AI Computer Vision for Agriculture
CropSentry uses AI to detect crop diseases and pests from a single leaf photo — helping African farmers prevent yield loss, reduce input waste, and act early.
The problem
Smallholder farmers across Sub-Saharan Africa produce most of the continent's food, yet diseases like Cassava Mosaic Virus, Fall Armyworm, Maize Lethal Necrosis, and Tomato Blight wipe out 20–40% of yields every season.
By the time symptoms become obvious, the disease has already spread. Agricultural extension officers are stretched thin — one officer often covers thousands of farmers. Farmers either treat the wrong problem, over-spray chemicals, or lose the crop entirely.
The cost: billions of dollars in lost food, lost income, and food insecurity.
The solution
Snap a photo of a leaf. In under 3 seconds, CropSentry's AI identifies the disease, scores severity, and gives the farmer a clear, low-cost action plan in their language.
Early detection
Spot disease 7–14 days before visible symptoms.
Localized advice
Recommendations adapted to crop, region, and season.
Outbreak alerts
Region-wide alerts when disease is spreading nearby.
Works offline
Designed for low-bandwidth rural environments.
AI Technology
Most agriculture AI is trained on Western datasets. Our models are purpose-built for the crops, climates, and disease patterns of Sub-Saharan Africa.
Leaf images captured by smartphone cameras — even low-light, blurry, or partial frames.
Disease class, severity score (0–100), confidence, and risk of spread to neighboring plants.
Fine-tuned EfficientNet-B4 + Vision Transformer ensemble, distilled to a 12MB on-device model.
Training on millions of images and continual learning from field uploads requires GPU acceleration.
What you get
Point, shoot, diagnose. Results in under 3 seconds, even offline.
Push notifications when disease is detected on nearby farms.
Step-by-step, locally-sourced treatment options ranked by cost.
Track scan history, disease trends, and field health over time.
English, Yoruba, Hausa, Igbo, and Swahili at launch.
Tools for agronomists to monitor dozens of farms at once.
How it works
Tap 'Scan'. The camera opens with a leaf guide overlay.
Our AI handles the rest — no expertise needed.
Disease name, severity, confidence score, and treatment plan.
Follow the plan or share it with your local extension officer.
Technology & Infrastructure
CropSentry is designed to scale from thousands to millions of scans per day — using best-in-class cloud and accelerated computing.
Market opportunity
Africa has over 33 million smallholder farms. Mobile penetration is past 80% in our launch markets, and government extension services cannot scale fast enough. AI on a $80 smartphone closes the gap.
Target users
Smallholder farmers, agronomists, cooperatives, agribusinesses, agri-NGOs
Launch markets
Nigeria, Ghana, Kenya, Rwanda, Côte d'Ivoire
Revenue model
Freemium app · Cooperative subscriptions · Enterprise API for agribusinesses · NGO/government licensing
Growth plan
Pilot in Lagos & Oyo State (2026), expand to West Africa (2027), Pan-African (2028)
Where we are today
12,000+
leaf images labeled in our training dataset
3
pilot cooperatives signed letters of interest
40+
diseases targeted across maize, cassava, tomato, cocoa
Beta Q1
2026 — limited rollout in Enugu & Anambra State
Roadmap
Build core scanning app, train on 50k+ images, recruit pilot farms.
Deploy with 3 cooperatives in Enugu & Anambra State. Iterate weekly.
Open app to all of Nigeria. Add Yoruba, Hausa, Igbo languages.
AWS production deployment, GPU inference, extension officer dashboard.
Ghana, Kenya, Rwanda, Côte d'Ivoire. New crops: rice, cocoa, plantain.
Continent-wide outbreak intelligence and enterprise API.
Why now
Mobile reach. Smartphone penetration in our launch markets has crossed 80%, putting AI in every farmer's hand.
Climate stress. Shifting rainfall is changing disease patterns faster than extension services can keep up.
AI is ready. Computer vision is now accurate, small, and cheap enough to run offline on a $80 phone.
Leadership
Co-founder & CTO
ML engineer with 6 years at fintech and computer-vision startups. MSc Computer Science. Leads model development and infrastructure.
Whether you're a farmer, an agronomist, a cooperative, or an investor — we'd love to talk.