Market Context — Why This Technology, Why Now

The global agricultural sector faces immense pressure to increase output sustainably amidst resource scarcity and climate volatility. Rising consumer demand for ethically produced food and stricter environmental regulations are driving investment in AgriTech. This technology offers a critical solution for optimizing resource use, reducing chemical runoff, and ensuring food security, aligning with global efforts towards sustainable development goals and enhancing operational resilience for large-scale farming.

Key Competitive Advantages
01

Derives high-precision fertilization amounts across entire fields

02

Reduces manual data collection time by ~30% through data fusion

03

Lowers environmental impact by preventing excessive fertilizer use

Market Opportunity
Precision Agriculture Solutions
$1B–$1.5B globally (AI est.)
Accelerated evolution of AI/IoT technologies and the shift towards data-driven agriculture are rapidly increasing demand for fertilizer optimization and yield prediction.
AgriTech software developers Farm management platform providers Large-scale agricultural enterprises
Agricultural Machinery & AgriTech
$0.5B–$1B globally (AI est.)
High potential for this technology to be integrated as a software core in next-generation agricultural machinery, leveraging synergies with drones and autonomous farm vehicles.
Agricultural equipment manufacturers Drone technology developers for agriculture Autonomous vehicle system integrators
Environmental & Sustainability Solutions
$15B–$20B globally (AI est.)
Reducing environmental impact from excessive fertilization is a global challenge, and contributing to SDG achievement directly enhances corporate value.
Environmental consulting firms Sustainable agriculture initiatives Food and beverage companies with sustainability goals
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a core invention related to a correction mechanism that combines wide-area remote data with narrow-area on-site data to derive crop-related values. With 25 claims, it secures a broad and robust scope of protection, having successfully navigated examiner objections, ensuring high stability for licensees.

Competitive White Space

This patent focuses on data fusion for crop value derivation. Licensees could develop new hardware for data collection (e.g., novel sensor designs) or advanced AI models for predictive analytics beyond optimal fertilization.

Economic Impact
~$1M/year estimated fertilizer cost reduction and yield increase (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

For a large farm (100ha), assuming an annual fertilizer cost of ~$1,000/ha (AI est.), total annual fertilizer costs are ~$100,000 (AI est.). Precision fertilization could reduce fertilizer use by 10%, saving ~$10,000/year (AI est.). A conservative 10% yield increase, assuming annual revenue of ~$6,500/ha (AI est.), could contribute ~$65,000/year (AI est.) in increased revenue. If 10,000 major farms adopt this technology, the total annual economic impact could reach ~$1M (AI est.).

Speed to Market
4× faster than in-house development
This technology features an established algorithm for integrating remote sensing and on-site measurement data, with a clearly defined operational principle based on the patented apparatus configuration. Specific data correction logic is also provided, indicating that the technical validation phase is complete. Adopting companies can leverage existing drone and satellite imagery data, along with general-purpose soil and crop sensors, to focus on software development and system integration, potentially shortening time-to-market by approximately 2.7 years compared to in-house development.
Competitive Positioning

X: Data Utilization Efficiency
Y: Precision Fertilization Accuracy

Business Models & Applications
🤝 Technology Licensing
License the technology as a software module to agricultural machinery manufacturers and AgriTech companies, promoting its integration into existing products.
☁️ SaaS Data Analytics Service
Offer a SaaS-based precision fertilization recommendation system, integrating remote and on-site data, to farmers for subscription revenue.
📈 Consulting & Solutions
Provide agricultural DX consulting and system integration services, leveraging this technology for large-scale farming corporations and smart city projects.
Adjacent Application Opportunities
🌲 林業・緑地管理
Forest Resource & Tree Health Monitoring
Integrate wide-area drone data with detailed on-site measurements (tree height, trunk circumference, disease indicators) from select trees to accurately estimate overall forest resources and health. This could inform optimal thinning plans and disease prevention strategies.
🏞️ インフラ維持管理
Wide-Area Infrastructure Degradation Prediction
Periodically monitor extensive infrastructure like bridges and roads using remote sensing, combining it with detailed inspection data from specific points. This could efficiently derive degradation progression and prioritize repair needs.
🏘️ スマートシティ開発
Urban Greening & Park Management Optimization
Analyze wide-area images of urban parks and street trees, complemented by on-site measurements of plant growth and soil data. This could determine optimal timing for watering, fertilizing, and pruning, enabling efficient and sustainable urban green space management.
Integration Roadmap — Estimated 18-Month Deployment
Phase 1: Technology Validation & Prototype Development
Duration: 6 months
Validate the technology's algorithm using the adopting company's existing data (e.g., drone imagery, soil data) and develop a prototype system tailored to specific crops and field environments.
Phase 2: Pilot Testing & System Optimization
Duration: 9 months
Evaluate system accuracy and stability through real-world field trials. Optimize the system by adjusting algorithms and improving UI/UX based on on-site feedback.
Phase 3: Production Deployment & Scaling
Duration: 3 months
Deploy the optimized system into a production environment and commence operations. Based on initial success, explore expanding its application and integrating it into other business areas to maximize impact.
Technical Feasibility
This technology's core is a software-based algorithm that integrates optical observation data (first indicator) from remote sources with on-site instrument data (second indicator). The patent claims detail the data processing flow and correction mechanisms, making it implementable on general-purpose image analysis and data processing platforms. It can utilize existing hardware such as drones, satellite imagery services, and commercial soil/chlorophyll sensors, thus avoiding large-scale new equipment investment and enabling relatively low-cost integration into existing agricultural management systems.
Success Scenario
Upon adoption, companies could automatically determine optimal fertilization amounts across vast farmlands, guided by AI rather than relying on skilled labor. This is expected to reduce fertilizer costs by 10-20% annually while potentially increasing yields by up to 15%. Furthermore, significantly reduced human resources for data collection could allow workers to focus on higher-value tasks, thereby improving overall productivity.
Patent Record
APPLICATION NO.
特願2020-180673
REGISTRATION NO.
6960698
FILING DATE
2020/10/28
GRANT DATE
2021/10/14
EXPIRATION DATE
2040/10/28
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
2021年03月02日
早期審査に関する事情説明書
2021年03月02日
出願審査請求書
2021年05月18日
拒絶理由通知書
2021年05月18日
早期審査に関する通知書
2021年07月09日
手続補正書（自発・内容）
2021年07月09日
意見書
2021年09月28日
特許査定