
Morocco
Agriculture
Implementing Organisation
Deep Leaf
Morocco, Casablanca-Settat, Sidi Bennour
Implementing Point of Contact
El Mahdi Aboulmanadel
Founder and Chief Executive Officer
Contributor of the Impact Story
Deep Leaf
Year of implementation
2023
Problem statement
Late and inaccurate detection of crop diseases, pests, and nutrient stress leads to $300B in annual global losses, with 40% crop losses in Africa and Asia. 80% of smallholders lack access to expert agronomic advice. Current solutions are fragmented and reactive: satellite companies show indices but stop there, IoT solutions cost $2,500+ per site and do not scale, and agronomists are expensive and not always available. Farmers spray entire fields uniformly, wasting chemicals with no guidance on treatment timing or residue management. DeepLeaf AI enables early, accurate, and scalable crop health diagnosis using AI-powered image analysis, satellite monitoring, and expert knowledge systems-providing full accompaniment from planning to certified, residue-free harvest.
Impact story details
DeepLeaf is an agritech company developing AI-driven solutions for early detection and management of crop health risks. Its software uses computer vision and machine learning models trained on large-scale plant image datasets to identify diseases, pests, and nutrient deficiencies across diverse crops. The platform provides end-to-end farm management from planning to certified harvest, using satellite imagery (Sentinel-1/2), weather data, and AI-powered diagnostics. DeepLeaf serves farmers of all scales-from smallholders to large commercial operations-through accessible channels including WhatsApp, USSD, mobile apps, and web dashboards. The company focuses on reducing chemical inputs by 60-80% through precision agriculture while ensuring zero residues at harvest through automated pre-harvest interval compliance.
AI Technology Used
Key Outcomes
Efficiency
Productivity, Economic Value Creation, Access
Reach, Accuracy
Quality Improvement, Resource Efficiency, Resilience
Risk Reduction
Impact Metrics
Accuracy in detecting crop diseases, pests, and nutrient deficiencies
Post-Implementation
0
Reduction in pesticide application through precision targeting
Post-Implementation
0
Improvement in crop yield through optimized treatment and early intervention
Post-Implementation
0
Implementation Context
Africa (Morocco, Kenya), Middle East (Qatar partnership), expanding to Turkey and Mexico
Smallholder farmers (1-5 ha), commercial farms (50-500 ha), and large-scale operations (10,000+ ha). Focus on underserved rural farming communities including women farmers and youth in agriculture across Africa and the Global South
Key Partnerships
Private Sector: Hassad Food (Qatar), MTN Chenosis, Les Domaines Agricoles. Programs: BRAIN by Open Startup, GIZ SAIS, TASMU Accelerator, QDB Talent Community Program, Katapult Africa Climate Program, AWS/Deloitte Social Entrepreneur Accelerator 2025
Replicability & Adaptation
Platform is designed for rapid context adaptation: supports 57 crops with localized crop databases; provides local language interfaces via WhatsApp, USSD, and SMS; maintains country-specific product authorization databases (500+ active ingredients, 1000+ products per country); satellite-first approach works anywhere on Earth with zero hardware requirements; scales from 1 hectare smallholdings to 100,000+ hectare operations; optional IoT sensors and drones can be added for higher precision when needed (greenhouses, high-value crops).
Supporting Materials
* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.