Market Context — Why This Technology, Why Now

Global agriculture is undergoing a rapid transformation driven by the need for increased food security, reduced environmental impact, and improved operational efficiency. Regulatory pressures for sustainable farming practices and consumer demand for high-quality, sustainably produced food are accelerating the adoption of precision agriculture technologies. This patent offers a critical tool for agribusinesses to navigate these trends, enabling data-driven decisions that cut costs, optimize resource use, and ensure resilient crop production in the face of climate volatility and labor scarcity.

Key Competitive Advantages
01

Automatically generates optimal pest control models for each producer using AI, eliminating reliance on experience.

02

Reduces costs by ~30% by minimizing excessive pesticide and fertilizer use and cutting labor.

03

Minimizes yield loss from pests and diseases through high-precision prediction, ensuring stable, high-quality agricultural production.

Market Opportunity
Large-Scale Agricultural Corporations
$3B–$4B globally (AI est.)
These entities prioritize efficiency and cost reduction to achieve economies of scale, showing high willingness to invest in data-driven precision pest control.
Global agribusiness conglomerates Large-scale commercial farm operators Vertical farming enterprises
Pesticide and Fertilizer Manufacturers
$1.5B–$2.5B globally (AI est.)
Seeking solutions to enhance product value and comply with environmental regulations by offering optimized application methods.
Agrochemical product developers Specialty fertilizer producers Bio-pesticide innovators
Agricultural IT Platform Providers
$1B–$2B globally (AI est.)
Aim to expand user services and differentiate their offerings by integrating new functionalities into existing platforms.
Smart farming software developers Agricultural IoT solution providers Farm management system vendors
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects a model generation device for evaluating pest and disease control effectiveness in agriculture, demonstrating clear inventiveness over six prior art documents. Its 9 claims were rigorously examined and upheld, indicating a robust and stable right with low invalidation risk.

Competitive White Space

This patent primarily covers the model generation and evaluation methodology. White space exists in developing novel sensor hardware for data collection, integrating specific robotic or drone-based application systems, or expanding into predictive models for other agricultural challenges like nutrient deficiencies or irrigation optimization.

Economic Impact
~$1M/year estimated cost reduction and revenue increase per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

Assuming an adopting enterprise operates a large-scale farm with ~$33.5M (AI est.) in annual sales and a 20% material cost ratio. A 30% reduction in material costs via this technology could save ~$2M (AI est.) annually. Additionally, a 5% improvement in yield loss could increase revenue by ~$1.5M (AI est.). The total potential economic impact is ~$3.5M (AI est.) annually. Conservatively, after accounting for initial implementation and operational costs, a net profit increase of ~$1M (AI est.) per year is projected.

Speed to Market
6× faster than in-house development
This technology's pest control evaluation model generation algorithm is already established and theoretically validated. Its regression analysis-based model generation also facilitates easy integration with general-purpose data analysis platforms. This could significantly shorten development time and accelerate market entry by approximately 2.5 years compared to developing a similar system from scratch. Focusing on developing integration modules for existing agricultural data collection systems and sensor data is expected to enable rapid commercialization.
Competitive Positioning

X: Ease of Implementation & Scalability
Y: Optimization of Pest Control Efficacy

Business Models & Applications
☁️ SaaS Model Provision
Offer the optimized pest control models generated by this technology as a cloud service. A monthly subscription fee, scaled by farm size or features, could ensure recurring revenue.
🤝 Consulting & Licensing
Provide support for implementing pest control models and offer cultivation improvement consulting based on data analysis. Combining this with technology licensing allows for high-value service offerings.
🔗 Agri-tech Integration
Provide this technology's API to existing smart agriculture equipment manufacturers and data platform providers. This integration aims to reach a broader customer base and build an ecosystem.
Adjacent Application Opportunities
🌳 Forestry & Forest Management
Forest Pest & Disease Prediction System
This technology could predict pest and disease outbreak risks in specific forest areas by performing regression analysis on tree species, climate, soil data, and historical outbreak information. It has the potential to optimize preventative felling plans and eradication measures, contributing to forest resource conservation and sustainable forestry management.
💊 Pharmaceutical Development
Drug Target Evaluation Model
By using chemical structure and biological response data of drug candidates as 'work information' and treatment efficacy against diseases as 'evaluation values' for regression analysis, this technology could identify more promising drug candidates in the early stages of the drug discovery process. This is expected to significantly reduce development time and costs.
🏭 Manufacturing & Quality Control
Production Line Anomaly Detection Model
This technology could predict anomaly occurrences by performing regression analysis on manufacturing process parameters (temperature, pressure, time, etc.) as 'work information' and product defect rates as 'evaluation values'. It has the potential to reduce quality defects and improve production efficiency.
Integration Roadmap — Estimated 18-Month Deployment
Data Collection & Infrastructure Setup
Duration: 3 months
Collect and organize existing cultivation and pest control data (work records, pest occurrence) from the licensee to build the necessary data infrastructure for the regression model. Sensor integration can also be explored.
Model Generation & Validation
Duration: 6 months
Generate regression models using collected data and conduct field trials at the licensee's farms. Repeatedly evaluate and refine model accuracy to improve the precision of proposed pest control plans.
Full-Scale Operation & Impact Maximization
Duration: 9 months
Begin full-scale implementation of pest control plans based on the generated models. Aim to maximize control effectiveness and economic benefits through continuous data feedback and model retraining.
Technical Feasibility
This technology utilizes cultivation and pest control data, along with evaluation values of control effectiveness, obtained from existing agricultural data collection systems and IoT sensors, to generate regression models via software. The 'model generation unit that performs regression analysis on a dataset to generate a regression model' as described in the claims can be implemented within a general-purpose data analysis environment, requiring no significant new capital investment. Rapid and smooth deployment is expected through API integration with existing smart agriculture platforms and development of data linkage modules.
Success Scenario
Implementing this technology could enable proactive prediction of crop pest and disease risks, allowing for precise control measures at optimal times and quantities. This is expected to reduce pesticide application frequency by an average of 20% while cutting yield losses from pests and diseases by more than half compared to conventional methods. Consequently, it is estimated to significantly reduce annual material costs and establish a stable supply system for high-quality agricultural products.
Patent Record
APPLICATION NO.
特願2021-131960
REGISTRATION NO.
7685754
FILING DATE
2021/08/13
GRANT DATE
2025/05/22
EXPIRATION DATE
2041/08/13
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04月02日
出願審査請求書
2025年02月04日
拒絶理由通知書
2025年04月03日
手続補正書（自発・内容）
2025年04月03日
意見書
2025年04月22日
特許査定