentially avoiding significant new capital investment. Furthermore, it does not mandate special high-precision sensors or complex AI algorithms, demonstrating high compatibility with standard image recognition systems, thus indicating low technical barriers to adoption.
Success Scenario
Upon adoption, this technology could enable autonomous farm machinery and drones to maintain over 90% recognition accuracy for crop growth monitoring, even in rainy or muddy conditions. This is estimated to reduce manual data supplementation tasks by approximately 25% annually and shorten precision agriculture data collection cycles by 20%. Ultimately, this could contribute to cost reduction through optimized fertilizer and pesticide application, increased yields, and the realization of sustainable, efficient agricultural management.
Patent Record
APPLICATION NO.
特願2021-159341
REGISTRATION NO.
7668010
FILING DATE
2021/09/29
GRANT DATE
2025/04/16
EXPIRATION DATE
2041/09/29
PATENT HOLDER
国立研究開発法人農業・食品産業技術総合研究機構
Examination History
2024年06月12日
出願審査請求書
2025年03月17日
特許査定