Market Context — Why This Technology, Why Now

The global push for sustainable agriculture and food security is driving massive investment into smart farming solutions, projected to grow at an 18.5% CAGR. Concurrently, environmental monitoring and critical infrastructure inspection face increasing demands for efficiency and accuracy. This technology provides a scalable, AI-driven solution to meet these challenges, enabling precise resource management, early detection of issues, and significant operational cost reductions across multiple industries.

Key Competitive Advantages
01

Replicates expert knowledge with AI, enabling objective and highly precise selection of representative points.

02

Reduces survey costs by up to ~30% by replacing extensive on-site surveys with aerial image analysis.

03

Enhances analysis precision by integrating multiple image types, including visible light and multispectral data.

Market Opportunity
🌾 Smart & Precision Agriculture
$1.5B–$2.5B globally (AI est.)
Efficiency improvements in agricultural production, driven by AI and IoT, are a global priority for food security and environmental impact reduction, accelerating the shift towards data-driven farming.
Large-scale agricultural enterprises Smart farming solution providers Agricultural drone manufacturers
🌳 Environmental Monitoring
$600M–$700M globally (AI est.)
There is a growing need for efficient, wide-area monitoring of ecosystem changes using aerial imagery and AI to address environmental issues such as deforestation, water pollution, and biodiversity loss.
Environmental consulting firms Government environmental agencies Satellite imagery providers
🏗️ Infrastructure Inspection & Disaster Monitoring
$600M–$700M globally (AI est.)
The efficient inspection of aging infrastructure and rapid assessment of damage during large-scale disasters urgently require the application of aerial image analysis technology.
Infrastructure maintenance companies Disaster response technology providers Civil engineering firms
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent provides broad protection for an information processing apparatus, method, and program, covering the AI-driven selection of representative ground survey points from multiple aerial images using unsupervised classification and smoothing. Its claims were established after overcoming multiple prior art rejections, demonstrating clear differentiation and a robust, difficult-to-invalidate intellectual property foundation.

Competitive White Space

This patent primarily covers the AI-driven selection of ground survey points from aerial imagery. It leaves white space for developing specialized sensor hardware for data acquisition or integrating with advanced predictive modeling for specific agricultural outcomes beyond initial point identification.

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

Implementing this technology could reduce on-site survey work for 100-hectare farmlands from 5 person-days to 3 person-days. At an estimated cost of ~$330/person-day (AI est.), direct labor cost savings could reach ~$66K (AI est.) per 100ha. Including efficiency gains from data analysis and reduced harvest losses from early detection, the total economic impact is estimated at ~$100K/year (AI est.) per facility.

Speed to Market
6× faster than in-house development
This technology features a clearly established algorithm with distinct modules for image acquisition, classification, smoothing, and representative point selection. Proof-of-concept and fundamental research phases are complete, and integration with existing drone or satellite imaging systems is relatively straightforward. This allows licensees to achieve the fastest possible market entry, significantly reducing development time and securing approximately 2.5 years of time-to-market advantage compared to developing a similar system from scratch.
Competitive Positioning

X: Survey Efficiency
Y: Analysis Precision

Business Models & Applications
☁️ SaaS Data Analysis Platform
Licensees upload aerial images from drones or satellites to a cloud platform, receiving analysis results via SaaS. This could operate on a monthly subscription or usage-based fee model.
🤝 Licensing Model
Grant licenses for the core algorithm or software to existing smart agriculture solution providers or drone service companies, collecting royalties.
🔬 Joint Development & Customization Services
Collaborate with specific agricultural corporations or local governments for customized development tailored to specific crops or regional characteristics, generating revenue from implementation and ongoing maintenance fees.
Adjacent Application Opportunities
🚨 Disaster & Emergency Response
Automated Post-Disaster Damage Mapping
After large-scale disasters, this technology could analyze multi-spectral drone imagery (visible, thermal, SAR) to automatically classify and map building damage, flood zones, and landslides. This has the potential to aid in prioritizing rescue efforts and developing infrastructure recovery plans.
🏙️ Urban Development & Real Estate
Undeveloped Land Suitability Assessment
For vast undeveloped areas, this technology could analyze satellite and aerial photos to automatically classify land suitability based on soil type, vegetation, drainage, and slope. This has the potential to support optimal development planning, environmental impact assessments, and real estate valuation.
⛏️ Resources & Energy
Environmental Change Monitoring in Mining/Oil Regions
Regularly monitoring vast areas around mining and oil fields with aerial imagery, this technology could automatically detect anomalies like vegetation changes, ground deformation, or illegal dumping. This has the potential to aid in environmental impact assessment, optimize resource management, and enhance safety.
Integration Roadmap — Estimated 15-Month Deployment
Phase 1: Technology Validation & PoC
Duration: 4 months
Utilize existing licensee data or aerial data from specific test areas to validate the technology's analytical precision and practical utility. Initial system requirements definition will also be completed during this phase.
Phase 2: System Development & Prototype Build
Duration: 7 months
Based on validation results, develop a prototype system including integration with the licensee's existing systems (e.g., drone operation platforms, GIS, agricultural management systems).
Phase 3: Production Deployment & Optimization
Duration: 4 months
Deploy the developed system into a production environment and optimize operations based on field feedback. Prepare for large-scale rollout and establish a continuous improvement plan.
Technical Feasibility
This technology is a software-based solution centered on aerial image data and AI-driven image processing. Its modular structure, comprising image acquisition, classification, smoothing, and representative point selection units, is well-defined. Integration with existing drone or satellite image acquisition systems is relatively straightforward via standard APIs and common image formats (e.g., JPEG, TIFF). No major hardware changes or specialized capital investment are required, making software implementation technically feasible in existing cloud or on-premise environments.
Success Scenario
Implementing this technology could reduce the time required for extensive farmland growth monitoring by over 50%. This would enable more frequent and detailed monitoring, potentially leading to approximately 10% annual savings in material costs through optimized fertilizer and pesticide use. Furthermore, early detection and management of pests and diseases could curb harvest losses by up to 20%, ultimately driving significant productivity improvements.
Patent Record
APPLICATION NO.
特願2022-046523
REGISTRATION NO.
7720091
FILING DATE
2022/03/23
GRANT DATE
2025/07/30
EXPIRATION DATE
2042/03/23
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12月17日
出願審査請求書
2024年12月17日
早期審査に関する事情説明書
2025年01月14日
早期審査に関する通知書
2025年03月05日
拒絶理由通知書
2025年04月23日
意見書
2025年04月23日
手続補正書（自発・内容）
2025年06月24日
特許査定