Reversing the Innovation Flow: Agricultural AI’s New Pioneers in Developing Countries

In this blog post, cross-published with the Montreal International Centre of Expertise in Artificial Intelligence (CEIMIA), Stéphanie Camaréna, Founder and CEO of, Source Transitions and co-chair of the IEEE Planet Positive 2030 committee on farmlands and grasslands, explores how AI is reshaping global agrifood systems. She examines the challenges and opportunities of AI-driven innovation, showing how developing nations are pioneering new, sustainable models for the future of food.

AI is enabling developing countries in developing countries to skip decades of conventional agricultural development, leapfrogging directly to sustainable food systems. More than 15,000 farmers across Kenya, India, Ethiopia and Nigeria are pioneering AI-powered farming advisory support through platforms that cost $3.50 per farmer adoption compared to US$35 for traditional advisory services. In India, the AI-powered Saagu Baagu Project in Telangana State, involved 7000 farmers who achieved 9% reduction in pesticide use, 5% decrease in fertiliser usage and 21% increase in yields per acre. Examples like this one demonstrate how the Global South is using AI to revolutionise their food systems; showing the world how to reach millions of previously isolated farmers and to help them quickly adopt context-sensitive advice for sustainable practices.

Making sure the global population has enough to eat remains a challenge. Global food insecurity has doubled, affecting now 300 million people compared to 150 million in 2020. This decline has been further exacerbated with disruptions caused by  climate change and changing weather patterns,  disproportionately affecting the smallholder farmers who produce one-third of the world’s food. 

Conversely, 30% of human-induced greenhouse gas emissions are a consequence of our global agrifood systems. Any attempt at reducing the negative impacts of farming systems like pollution, water usage, food losses, will directly benefit the countries most at risk of the impacts of climate change. 

Africa faces the highest food insecurity globally, with 85% of climate-threatened countries being African. In Asia, meanwhile, the groundwater crisis in India and Pakistan threatens 1.8 billion people. And while Latin America’s agriculture exports are booming, food insecurity is higher than a decade ago.

The brutal reality is that, to prevent increased environmental challenges causing food crises demands immediate action, but countries that most need these solutions often can’t afford the infrastructure investments to improve their situations. The traditional approaches to improving physical infrastructure (such as roads, cold storage, and banks) that would be required to mitigate against volatile weather patterns and adapt food systems, may be out of reach financially for the responsible governments.

The type of transitions required for food systems to support a growing population have to be scaled exponentially. Traditional systems, meanwhile, scale incrementally, never catching up to what the population needs. Opportunities therefore lie in bypassing physical barriers and middlemen, connecting farmers directly to their buyers; to the contextual advice that they need to manage their crops; and the financial services that will allow them to do so without the need for physical banking. What we’re witnessing is innovation born from necessity – solutions that could only emerge in contexts without legacy constraints.

Physical farming advisory services

Agricultural advisory services traditionally rely on agents and agent networks, mostly in-person services with a limited reach due to an insufficient number of agents per farmer. In Kenya, the agent-to-farmer ratio is estimated at 1:1000 according to government reports, but it can be as low as 1:4000, far below the FAO’s recommended ratio of 1:400. The global development organisation Digital Green’s platform – called FarmerChat – delivers “free, real-time, climate-smart and locally relevant advice in farmers’ own languages through text, video, and voice”. The not-for-profit reports increase farmer incomes by up to 24%, reaching more than 8 million farmers, 43% of whom are women. It sees more than 80% of their users adopting some or all of the sustainable agricultural practices. But whose knowledge shapes this advice? The question matters – local wisdom must drive these algorithms, not imported assumptions.

Traditional banking and credit systems

Only one third of the USD$238 billion annual credit demand from smallholder farmers is met globally –  leaving a staggering gap that manifests in agricultural underinvestment and scant reward for farmers. Credit risk scoring based on crop yields, market sales and payment behaviour actually reflect a farmer’s potential. An alternative or potentially fairer means of assessing risk is to use a tool such as Apollo Agriculture’s AI tool, which uses additional data sources such as satellite imagery and agronomic data. With more data sources, machine learning algorithms are better at predicting yields,  modeling cashflows, and establishing more accurate risk profiles. Having access to such tools and information may help more forward thinking lenders make astute investment choices that are also fairer for farmers.

Intermediate mechanisation and infrastructure development

While industrialized countries focus on “high-tech automation, robotics and IoT systems that rely on strong connectivity and significant capital investment, developing countries are implementing simpler but effective solutions. In India, an AI-driven sowing application resulted in a 30% increase in productivity per hectare through a simple SMS-based advisory system. In Uganda, an AI-powered smartphone app diagnoses plant diseases, significantly reducing yield losses by enabling farmers to take corrective action before outbreaks spread.

Traditional middlemen-dependent supply chains

Direct farmer-to-buyer connections dramatically reduce post-harvest losses (which can reach 40% in some African countries) by shortening the time between harvest and sale. Platforms like Twiga Foods (Kenya) have reduced post-harvest losses to 4% through features such as  AI-powered logistics, dynamic pricing, and demand forecasting. Frubana (Colombia / Brazil / Mexico) has brought waste levels close to 1–2% of tonnage by using predictive analytics to forecast demand, aligning production and harvesting with market needs.

Extensive laboratory and research infrastructure

While traditional agricultural research facilities require millions in laboratory infrastructure and years of field trials, farmers in the Global South are now accessing the same diagnostic capabilities through AI-powered smartphone apps that cost virtually nothing to deploy. The startup Traive in Brazil collects massive amounts of agriculture-related data and analyses it with AI to define risks for lenders and provide greater access to credit for farmers. “Lenders were taking three months to do something that we can do in five minutes with way better accuracy”.

Each of these solutions tells a similar story: what was considered essential infrastructure yesterday may be redundant today. The question for developed countries isn’t whether to adopt these innovations, but whether they can abandon their sunk costs quickly enough to keep pace. What is striking is the elegance of the alternatives. Where traditional development builds institutions, new AI-driven solutions build networks. Where traditional mitigation of problems adds layers of intermediation, the new solutions create direct connections. This is a fundamental reimagining of how agricultural systems can work.

These aren’t just technical victories. Each represents a fundamentally different approach to development – one that emerges in response to local constraints, rather than being universal or imported solutions.

Kilimo (Argentina) – Water conservation market pioneer

Kilimo uses big data and artificial intelligence to model field-by-field evapotranspiration and to provide site-specific irrigation recommendations. The AI also combines meteorological and satellite data to make irrigation more effective. But the key innovation resides in Kilimo’s self-sustaining economic model where conservation pays. Farmers save money on energy costs while earning from water credits making sustainable practices profitable rather than costly. The programme saved over 72 billion litres of water across seven Latin American countries working with over 2000 farmers. Kilimo leapfrogged the physical water infrastructure (traditional water meters, complex irrigation monitoring systems, government managed water allocation bureaucracies and physical rights trading markets) and traditional conservation paths (decades of building water user associations, regulatory compliance monitoring systems, manual field inspections for water usage and paper-based water rights documentation).

Hello Tractor (Nigeria/Kenya) – Mechanisation revolution

The core application uses machine learning to monitor tractor usage, forecast weather patterns and enable communication via text messages. Learning underlying drivers of demand for tractor services and predicting demand while advising on route optimisation and usage patterns has allowed Hello Tractor to transform the lives of over 2 million smallholder farmers who share the equipment of over 5,000 farm equipment owners. The impact is significant for farmers who plant up to 40 times faster through shared mechanisation. The initiative skipped through traditional models of mechanisation and ownership, and conventional credit systems to jump directly to PAYG financing models. The irony is that solutions designed for resource-scarce environments are creating more efficient systems than their resource-rich counterparts.

AI-powered leapfrogging represents a paradigm shift in food systems transformation, presenting unprecedented opportunities for innovation unconstrained by legacy systems. While challenges remain: connectivity gaps, digital literacy and financing, the potential for transformation is real. This is not just a developing countries´ phenomenon. In fact, the innovation flow is reversing. Industrialized countries trapped in inefficient agricultural infrastructures, regulatory and business constraints could benefit from these agile approaches by letting courageous and imaginative solutions emerge.

European farms struggling with subsidy dependencies could learn from Kilimo’s market-based conservation model, while American agriculture’s data monopolies could be disrupted by Hello Tractor’s sharing economy approach. The ethical dimensions persist: while localised AI offers culturally relevant solutions, we must guard against perpetuating digital colonialism through imported algorithms trained on industrial farming data. If leap-frogging will help reshape how we produce and distribute food globally, we still need to question how equitably it will distribute its benefits. Those willing to challenge the status quo will define the next era of food security and agricultural sustainability.

Our webinar “AI for Sustainable Food and Agriculture” explored how the food and agriculture sectors are transitioning towards more sustainable models in response to climate change. Part of IEEE webinar series on AI and Sustainability, the event is available to watch on demand here.