AI-powered plant intelligence for climate-resilient agriculture.
DeepPhenoTech combines plant science, AI, computer vision, robotics, and advanced sensing to transform crop data into actionable intelligence for breeding, crop monitoring, and precision crop protection.
By 2050, the world will need to grow 60% more food to feed a growing population.
Climate change has already cut global agricultural productivity by 21% since 1961 — across all crops, not just cereals.
Most of what happens inside a field goes unmeasured — until it's too late to act.
DeepPhenoTech turns that gap into data — and data into decisions.
Sources: UN FAO (food production needs, 2050); Ortiz-Bobea et al., Nature Climate Change, 2021 (agricultural productivity impact, all crops)
DeepPhenoTech is a deep-tech company developing AI and robotic tools for plant breeding, precision farming, and cloud-based crop intelligence — bridging the gap between biological complexity and actionable agricultural decisions.
A future where every crop can be measured, understood, and managed with precision.
To advance climate-resilient agriculture through intelligent, precise, and scalable crop technologies.
Modern agriculture generates enormous biological and environmental variation, but much of it remains difficult to measure at scale.
Researchers and farmers often lack precise, timely information about crop health, growth, architecture, and yield potential.
Traditional crop monitoring and phenotyping can be slow, subjective, labour-intensive, and difficult to scale.
Disease is often identified after visible symptoms spread, leading to unnecessary or blanket crop protection treatments.
Breeders and seed companies need high-quality, high-throughput plant data to identify traits and accelerate crop improvement.
Autonomous, AI-powered systems capture high-resolution crop information, analyse biological traits, and support more precise agricultural decisions.
Multisensor systems capture information from plants, soil, and the crop environment.
Robotics and computer vision collect high-resolution data at field, plot, canopy, or plant scale.
AI and machine-learning models extract meaningful biological traits and detect patterns.
Growth dynamics over time, in relation to environment, become crop intelligence.
Supports breeding, genomic prediction, crop modeling, monitoring, and precision agriculture.
DeepPhenoTech brings together biological science and advanced engineering to build intelligent systems for agriculture.
Plant physiology, crop breeding, plant architecture, crop health, agronomic and soil traits, and climate resilience.
High-resolution imaging for plant detection, segmentation, trait extraction, and disease identification.
Machine learning and deep learning transform raw crop images and sensor data into biological information.
Autonomous mobile platforms enable repeatable, scalable data collection in field or greenhouse.
RGB and multispectral cameras, LiDAR, RTK-GNSS, soil sensing, lodging-strength quantification, and light measurement.
Trait extraction, visualisation, stability analysis, selection support, and decision-support tools.
Conventional imaging focuses on the top of the canopy — and only what's above the soil. DeepPhenoTech's approach is designed to collect information from within the canopy itself, capturing above-ground and below-ground data at the same time. CropScanalyzer can operate inside narrow crop rows, gathering in-canopy plant data alongside root and soil-level data that conventional imaging never reaches at all.
Field → Sensors → Images → AI → Traits → Insights → Decisions. Our pipeline draws on computer vision, deep learning, image segmentation, object detection, and disease classification — including YOLO-based computer vision models and SAM-based segmentation workflows.
Sense → Analyse → Predict → Act. CropScanalyzer measures the crop, AIRadiBot protects it, and DeepAgCloud turns both data streams into decisions.
From a single plant to a full trial plot.
Built to work at the scale breeding programs actually need — row by row, trial by trial.
Target applications include the following — many are part of ongoing field validation, not yet deployed across every crop.
Accelerate crop improvement with high-throughput phenotyping and quantitative plant data.
Generate objective crop data to support breeding, product development, and field evaluation.
Enable researchers to collect richer, more reproducible crop data at field scale.
Use crop intelligence to support more targeted crop management.
Detect disease and crop stress earlier and support precision crop protection.
Target applications include grapes, tomatoes, strawberries, and vegetables.
Founder & Director
Plant Scientist & Agricultural Technology Innovator
10+ years of international research experience in plant science, crop physiology, plant breeding, and phenotyping. Ph.D. in Crop Physiology, University of Nottingham, UK, with research experience across INRAE (France), CIMMYT (Turkey), and SLU (Sweden).
AI Expert
Machine Vision & AI/ML
Engineering in machine vision, and an expert in AI/ML model development for agriculture applications.
Robotics / Autonomous Systems
Robotic Engineer
Robotic engineer and sensor integration lead for autonomous navigation.
Advisor
Mechatronic Engineer
CEO of EKOBOT, Sweden, with experience across the EU market.
Pune Agri Innovation Hackathon, 2026
Sten K Johnson Foundation, Lund, Sweden, 2024
Einar and Inga Nilsson's Foundation, Sweden, 2024 — for imaging system development and testing
Technology development and research support has also come through interactions with Sony R&D (Lund, Sweden), Wageningen University & Research, the University of Angers, the Swedish University of Agricultural Sciences, DCM Shriram Seeds, and Sahyadri Frame, Nashik. These reflect research support and technical interaction, not formal strategic partnerships unless stated.
We are interested in working with research institutions, universities, seed companies, agricultural organizations, technology companies, and partners developing the next generation of sustainable agriculture.
