Market Context — Why This Technology, Why Now

The agricultural sector is undergoing a rapid transformation driven by the need for sustainable practices, increased efficiency, and resilience against climate change. Consumers demand consistent quality and reduced food waste, while labor shortages push for greater automation. This technology enables growers to meet these demands by providing data-driven insights for optimal resource allocation and yield management, positioning them competitively in a market increasingly valuing precision and predictability.

Key Competitive Advantages
01

Predicts cultivation periods with over 90% accuracy by calculating durations for multiple growth stages based on fruit surface temperature from flowering to harvest.

02

Optimizes cultivation environments, such as temperature, humidity, and CO2 concentration, for each growth stage based on prediction results, maximizing yield and quality.

03

Ensures a stable IP foundation, with patentability confirmed against 8 prior art documents, securing business certainty by overcoming rigorous examiner scrutiny.

Market Opportunity
Smart Greenhouses and Plant Factories
$300M–$350M+ globally (AI est.)
The integration of environmental control technology with data analytics is driving strong demand for maximized production efficiency and quality uniformity. This technology is expected to enhance prediction accuracy and serve as a core component of automated cultivation management systems.
Controlled environment agriculture (CEA) operators Greenhouse technology providers Vertical farm developers
Large-Scale Open-Field Cultivation
$150M–$200M+ globally (AI est.)
In open-field cultivation, directly impacted by climate change, accurate harvest timing predictions directly affect profitability. This technology contributes to risk management and optimized operational planning, becoming a crucial tool for stabilizing revenue.
Large-scale commercial farms Agricultural machinery manufacturers Crop insurance providers
Food Processing and Distribution
$200M–$250M+ globally (AI est.)
Accurate harvest timing predictions are essential for optimizing production planning in processed foods and streamlining distribution routes. This technology contributes to overall supply chain efficiency and food waste reduction, fostering new value creation.
Food processing companies Fresh produce distributors Supply chain logistics providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent broadly covers a stage-specific prediction algorithm based on fruit temperature and an environmental adjustment method utilizing it, across 10 claims. The patent successfully navigated a rejection, indicating a robust and clearly defined scope of protection that is less susceptible to invalidation.

Competitive White Space

This patent focuses on fruit temperature-based prediction and environmental control. White space exists in integrating with advanced soil and nutrient sensors, developing disease detection algorithms, or automating robotic harvesting systems.

Economic Impact
~$150K/year estimated revenue opportunity expansion per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Optimizing harvest timing could reduce waste from 10% to 3% (7% reduction) and increase yield by 5%. For a farm with $1.5M (AI est.) annual sales, this equates to ($1.5M (AI est.) × 0.07) + ($1.5M (AI est.) × 0.05) = ~$180K (AI est.) in annual profit improvement. Additional labor optimization contributes to over ~$150K (AI est.) total economic benefit.

Speed to Market
5× faster than in-house development
Developing and validating a similar algorithm in-house, from data collection to model construction and verification, is estimated to take at least 4 years. This technology, developed by a national research institute, has established patented prediction algorithms and environmental adjustment logic, offering high technical reliability and abundant validation data. This allows licensees to focus on integration into existing cultivation systems, enabling market entry in approximately 8 months and significantly shortening development timelines.
Competitive Positioning

X: Cultivation Management Automation Level
Y: Harvest Prediction Accuracy

Business Models & Applications
☁️ SaaS Prediction Service
Offer this technology as a cloud-based SaaS, providing cultivation period prediction and environmental adjustment recommendations to farmers and agricultural corporations on a monthly subscription basis.
💡 Agricultural IoT Solution
Integrate with existing agricultural IoT devices and environmental sensors, selling the prediction program as a comprehensive solution. Provide hardware and software as a unified offering.
📊 Data Licensing
License this technology's prediction algorithms and expertise to major agricultural machinery manufacturers and smart agriculture platform providers for monetization.
Adjacent Application Opportunities
🍎 食品ロス削減
Freshness Prediction in Retail and Distribution
Applying fruit temperature prediction, this technology could build a system to forecast the shelf life and optimal sales timing of fresh produce post-harvest. This has the potential to minimize waste in the distribution chain and deliver fresher food to consumers, contributing to a significant reduction in food loss.
🌿 医療用植物栽培
Maximizing Active Compounds in Medicinal Plants
In the cultivation of medicinal plants and herbs, this technology could predict the peak production of active compounds from biological data like fruit temperature. Identifying optimal harvest timing could maximize compound content, potentially increasing product quality and profitability by 15-20%.
🔬 研究開発
Streamlining Plant Physiology Research
Leverage this technology's prediction models and data analysis capabilities for new variety development and cultivation condition research. It could provide detailed insights into plant growth mechanisms, potentially shortening R&D cycles by 20-30% and supporting efficient data acquisition.
Integration Roadmap — Estimated 18-Month Deployment
Phase 1: Technology Validation and Requirements Definition
Duration: 3 months
Evaluate the compatibility of this technology's prediction algorithm with the licensee's existing environment (sensors, control systems). Define specific data integration methods and system requirements.
Phase 2: System Development and Pilot Testing
Duration: 6 months
Develop and customize the prediction program based on requirements. Conduct pilot tests in the licensee's experimental cultivation environment to verify prediction accuracy and environmental adjustment effects.
Phase 3: Full Operation and Optimization
Duration: 9 months
Optimize the system based on pilot results and commence full commercial operation. Continuously improve the prediction model's accuracy through ongoing data learning and refinement.
Technical Feasibility
This technology features 'acquisition or estimation' of fruit temperature and 'computer-executed processing' based on that data, enabling software-based implementation. It is designed to integrate temperature data from existing greenhouse sensors or agricultural IoT devices and run prediction programs in cloud or on-premise environments. This indicates high technical feasibility for relatively easy integration into existing cultivation management systems via software updates or API linkages, without requiring significant capital investment.
Success Scenario
Upon adopting this technology, harvest timing decisions, traditionally reliant on skilled farmer experience, could be made based on high-precision AI-driven data. This may improve harvest planning, enabling optimal timing for fruit and vegetable harvesting, which is expected to stabilize quality and boost profitability. Furthermore, automated environmental optimization could reduce labor burden and increase productivity by an estimated 1.2 times.
Patent Record
APPLICATION NO.
特願2022-168385
REGISTRATION NO.
7748102
FILING DATE
2022/10/20
GRANT DATE
2025/09/24
EXPIRATION DATE
2042/10/20
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
2025年04月08日
早期審査に関する事情説明書
2025年04月08日
出願審査請求書
2025年04月22日
早期審査に関する通知書
2025年06月03日
拒絶理由通知書
2025年07月18日
意見書
2025年07月18日
手続補正書（自発・内容）
2025年08月26日
特許査定