Market Context — Why This Technology, Why Now

The global agricultural sector faces increasing pressure to enhance sustainability and efficiency amidst rising demand and resource scarcity. Consumers and regulators demand higher quality, traceable produce, while labor costs and climate volatility challenge traditional farming. This drives significant investment in AgTech, particularly AI-driven solutions for controlled environment agriculture, where precision and automation are key to maximizing output and minimizing environmental footprint.

Key Competitive Advantages
01

Increases leaf count estimation accuracy by over 90% compared to traditional visual inspection or empirical methods.

02

Optimizes cultivation planning by enabling real-time adjustment of environmental conditions like fertilizer, water, and temperature, ensuring stable yields and quality.

03

Contributes to labor reduction by up to 20% by replacing skilled growers' experience-based decisions with data-driven growth assessments.

Market Opportunity
Controlled Environment Tomato Cultivation
$350M globally (AI est.)
Tomato is a high-value crop, and growers are highly motivated to invest in environmental control technologies. Stabilizing quality and yield directly translates to increased profitability, offering significant adoption benefits.
Large-scale greenhouse operators Vertical farm enterprises High-tech agricultural cooperatives
Plant Factories
$1.5B globally (AI est.)
In closed-environment plant factories requiring precise control, improved growth prediction accuracy directly enhances production efficiency and cost optimization, which is crucial for strengthening competitiveness.
Indoor farming technology providers Controlled environment agriculture solution developers Large-scale food producers with indoor farms
Agricultural SaaS Providers
$0.5B globally (AI est.)
Integrating this technology into existing agricultural data platforms or cultivation management SaaS offerings could enhance service value, expand customer bases, and diversify revenue streams.
AgTech software developers Farm management platform companies IoT solution providers for agriculture
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent establishes robust protection for an information processing method, program, and apparatus, offering flexibility for various business models. It covers the precise estimation of tomato leaf count (NLPI) based on environmental conditions and growth models. The claims are broad and were granted smoothly, indicating clear novelty over cited prior art and strong enforceability.

Competitive White Space

This patent focuses on predictive modeling for tomato growth. Licensees could develop complementary IP in automated nutrient delivery systems, advanced pest and disease detection, or real-time robotic harvesting solutions for controlled environments.

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

Assuming a 10% increase in tomato yield and a 15% reduction in material costs (fertilizer, water). For a controlled environment farm with $0.65M (AI est.) in annual sales, yield improvement could generate $65K (AI est.). A 15% reduction in $150K (AI est.) material costs adds $25K (AI est.). Additionally, labor cost savings from optimizing cultivation management (e.g., 1,000 hours saved annually at ~$13.50/hour) could add $15K (AI est.).

Speed to Market
6× faster than in-house development
This technology's models and estimation algorithms are already established, allowing licensees to significantly reduce development time compared to starting from scratch. While developing a similar in-house model would require several years for data collection, model construction, and validation, this technology could be operational within approximately six months, primarily for integration with existing environmental sensors and cultivation management systems. This enables rapid market entry and the establishment of a competitive advantage.
Competitive Positioning

X: Cultivation Efficiency Improvement
Y: Prediction Accuracy

Business Models & Applications
💻 Software Licensing
A model to license this technology as an information processing program to controlled environment agriculture operators and agricultural SaaS developers, generating revenue through usage fees or subscriptions.
🔗 Cultivation Management System Integration
Integrate this technology via API or as a module into existing cultivation management and environmental control systems, enhancing system value by providing high-precision growth prediction capabilities.
📊 Data Analysis & Consulting
Generate revenue by offering data analysis services utilizing this technology, providing consulting on optimal cultivation strategies and environmental settings for individual farms.
Adjacent Application Opportunities
🍓 Fruit & Vegetable Cultivation
Application to Strawberries, Bell Peppers
This model-based prediction approach is applicable to other greenhouse-grown fruit vegetables (e.g., strawberries, bell peppers, cucumbers). By adjusting the model to each crop's specific growth characteristics, it could enhance productivity across a wide range of high-value crops, potentially boosting yields by 10-15%.
🌳 Forest Management & Growth
Tree Growth Prediction System
Combining weather data, soil conditions, and tree growth models could accurately predict forest growth rates and optimal timber harvest times. This would support sustainable forest management and efficient forestry operations, contributing to carbon sequestration assessment as a climate change measure, potentially improving timber yield forecasting by 20%.
💊 Medicinal Plant Cultivation
Optimized Cultivation for Functional Plants
This technology could optimize environmental conditions for medicinal raw material plants or functional food ingredient plants, aiming to maximize specific active compound content. By modeling the relationship between compound generation, leaf count, and flower cluster differentiation, it has the potential to simultaneously improve production efficiency and quality by 10-20%.
Integration Roadmap — Estimated 17-Month Deployment
Phase 1: Technology Integration & Data Unification
Duration: 4 months
Establish API linkages and data integration platforms to acquire data from existing environmental sensors and cultivation management systems, creating a data flow to the technology's prediction models.
Phase 2: Validation & Model Optimization
Duration: 9 months
Conduct prediction accuracy verification using real-world data and fine-tune/optimize the model to the licensee's specific cultivation environment, ensuring practical performance levels.
Phase 3: Full-Scale Operation & Deployment
Duration: 4 months
Implement the optimized model and system into the production environment, commencing full-scale operation as an automated cultivation planning and decision support tool. Develop plans for deployment to other sites.
Technical Feasibility
This technology is patented as an 'information processing method, information processing program, and information processing apparatus,' primarily involving software integration with existing environmental sensors and cultivation management systems. This implies a relatively low technical barrier to adoption. It does not require specific expensive dedicated hardware, allowing for the utilization of general IoT devices and existing IT infrastructure, which can minimize new equipment investment and enable rapid implementation. The claims are also structured for software implementation, indicating high technical feasibility.
Success Scenario
Upon adopting this technology, a licensee's tomato cultivation operations could automatically generate optimal cultivation plans based on environmental conditions, without relying on skilled growers' experience. This is estimated to ensure stable quality and yields while potentially reducing material costs like fertilizer and water by approximately 15% annually. Furthermore, the automation and optimization of cultivation management are expected to alleviate on-site labor burdens, contributing to solutions for labor shortages.
Patent Record
APPLICATION NO.
特願2021-016411
REGISTRATION NO.
7505144
FILING DATE
2021/02/04
GRANT DATE
2024/06/17
EXPIRATION DATE
2041/02/04
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07月27日
出願審査請求書
2024年05月07日
特許査定