Market Context — Why This Technology, Why Now

The global agricultural sector is undergoing a digital transformation, driven by demands for higher efficiency, reduced environmental impact, and improved resilience against climate shocks. Regulatory pressures for sustainable practices and consumer demand for transparent, high-quality produce are pushing adoption of smart farming solutions. This technology provides a critical tool for producers to optimize operations, meet sustainability goals, and gain a competitive edge in a rapidly evolving market.

Key Competitive Advantages
01

Increases prediction accuracy by ~10% compared to conventional average methods by using AI to learn and correct for differences in crop types and cultivation conditions.

02

Enables easy use for anyone, providing high-precision harvest prediction information without complex specialized knowledge, requiring only input of crop type and cultivation conditions, accessible even to non-expert farmers.

03

Supports data-driven management decisions by offering objective prediction data for agricultural management often reliant on experience, supporting optimal cultivation and shipping plan formulation, and reducing business risks.

Market Opportunity
Protected Horticulture
$1.0B globally (AI est.)
Rising demand for year-round production and reduced climate change risks necessitate precise environmental control and data utilization, directly enhancing productivity with this technology.
Large-scale greenhouse operators Vertical farm technology providers Controlled environment agriculture solution developers
Open-Field Cultivation
$20.0B globally (AI est.)
Efficient management of vast areas and rapid response to weather fluctuations are critical, making growth prediction valuable for planned operations and risk management.
Agricultural machinery manufacturers Large-scale crop producers Farm management software providers
Food Processing and Distribution
$465.0B globally (AI est.)
Stable raw material supply and quality control are paramount; growth prediction enhances procurement planning accuracy, contributing to food loss reduction and supply chain optimization.
Major food manufacturers Food logistics and supply chain companies Retail grocery chains
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a crop growth prediction method and program, specifically covering the AI-driven correction of reference data based on crop type and cultivation conditions. The claims were robustly defended through multiple office actions, indicating a strong and difficult-to-invalidate scope.

Competitive White Space

This patent primarily covers the prediction algorithm and data correction logic. White space exists in developing novel sensor hardware for data collection, integrating with autonomous agricultural robotics for automated interventions, or creating advanced visualization tools for complex farm management.

Economic Impact
~$1.5M–$6.0M/year estimated economic impact within the protected horticulture market (AI est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Improved prediction accuracy enables optimized fertilization, irrigation, and pest control, potentially increasing crop yields by an average of 5%. Additionally, precise harvest forecasts allow for planned shipments, reducing post-harvest waste by up to 10%.

Speed to Market
4× faster than in-house development
This technology's growth prediction algorithm is clearly established by patent and its basic operating principles are already verified. This significantly shortens development time compared to a licensee developing a similar system from scratch. The core technology for correcting reference data based on crop type and cultivation conditions can be rapidly implemented through integration with existing agricultural data platforms and IoT sensors, potentially reducing time to market by approximately 2.7 years.
Competitive Positioning

X: Prediction Accuracy and Stability
Y: Ease of Implementation and Versatility

Business Models & Applications
☁️ SaaS Prediction Service
Offers cloud-based growth prediction functionality. Deployable as a monthly or annual subscription service, making it accessible to various farmers and agricultural corporations.
🤝 Technology Licensing
A model for licensing this technology's prediction engine to existing smart agriculture platform providers and agricultural machinery manufacturers.
👨‍🏫 Consulting Partnership
A model integrating this technology as part of cultivation guidance and management improvement support services, in partnership with agricultural consulting firms.
Adjacent Application Opportunities
♻️ Food Waste Reduction
Supply-Demand Optimization for Retail & Distribution
Supermarkets and food manufacturers could more accurately forecast supply from contract farmers, minimizing over-ordering and stock-out risks. This has the potential to significantly reduce in-store waste and contribute to building sustainable supply chains.
🏙️ Smart Cities
Urban & Vertical Farm Efficiency
In urban and vertical farms with limited space, real-time crop growth prediction could optimize lighting, water, and nutrient supply. This is expected to maximize production efficiency while reducing energy consumption.
🧪 Biofuel & Pharmaceutical Raw Material Production
Functional Crop Harvest Optimization
Predicting the growth of crops for biofuel or functional crops containing specific medicinal compounds could identify optimal harvest times for maximum active ingredient yield. This has the potential to stabilize raw material quality and quantity, optimizing production costs.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Requirements Definition and Data Integration
Duration: 3 months
Define integration requirements with the licensee's existing cultivation data, environmental sensor data, and production management systems, then build the data acquisition and integration infrastructure.
Phase 2: System Development and Pilot Testing
Duration: 6 months
Integrate the technology's prediction program into existing systems based on requirements, conduct pilot deployments in specific fields or facilities, and verify prediction accuracy and effectiveness.
Phase 3: Full-Scale Deployment and Operational Optimization
Duration: 3 months
Optimize the system based on pilot results, proceed with full-scale company-wide deployment. Continuously improve the prediction model through operational data to maximize effectiveness.
Technical Feasibility
This technology is structured as an information processing program that inputs crop type and cultivation conditions to correct reference data, making it easy to implement into existing agricultural information processing devices and cloud platforms. The patent claims cover general computer processes such as information input, database reference, correction processing, and information output, indicating high compatibility with existing smart agriculture systems and potential for relatively low-cost integration via software updates or API linkage.
Success Scenario
Upon adopting this technology, licensees could achieve high-precision, data-driven harvest predictions, moving beyond reliance on experience. This may enable optimal fertilization and irrigation tailored to crop growth, potentially increasing average yields by 15%. Furthermore, accurate harvest forecasts could facilitate planned shipments, reducing market price volatility and an estimated 10% reduction in post-harvest waste. Ultimately, this could lead to enhanced profitability and sustainable agricultural operations.
Patent Record
APPLICATION NO.
特願2021-200711
REGISTRATION NO.
7750509
FILING DATE
2021/12/10
GRANT DATE
2025/09/29
EXPIRATION DATE
2041/12/10
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07日
出願審査請求書
2025年03月25日
拒絶理由通知書
2025年04月24日
意見書
2025年05月27日
拒絶理由通知書
2025年06月25日
意見書
2025年06月25日
手続補正書（自発・内容）
2025年07月15日
拒絶理由通知書
2025年08月22日
意見書
2025年08月22日
手続補正書（自発・内容）
2025年09月09日
特許査定