Market Context — Why This Technology, Why Now

The agricultural sector is undergoing a rapid digital transformation driven by the need for increased efficiency, sustainability, and resilience against climate variability. Rising consumer demand for consistent quality produce and increasing labor costs are pushing farms towards precision agriculture. This technology directly addresses these pressures by enabling optimized resource allocation and reduced post-harvest losses, critical for global food supply chain stability.

Key Competitive Advantages
01

Maximize Yields with High-Precision Growth Prediction: Analyzes daily average temperature, day length, solar radiation, and leaf information to predict onion bulb dry weight and harvest date with over 95% accuracy. This ensures optimal harvest timing, maximizing yields.

02

Reduce Costs by ~10% and Stabilize Quality: Optimizes cultivation management, including fertilizer and irrigation, through precise growth prediction. This could reduce unnecessary input materials, potentially cutting production costs by approximately 10%, while stabilizing quality.

03

High Versatility for Bulbous Allium Crops: Designed with a concept applicable to all bulbous allium crops (e.g., green onions, garlic), despite being specialized for onions. This offers potential for business expansion through multi-product deployment.

Market Opportunity
🌱 Open-Field Agriculture
$350M–$13.5B globally (AI est.)
Open-field cultivation of crops like onions and green onions is highly susceptible to weather, making yield prediction difficult. This technology enables precise cultivation planning and harvest optimization, directly leading to stable profits and increased adoption.
Large-scale open-field vegetable growers Agricultural cooperatives and grower associations Smart farming solution providers for field crops
🏭 Food Processing & Distribution
$200M–$6.5B globally (AI est.)
A stable supply of raw materials is crucial for the food processing industry. High-precision yield prediction from this technology contributes to optimizing procurement plans and streamlining the supply chain, reducing food waste and cutting costs.
Food processing companies requiring stable raw material supply Large-scale food distributors Supply chain optimization software developers
📊 Agricultural Data Platforms
$150M–$3.5B globally (AI est.)
Integrating this technology into existing agricultural IoT platforms and farm management systems could significantly enhance service value, accelerating agricultural digital transformation through data utilization.
Agricultural IoT platform developers Farm management software companies Data analytics providers for agriculture
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent specifically protects a crop growth prediction method and program, as detailed in claim 5. Its technical reliability is high, stemming from its origin as a national research institute application. The patent's novelty and inventiveness were thoroughly verified against four prior art documents during examination, indicating a robust and stable right.

Competitive White Space

The patent focuses on growth prediction for bulbous allium crops. White space exists in integrating advanced soil sensor data or real-time pest/disease prediction models, allowing licensees to build complementary IP.

Economic Impact
~$250K/year estimated revenue improvement per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming a 100-hectare onion farm implements this technology, achieving a 10% yield improvement and a 5% reduction in waste loss. With an average onion price of $0.33/kg (AI est.) and an average yield of 50 tons/ha, the revenue per hectare is $16.5K (AI est.). The annual revenue improvement is calculated as $16.5K/ha × (0.10 yield improvement + 0.05 waste reduction) × 100ha = ~$250K/year (AI est.).

Speed to Market
7× faster than in-house development
Developing and validating a similar growth prediction model in-house from scratch would require at least 3.5 years for data collection, algorithm development, and model verification, along with substantial investment. This technology, based on established algorithms and technical knowledge from a national research institute, can be integrated with existing weather and cultivation data for system deployment and operational launch in approximately 0.5 years. Academic validation is complete, enabling rapid market entry and competitive advantage.
Competitive Positioning

X: Prediction Accuracy & Reliability
Y: Cost-Effectiveness & Ease of Adoption

Business Models & Applications
☁️ SaaS Prediction Service
Offer a SaaS for agricultural businesses to predict crop growth status, yield, and harvest timing. A monthly or annual subscription model could provide stable revenue.
🔗 API Integration & Licensing
Provide this prediction algorithm via API or license to agricultural machinery manufacturers and smart farming solution providers, facilitating integration into existing systems.
🤝 Consulting Package
Offer comprehensive consulting services for large-scale farms and agricultural corporations, focusing on optimizing farming plans, data analysis, and productivity improvements using this technology.
Adjacent Application Opportunities
🥕 Other Root & Leafy Vegetables
Extension to Precision Crop Management Systems
Extend this technology's prediction model to other major root and leafy vegetables like potatoes, radishes, and cabbage. This could expand the target crops and open up broader agricultural market opportunities, potentially building a versatile model combining common weather data with crop-specific growth characteristics.
🛰️ Satellite Data Integrated Agriculture
Remote Sensing Integrated Prediction
Integrate remote sensing data (e.g., leaf color, growth images from drones or satellites) with this technology's prediction model. This could create a system capable of monitoring and predicting growth status over vast agricultural areas with higher precision, enhancing decision support for large-scale farming operations.
🌍 Smart City & Regional Revitalization
Regional Agricultural DMP Support
Incorporate this technology as part of a Data Management Platform (DMP) that aggregates and analyzes agricultural data across an entire region. This could contribute to optimal planting plans, logistics optimization, and climate change adaptation strategies for specific regions, supporting sustainable food supply systems and regional agricultural DX.
Integration Roadmap — Estimated 18-Month Deployment
Phase 1: Technical Validation & Data Linkage
Duration: 3 months
Verify compatibility with the licensee's existing cultivation data (weather, growth records) and design APIs or data flows to link with this technology's prediction model.
Phase 2: Model Adjustment & Pilot Operation
Duration: 6 months
Fine-tune the prediction model for the licensee's specific cultivation environment and crop varieties. Initiate pilot operations in limited fields to evaluate prediction accuracy and real-world effectiveness.
Phase 3: Full Deployment & Optimization
Duration: 9 months
Based on pilot results, fully deploy the system. Continuously feed data back to further improve the prediction model's accuracy and optimize operations for maximum profitability.
Technical Feasibility
This technology is structured as a program that processes daily weather data and leaf values, making software integration with existing agricultural IoT platforms and farm management systems straightforward. The processes for calculating development index, dry matter distribution, and harvest date/yield estimation, as described in the claims, can be implemented on standard server environments or cloud infrastructure, requiring no significant new hardware investment, thus presenting low technical adoption barriers.
Success Scenario
Upon adopting this technology, licensees could predict onion harvest times and yields with significantly higher accuracy. This may optimize harvest planning, potentially improving labor allocation efficiency by approximately 15%. Furthermore, it could enable supply adjustments based on market demand fluctuations, potentially reducing waste loss by up to 20% and leading to stable annual revenue growth.
Patent Record
APPLICATION NO.
特願2020-206604
REGISTRATION NO.
7461650
FILING DATE
2020/12/14
GRANT DATE
2024/03/27
EXPIRATION DATE
2040/12/14
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
2023年05月25日
出願審査請求書
2024年03月12日
特許査定