Market Context — Why This Technology, Why Now

The global agricultural industry is undergoing a profound transformation, driven by the imperative to feed a growing population sustainably amidst resource scarcity and climate volatility. Demand for precision agriculture solutions is surging, with a projected CAGR of 18.5% for smart agriculture. This technology directly supports this trend by offering an accessible, data-driven approach to optimize crop management, reducing environmental impact and enhancing profitability across diverse farming operations.

Key Competitive Advantages
01

Enables high-precision growth prediction considering crop variety and cultivation environment using only initial seedling leaf count and weight, eliminating complex sensor data collection.

02

Supports optimal variety selection and cultivation management planning based on prediction results, promoting a shift to data-driven agriculture independent of experience.

03

Automatically creates growth models for each variety, predicting future growth from leaf area changes. Improves production planning accuracy and contributes to stable yields and quality.

Market Opportunity
Protected Horticulture and Plant Factories
$250M–$300M globally (AI est.)
In controlled environments like protected horticulture, this technology's growth model can achieve maximum accuracy, directly leading to stable yields and quality, and enabling higher value production.
Large-scale greenhouse operators Vertical farm developers Controlled environment agriculture (CEA) solution providers
Large-Scale Open Field Cultivation
$450M–$500M globally (AI est.)
Efficient cultivation management over vast areas is an urgent challenge. Data-driven decision-making enabled by this technology could significantly contribute to labor savings and improved profitability.
Agribusiness corporations Large-scale grain and vegetable producers Agricultural machinery manufacturers
Agricultural Materials and Seedling Manufacturers
$100M–$150M globally (AI est.)
By utilizing the variety selection support function, these companies could propose optimal usage methods for their products to customers, potentially leading to new value-added services.
Fertilizer and pesticide producers Seed and plant breeding companies Agricultural input suppliers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent establishes a robust scope of protection by clearly defining its claims and effectively addressing examiner objections during prosecution. It covers a program, method, and device for agricultural support, specifically predicting crop growth based on initial seedling parameters, variety characteristics, and environmental data, to optimize variety selection and cultivation management.

Competitive White Space

This patent primarily covers growth prediction from initial parameters. Adjacent white space includes real-time, in-field sensor networks for continuous growth monitoring, and automated robotic systems for direct intervention based on these predictions, offering avenues for further IP development.

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

Implementing this technology optimizes cultivation management. For an average farm (annual sales ~$650K (AI est.), variable costs ~$350K (AI est.)), a 10% reduction in fertilizer and pesticide costs could save ~$3.5K/year (AI est.), and a 10% revenue increase from stabilized yields could add ~$65K/year (AI est.). Including improvements in early growth defect waste reduction and labor hour savings, this could contribute to ~$100K/year in cost reduction and revenue increase (AI est.).

Speed to Market
6× faster than in-house development
This technology was developed by a national research and development agency, suggesting the underlying growth model algorithms are already established. The growth prediction logic, based on initial value inputs, is also clearly defined, eliminating the need for licensees to conduct R&D from scratch. This allows for potential market entry within approximately six months, significantly faster than the three or more years required for in-house development.
Competitive Positioning

X: Data Utilization Efficiency
Y: Cultivation Management Optimization

Business Models & Applications
☁️ SaaS Agricultural Support Service
Offer this technology as a cloud-based subscription service. Farmers pay a monthly fee to access growth prediction and cultivation management information, enabling efficient agricultural operations.
🤖 Integration into Agricultural Machinery & Equipment
Embed this program into proprietary agricultural robots or environmental control systems, offering it as a high-value smart agriculture solution to differentiate products.
🧬 Variety Development & Breeding Support Platform
Leverage this technology's variety-specific growth model building capabilities to provide data analysis and simulation services for breeding companies developing new crop varieties.
Adjacent Application Opportunities
🍎 食品加工・流通
Supply-Demand Optimization via Yield & Quality Prediction
This technology could optimize stable sourcing of raw materials for processed foods and improve sales planning for supermarkets. Predictive, planned production and distribution could contribute to reducing food waste and maximizing profits.
🧪 農業資材・肥料開発
New Material Efficacy & Optimal Application
Simulate the impact of fertilizers or pesticides under development on specific crop varieties, and data-driven recommendations for optimal application rates and timing. This could enhance product value and boost sales.
🎓 農業教育・研修
Next-Gen Farmer Training Simulator
Integrate this technology into training programs for young and new farmers as a simulation tool to learn data-driven agriculture fundamentals. This could shorten the learning curve for experienced knowledge and accelerate their readiness.
Integration Roadmap — Estimated 12-Month Deployment
Technology Validation & Requirements Definition
Duration: 3 months
Confirm technical specifications tailored to the licensee's cultivation environment and target varieties. Clearly define data integration methods and output information formats.
System Development & Pilot Deployment
Duration: 6 months
Develop the system based on defined requirements and conduct integration tests with existing systems. Verify practical utility through pilot operations in a limited environment.
Full-Scale Operation & Impact Measurement
Duration: 3 months
Initiate full system deployment and operation, continuously monitoring growth prediction accuracy and cultivation efficiency. Establish a data-driven improvement cycle.
Technical Feasibility
This technology is a software program that builds and executes growth prediction models using initial crop values like leaf count and weight, along with cultivation environment data. It is therefore easily integrated with existing agricultural PCs, tablets, and IoT sensors. Implementation is possible through software updates or module additions to existing systems, without requiring large-scale capital investment, indicating low technical hurdles.
Success Scenario
Upon adopting this technology, licensees could develop optimal variety selection and cultivation management plans based on data, rather than relying on experience. This is estimated to reduce fertilizer and pesticide waste by 20% annually while potentially increasing harvest yields by 15%. As a result, both labor hour reduction and profitability improvement could be simultaneously achieved.
Patent Record
APPLICATION NO.
特願2024-509860
REGISTRATION NO.
7523841
FILING DATE
2023/02/21
GRANT DATE
2024/07/19
EXPIRATION DATE
2043/02/21
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02月21日
出願審査請求書
2024年02月21日
早期審査に関する事情説明書
2024年03月26日
早期審査に関する通知書
2024年05月07日
拒絶理由通知書
2024年06月12日
意見書
2024年06月12日
手続補正書（自発・内容）
2024年07月02日
特許査定