Mission
Making agriculture more predictable, productive and sustainable through autonomous monitoring and AI, without adding operational complexity for farmers.
Autonomous crop monitoring from flight to decision.
Shinsei Technology combines autonomous drones, intelligent docking stations, multispectral imaging and AI to detect crop problems early and turn field data into actionable recommendations.
- Autonomous monitoring without adding operational complexity for farmers
- Near-real-time crop intelligence from recurring multispectral flights
- Decision support for targeted scouting and input application
Fully managed service
Japan + Brazil research roots
Pilot discussions open
What drives Shinsei
Built to make agriculture more predictable, productive and sustainable.
Shinsei is building SHUNRAI as a farmer-first autonomous intelligence service, designed for measurable field outcomes and seamless operational adoption.
Vision
To become a global reference in autonomous agricultural intelligence, helping farmers reduce losses, improve productivity and build a more sustainable, climate-resilient food system.
Farmer-first innovation
We build technology that solves real problems for farmers, improves decision-making, and creates measurable value in the field.
Seamless adoption
We deliver agricultural intelligence as a fully managed service, designed to integrate into existing farming operations without adding unnecessary complexity, workload or technical burden for producers.
Sustainability with productivity
We believe environmental responsibility and agricultural productivity must advance together.
Climate and environmental responsibility
We support more efficient use of land, water, fertilisers and chemicals, contributing to lower emissions and healthier ecosystems.
Precision over excess
We promote targeted, data-driven application of agricultural inputs, helping reduce waste and unnecessary chemical use.
Scientific and operational excellence
We combine scientific rigour, advanced AI, remote sensing and practical field knowledge to deliver reliable insights that farmers can trust.
Our solution
SHUNRAI is a fully managed autonomous crop-monitoring service.
SHUNRAI combines drones, multispectral imaging and AI to detect field problems early and transform them into actionable recommendations. The system is designed to deliver recurring monitoring, field diagnostics, prescription maps and coordinates while reducing the operational burden on producers.
Drone + Dock + AI
An integrated technology stack connecting autonomous flight, field deployment, onboard processing and cloud intelligence.
Autonomous monitoring
Recurring missions are planned, executed and reset with minimal on-site operational burden.
Near-real-time insights
Processed results and alerts become available through the SHUNRAI platform after each mission.
Actionable deliverables
Field diagnostics, prescription polygons, coordinate files and decision-support reports help guide targeted interventions.
Measurable outcomes
Designed for measurable farmer outcomes
SHUNRAI is built to move beyond monitoring and support operational decisions in the field.
- Targeted input application
- Faster detection of pests, diseases, weeds, nutritional stress and water stress
- Improved field scouting and intervention planning
- Higher productivity and fewer losses
Operational intelligence built for recurring field decisions
SHUNRAI is designed to help farmers move from raw monitoring outputs to targeted action with less delay and less operational burden.
- Customised reports
- Decision-support recommendations
- Coordinate files and waypoints
Crop-Specific AI Modules
- Weed identification
- Early disease detection
- Pest monitoring
- Nutritional deficiency diagnosis
- Soil Regeneration Intelligence
How it works
From monitoring to actionable intelligence.
SHUNRAI connects recurring field capture, structured analysis and decision-support outputs in one continuous intelligence loop.
Capture
Recurring multispectral monitoring of crop areas.
Map & Measure
Orthomosaics, field maps and vegetation indices.
Diagnose
Detection support for weeds, pests, diseases, nutritional problems, water stress and uneven growth.
Predict
Yield projection, risk forecasting, problem spread estimation, intervention timing, crop vigour trends and canopy density analysis.
Prescribe
Waypoints, coordinates, prescription polygons, decision-support reports and targeted input application plans.
Resources
Evidence, execution base and applied research.
Shinsei is building SHUNRAI side-by-side with agricultural partners, connecting technical development with real field conditions.
Born from applied research in Japan and Brazil
Shinsei Technology was born from a technical and research foundation linked to Soka University and the University of São Paulo, with support from specialists in AI, remote sensing, robotics, aviation and autonomous systems. The company translates advanced research into field-ready agricultural intelligence systems.
Evidence and execution base
Shinsei is building SHUNRAI side-by-side with agricultural partners, connecting technical development with real field conditions.
White paper
Multispectral Imaging for Soy Agriculture
Applied research material focused on crop monitoring, signal detection and field decision support in soy operations.
Download PDFWhite paper
Multispectral Imaging for Coffee Agriculture
Research-oriented material covering practical applications of multispectral analysis in coffee monitoring and agricultural intelligence workflows.
Download PDFCore team
A multidisciplinary founding team combining AI, embedded systems, remote sensing, drone autonomy, crop research and field operations.
Shinsei combines practical field knowledge, scientific modelling, autonomous systems and product architecture across Brazil and Japan.
Co-founder
Dr. Marcelo Ono
Field Operations and Agronomic Validation
Engineer, PhD, specialist in agricultural operations and crop research. Fifteenth generation rice farmer in Japan.
Leads and directly participates in field operations, agronomic validation of models and the relationship with producers.
Co-founder
Dr. Luiz H. Mormille
Technology, AI and Product Architecture
PhD in Artificial Intelligence, specialising in computer vision, deep learning, embedded systems and AI applications for agriculture.
Oversees the end-to-end technological architecture, embedded software, data pipelines, AI models and the digital platform.
Founding Partner
Prof. Renato Vicente
Scientific Strategy and Modelling
Professor at IME-USP and director of the TELUS Digital Research Hub. Co-founder of Experian DataLabs and Serasa Agro.
Responsible for scientific strategy, methodological design, validation of results and connections to academia, industry and the innovation ecosystem.
Founding Partner
Prof. Bismark Asante
Remote Sensing and Autonomous Systems
Engineer, PhD and Professor at Soka University, specialising in drones and navigation and control systems.
Leads the adaptation of commercial drones, integration between hardware and embedded software, and development of the autonomous operating base.
Partner
Daisuke Yano
Hardware and Systems Integration
PhD candidate in Information Systems Engineering at Soka University.
Works with component integration, digital signal processing and implementation of physical systems for the drone and docking station.
Support team and mentors
Legal, governance, regulation and strategic support
Strategic Support
Luciano Cordeiro
Legal Strategy and Governance
Supports Shinsei with legal strategy, governance and corporate structuring in Brazil.
Strategic Support
André Hiratsuka
Legal Strategy and Corporate Structuring
Supports Shinsei with legal and corporate structuring in Japan.
Strategic Partner: Euphrates
Software and data engineering support for scalable infrastructure.
Contact
Open to pilot discussions, strategic partnerships and technical conversations.
If you want to discuss SHUNRAI, explore a pilot or learn more about the autonomous crop monitoring model, send a message and the team will get back to you.
- Autonomous crop monitoring pilots
- Strategic agricultural partnerships
- Technology and research collaboration