Market Context — Why This Technology, Why Now

The global agriculture sector faces immense pressure to increase yields sustainably while battling climate change and labor scarcity. Regulatory demands for reduced pesticide and fertilizer use, coupled with consumer preference for eco-friendly produce, are accelerating the adoption of precision farming. This technology provides a critical component for next-gen agricultural robotics and AI systems, enabling data-driven decisions that optimize resource allocation and enhance crop resilience in a competitive market.

Key Competitive Advantages
01

Achieves High-Precision Extraction Independent of Lighting Conditions

02

Drives Agricultural Digital Transformation, Boosts Operational Efficiency by 2x

03

Optimizes Resource Use, Reduces Costs by ~30%

Market Opportunity
Precision Agriculture Solutions
$40B globally (AI est.)
Demand for precise farm management using drones and AI is increasing, and this technology significantly enhances the accuracy of growth monitoring.
Agri-tech software developers Drone manufacturers for agriculture AI farm management platform providers
Agricultural Robotics and Automated Machinery
$20B globally (AI est.)
Integration into weeding robots, harvesting robots, and pesticide spraying drones could improve the accuracy and efficiency of autonomous operations.
Agricultural machinery OEMs Robotics developers for farming Autonomous vehicle manufacturers
Smart Greenhouses and Plant Factories
$10B globally (AI est.)
Combined with light environment control in enclosed settings, this technology could provide high-precision, real-time plant growth monitoring, contributing to cultivation optimization.
Controlled environment agriculture (CEA) system integrators Greenhouse technology providers Vertical farm operators
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an information processing apparatus and method for plant stock extraction, specifically leveraging Lab color space to ensure accuracy regardless of light conditions. The claims were refined through examiner feedback, resulting in a robust and stable patent with low invalidation risk, demonstrating technical superiority over five cited prior art documents.

Competitive White Space

This patent focuses on the image processing algorithm for plant stock extraction. White space exists in developing novel hardware for image acquisition (e.g., specialized drone platforms), integrating the extracted data into advanced predictive analytics models, or creating specific robotic actuation systems for targeted intervention based on the data.

Economic Impact
~$1M/year estimated agricultural cost reduction potential per large-scale farm (AI est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

For large-scale agricultural corporations (e.g., managing 500 hectares), assuming conventional broad-acre pesticide and fertilizer costs of ~$3.5M/year (AI est.). Localized application, based on precise plant stock extraction using this technology, could reduce material costs by 30%. This projects an annual savings of ~$1M ($3.5M × 30%) (AI est.). Additional benefits include reduced yield loss from early disease detection.

Speed to Market
4× faster than in-house development
This technology is a research outcome from a national R&D agency, with a well-established image processing algorithm utilizing Lab color space. This significantly shortens the R&D period compared to companies developing similar technology from scratch. With fundamental technical validation and algorithm optimization complete, early market entry is expected by focusing on implementation into existing systems and pilot operations.
Competitive Positioning

X: Operational Cost Efficiency
Y: Adaptability to Environmental Changes

Business Models & Applications
🚜 Licensing to Agricultural Machinery Manufacturers
A model to generate revenue by licensing this technology to agricultural machinery manufacturers for developing next-generation automated farm equipment and drones.
☁️ Precision Agriculture SaaS Platform
Analyze plant stock data acquired by this technology on a cloud platform, offering growth diagnostics, disease prediction, and optimal resource application plans as a SaaS to farmers.
🏭 Control Systems for Plant Factories
Integrate this technology into environmental control systems for plant factories and smart greenhouses, providing high-precision, light-agnostic growth monitoring and optimization.
Adjacent Application Opportunities
🌳 Forestry and Forest Management
Tree Health Monitoring System
Automatically detects tree health and disease from drone imagery in forests. This enables stable data acquisition even in highly variable light environments, potentially contributing to efficient forest management and early intervention across vast areas, reducing manual inspection time by up to 40%.
🌊 Environmental Monitoring
Aquatic Plant and Algae Monitoring
Automatically detects abnormal occurrences of aquatic plants and harmful algae in lakes and rivers from images. It could provide stable monitoring accuracy even with surface reflections and varying light penetration, aiding water quality management and ecosystem protection with up to 90% detection accuracy.
🌿 Urban Greening and Landscape Management
Park and Street Tree Health Check
Automatically monitors the health of park and street trees in urban areas. This could enable early detection of individual plant abnormalities in complex lighting conditions (shade, artificial light, reflections), contributing to efficient maintenance and landscape preservation, potentially reducing inspection costs by 25%.
Integration Roadmap — Estimated 16-Month Deployment
Phase 1: Technical Validation and Requirements Definition
Duration: 4 months
Define specific challenges and integration requirements with the licensee's existing systems. Validate the technology's applicability and initial performance through a Proof of Concept (PoC) based on provided technical documentation.
Phase 2: System Development and Prototype Deployment
Duration: 7 months
Based on defined requirements, develop and integrate the technology's algorithm into the licensee's systems. Deploy a prototype in a small-scale environment to evaluate and adjust functionality, stability, and accuracy under actual operating conditions.
Phase 3: Pilot Operation and Full-Scale Rollout
Duration: 5 months
Incorporate feedback from prototype validation to optimize the overall system. Subsequently, initiate full-scale pilot operations, iteratively measuring effects and making improvements, enabling gradual company-wide deployment.
Technical Feasibility
This technology is a software-based solution centered on Lab color space image processing logic, making it easily integrable with existing camera systems and image input devices. The patent claims clearly define the information processing apparatus configuration, allowing for relatively straightforward integration into existing agricultural machinery or monitoring systems using general-purpose computing resources and image processing libraries. Large-scale hardware modifications are not required, and deployment via software updates is anticipated.
Success Scenario
Upon adoption, this technology could enable automatic, high-precision monitoring of plant stock growth and disease indicators in fields or greenhouses, regardless of day, night, or weather. This is estimated to reduce labor costs for traditional manual inspections by up to 50% annually and cut material costs by over 30% through optimized pesticide and fertilizer use. Ultimately, this could lead to stabilized yields, improved quality, and competitive, sustainable agricultural operations.
Patent Record
APPLICATION NO.
特願2021-039658
REGISTRATION NO.
7627937
FILING DATE
2021/03/11
GRANT DATE
2025/01/30
EXPIRATION DATE
2041/03/11
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10月23日
出願審査請求書
2024年08月06日
拒絶理由通知書
2024年09月13日
意見書
2024年09月13日
手続補正書（自発・内容）
2025年01月07日
特許査定