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Lustre AgriSense AI
Lustre AgriSense AI
AI-powered climate and agronomy decision support for African agriculture
AI-powered climate and agronomy decision support for African agriculture
Smallholder farmers and the organisations that support them are making increasingly complex decisions in an environment of changing weather, fragmented information, limited extension capacity and rapidly evolving agricultural risks.
Lustre AgriSense turns agricultural, climate and farmer-generated data into practical, localised decisions.
AgriSense is an AI-powered decision-support platform developed by Lustre Innovation Centre to help farmers, agricultural extension workers and other actors in the food system understand risks, identify appropriate actions and make better decisions.
From information to action
From information to action
Agricultural information is often available, but it is not always available in the right form, at the right time, or in a way that helps farmers make a decision.
AgriSense is designed around a simple principle:
Observe → Predict → Act.
The platform combines agricultural knowledge with climate, weather, location, farmer and other relevant data to generate practical and explainable recommendations.
Rather than simply telling farmers what agriculture is, AgriSense is designed to help answer:
What is happening?
What is likely to happen next?
What should I do now?
Three connected decision-support tools. Try AgriSense!
Three connected decision-support tools. Try AgriSense!
Extension Copilot
Extension Copilot
An AI agronomist for every extension worker.
An AI agronomist for every extension worker.
Agricultural extension workers serve as a critical bridge between technical knowledge and farmers, but often have to support large numbers of farmers with limited time and resources.
AgriSense Extension Copilot is designed to augment extension workers with AI-powered agricultural intelligence.
Extension workers can describe a farmer's situation, upload relevant observations or images, and receive contextualised guidance based on available agricultural and climate information.
The system can support questions relating to:
Crop production and management
Pest and disease risks
Weather and climate conditions
Soil and nutrient management
Planting and harvesting decisions
Climate adaptation
Livestock management
Farmer follow-up and prioritisation
The extension worker remains in control. AgriSense is designed to support professional judgement, not replace it.
Farm Decision Engine
Farm Decision Engine
What should I do on my farm this week.
What should I do on my farm this week.
Farmers do not necessarily need more information. They need information that is relevant to their farm, their crop, their location and their current conditions.
The Farm Decision Engine is designed to translate agricultural and climate intelligence into simple, actionable recommendations.
Depending on the available data, the system can consider:
Location and agroecological conditions
Crop and production stage
Weather forecasts
Historical climate patterns
Soil and water conditions
Farmer observations
Satellite and remote-sensing information
Agricultural calendars and technical recommendations
The objective is to provide farmers with timely, understandable and actionable guidance while remaining appropriate for low-connectivity and mobile-first environments.
Livestock Climate Advisor
Livestock Climate Advisor
Climate-smart decision support for livestock keepers.
Climate-smart decision support for livestock keepers.
Climate change is creating new challenges for livestock producers, including heat stress, changing forage availability, water scarcity and changing disease risks.
The Livestock Climate Advisor extends the AgriSense intelligence layer beyond crops to livestock systems.
It is designed to support livestock keepers and extension workers with climate-aware information relating to:
Heat and climate stress
Water availability
Feed and forage conditions
Livestock productivity
Climate-related risks
Early warning and preparedness
Basic management and escalation guidance
The system is designed as a decision-support tool and does not replace professional veterinary diagnosis or treatment.
Built for African agricultural realities
Built for African agricultural realities
AgriSense is being designed for the realities of smallholder and informal farming systems.
This includes:
Mobile-first access
Designed to work through channels familiar to farmers and extension workers, with the potential to support WhatsApp, voice, SMS and other low-connectivity channels.
Localised intelligence
Recommendations can be contextualised to geography, crop, farming system, climate and user needs.
Human-centred design
The technology is developed around real farmer and extension-worker workflows rather than technology for its own sake.
Explainable AI
Recommendations should be accompanied by understandable reasoning, relevant evidence and appropriate confidence levels.
Human-in-the-loop
Extension workers and agricultural experts remain an important part of the decision-making process.
Responsible data use
AgriSense is designed around principles of data privacy, farmer consent, responsible AI and appropriate data governance.
Designed to connect the agricultural ecosystem
Designed to connect the agricultural ecosystem
AgriSense is not intended to operate as a standalone farmer application.
We envision a platform that can connect:
Farmers → Extension workers → Cooperatives → Agribusinesses → Research institutions → Governments → Development programmes
This creates opportunities for AgriSense to strengthen existing agricultural systems rather than creating parallel ones.
The platform can potentially be integrated into existing extension programmes, farmer networks, agricultural value chains and national or sub-national agricultural information systems.
From prototype to scale
From prototype to scale
Lustre is developing AgriSense through an iterative innovation process.
We begin with a clearly defined agricultural decision problem, develop a focused prototype, test it with real users and use evidence to improve the product.
Our approach is:
Identify → Prototype → Test → Learn → Validate → Scale
The first deployments will focus on demonstrating whether AI-enabled decision support can improve the timeliness, relevance and adoption of agricultural recommendations.
We will measure not only how many farmers use the technology, but whether it changes decisions and contributes to better agricultural outcomes.
Our ambition
Our ambition
Lustre AgriSense aims to become a scalable African technology platform for climate-smart agricultural decision-making.
Our long-term ambition is to help put high-quality agricultural intelligence within reach of farmers and the organisations that support the, particularly those operating in contexts where agricultural extension capacity, climate information and access to technical expertise remain limited.
Better information. Better decisions. More resilient farms.
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