Market Context — Why This Technology, Why Now

The global agricultural sector is undergoing a profound digital transformation, driven by demands for sustainable practices, increased efficiency, and higher yields amidst climate change and resource scarcity. Regulatory pressures for reduced pesticide use and consumer demand for traceable, high-quality produce further accelerate the adoption of precision farming technologies. This patent offers a critical tool for agribusinesses to meet these challenges, gain a competitive edge, and secure future food supply chains through advanced data analytics.

Key Competitive Advantages
01

Generates orthomosaic images by segmenting and combining drone aerial photos based on crop lodging, enabling early detection of hidden pests, diseases, and growth irregularities beneath the canopy for precise intervention.

02

Dramatically reduces operational time compared to manual visual inspection of vast fields using drones and AI. This could reduce labor and fuel costs, leading to an estimated ~30% reduction in annual operating expenses.

03

Integrates and analyzes real-time growth data and pest damage estimates. This supports scientific decision-making over traditional experience, contributing to maximized yields and optimized use of pesticides and fertilizers.

Market Opportunity
Large-Scale Agricultural Corporations
$1.5B–$2.5B globally (AI est.)
For agricultural corporations managing vast fields, manual growth management is inefficient. There is a high demand for cost reduction and productivity improvement through drone and AI automation.
Global agribusinesses Large-scale farm operators Agricultural cooperatives
Agricultural Machinery and Material Manufacturers
$1.0B–$1.5B globally (AI est.)
Integrating this technology as an added value to smart agriculture solutions enables product differentiation and new market development. Upselling to existing customers is also expected.
Drone manufacturers for agriculture Smart farming equipment OEMs Agricultural input suppliers
Food Processing and Distribution Industry
$0.5B–$1.0B globally (AI est.)
This technology contributes to stable crop quality and improved traceability. Strengthening quality control at the production stage leads to enhanced brand value and improved yield rates.
Major food processors Fresh produce distributors Food quality assurance providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a broad scope across information processing apparatuses, methods, and programs, covering a unique algorithm for generating orthomosaic images by segmenting and combining drone images based on crop lodging, and subsequently estimating damage. This robust protection, validated through rigorous examination against prior art, minimizes invalidation risks and offers a stable foundation for licensees.

Competitive White Space

This patent focuses on image processing and analysis. White space exists in integrating this data with autonomous farming machinery for automated intervention, or combining it with advanced soil and weather data for more comprehensive predictive models.

Economic Impact
~$150K/year estimated cost savings and revenue increase per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

For large-scale agricultural corporations managing an average of 50 hectares, traditional manual crop and pest monitoring incurs annual labor costs of ~$50K (AI est.) (2 workers x ~$25K/year/worker) and improper pesticide application costs of ~$50K (AI est.). Implementing this technology could reduce monitoring labor costs by 80% (~$50K reduction, AI est.) and pesticide costs by 50% (~$50K reduction, AI est.) through precise damage estimation. Additionally, increased yields could generate an estimated ~$100K (AI est.) in annual revenue. The total expected economic impact is ~$150K/year (AI est.).

Speed to Market
5× faster than in-house development
This technology is a research outcome from a national research and development agency, and its core algorithms are presumed to be well-established. Drone-based image acquisition technology is widely adopted, and integration with existing general-purpose drones and camera systems is relatively straightforward. The fundamental image processing and AI-based damage estimation technologies have already been validated, making it highly probable that licensees can significantly shorten development periods and achieve early market entry compared to developing from scratch.
Competitive Positioning

X: Analysis Accuracy and Immediacy
Y: Cost-Effectiveness and Versatility

Business Models & Applications
💻 SaaS Service Provision
Offer a cloud-based crop growth and damage monitoring service utilizing this technology on a monthly subscription basis. Farmers can easily adopt it and receive real-time analysis reports.
🚜 Licensing to Agricultural Machinery
License this technology's algorithms to drone manufacturers and agricultural machinery makers. This enables enhanced functionality and higher value for smart farming equipment.
📊 Consulting & Data Sales
Provide agricultural management consulting based on field data analysis results. Analyze growth data and pest trends for specific regions and sell insights to agricultural material manufacturers.
Adjacent Application Opportunities
🌲 林業・森林管理
Forest Health Diagnosis and Early Pest Detection
Applying drone aerial imagery to forests for diagnosing tree health and detecting early signs of pests like pine weevils. This could significantly improve forest resource management efficiency across vast areas, potentially reducing timber loss by 15-20%.
🏗️ インフラ点検・災害監視
Wide-Area Infrastructure Anomaly Detection and Damage Assessment
Utilizing drone imagery to inspect extensive infrastructure such as power lines, pipelines, and slopes for deformation or collapse. In disaster scenarios, it could rapidly assess damage, speeding up recovery efforts by identifying affected areas and prioritizing repairs, potentially cutting assessment time by 50%.
🏙️ 都市緑化・公園管理
Monitoring Health of Urban Green Spaces and Street Trees
Regularly monitoring the health of urban parks and street trees with drone images. This enables early detection of diseases, poor growth, or risk of falling trees, facilitating efficient maintenance planning and contributing to improved urban environmental quality, potentially reducing maintenance costs by 10-15%.
Integration Roadmap — Estimated 12-Month Deployment
Proof of Concept & Requirements Definition
Duration: 3 months
Collaborate with the licensee's field data to conduct initial validation of this technology. Define specific analysis needs and integration requirements with existing systems, then formulate an implementation plan.
System Development & Feature Implementation
Duration: 6 months
Based on defined requirements, integrate the technology's algorithms into existing drone systems and information infrastructure, performing customized development. Conduct operational verification in a test environment.
Production Deployment & Operation Optimization
Duration: 3 months
Initiate production operation in the field, conducting performance evaluation and adjustments using real data. Continuously optimize the system based on feedback to maximize its effectiveness.
Technical Feasibility
This technology is primarily software-centric, processing image data acquired by general-purpose cameras mounted on unmanned aerial vehicles. The patent claims describe an image acquisition unit, a segmented image generation unit, an orthomosaic image generation unit, and a damage estimation unit, all of which can be implemented using existing image processing libraries and AI frameworks. It exhibits high compatibility for relatively easy integration through software updates or API linkages with existing drone systems and cloud platforms. No significant investment in new hardware is required, suggesting low barriers to adoption.
Success Scenario
Implementing this technology could dramatically transform a licensee's field management system. Early detection of crop growth conditions and pest damage across vast agricultural lands would be automated, reducing visual inspection times from several days to a few hours. This could optimize the timing of pesticide application and fertilization, potentially increasing annual yields by 10%. Furthermore, it is expected to reduce unnecessary pesticide use by 20%, achieving both environmental load reduction and cost savings.
Patent Record
APPLICATION NO.
特願2022-004505
REGISTRATION NO.
7370087
FILING DATE
2022/01/14
GRANT DATE
2023/10/19
EXPIRATION DATE
2042/01/14
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06月26日
出願審査請求書
2023年08月02日
早期審査に関する事情説明書
2023年08月15日
早期審査に関する通知書
2023年09月12日
特許査定