When Catherine Nakalembe, an assistant professor at the University of Maryland and director of the Africa program at NASA Harvest, set out to map crop types across western Kenya, she quickly discovered that the most sophisticated computer‑vision tools could not see the fields she needed to study. The satellite imagery was abundant, but the pretrained models—most of them trained on European and U.S. farms—failed to recognize staple African crops such as maize, beans and cassava. To bridge the gap, Nakalembe equipped dozens of local volunteers with GoPro cameras mounted on their helmets and harvested more than five million ground‑level images in a fortnight. The resulting dataset fed a custom deep‑learning pipeline capable of distinguishing the three crops and estimating yields for Kenya, Uganda, Senegal and several other African nations.

Nakalembe’s experience is emblematic of a broader challenge confronting AI‑driven agritech in low‑ and middle‑income economies. According to a statement from NASA Harvest, most existing models assume high‑speed internet, abundant labeled data and agricultural practices that mirror those of temperate, industrialised regions. In many parts of the Global South, internet bandwidth remains costly, connectivity is intermittent, and the diversity of smallholder farms defies the one‑size‑fits‑all approach of many Western‑origin algorithms. "If these systems aren’t adapted, they remain irrelevant, potentially deepening existing inequalities in wealth and access to resources," she warned.

The stakes are high. Agriculture still provides livelihoods for more than two billion people in developing economies, a demographic that is increasingly vulnerable to climate‑driven shocks such as erratic rainfall, heat stress and pest outbreaks. The United Nations’ Sustainable Development Goal of zero hunger is already off‑track, with the Food and Agriculture Organization estimating that roughly 28 % of the world’s population—about 2.3 billion people—faces moderate to severe food insecurity.

Against this backdrop, governments, NGOs and private firms are racing to apply AI to everything from deforestation monitoring to real‑time advisory services for farmers. In Brazil’s Pará state, the environmental nonprofit Rare has deployed an AI system that converts coastal sensor data into voice alerts delivered via WhatsApp, helping fishers and oyster growers avoid hazardous conditions. Microsoft’s research arm is using bioacoustic AI to listen for illegal logging in the Amazon, while Digital Green’s FarmerChat app, now active in more than one million households across South Asia and Africa, leverages generative language models to answer agronomic questions in 16 local languages. Rikin Gandhi, co‑founder and chief executive of Digital Green, told Rest of World that the platform has fielded over eight million queries, training small language models on a corpus of 120 000 farmer‑expert interactions to capture the colloquial terminology used in the field.

The emphasis on hyperlocal relevance is echoed by Oren Ahoobim, a partner at the consultancy Dalberg Advisors. He noted that recent advances in satellite resolution, drone imaging and cloud‑based compute have dramatically improved the quality of raw data feeding AI pipelines, allowing predictive insights that can guide planting decisions, fertilizer application and disease management. "Better information translates into better decisions earlier in the season," Ahoobim said, underscoring the potential for yield gains and cost reductions.

Nevertheless, the technology’s promise is not universal. In Maharashtra, western India, the conservation group Farmers for Forests attempted to use a popular open‑source model—originally trained on North American forest inventories—to map tree cover for a reforestation initiative. The model missed more than half of the trees, prompting co‑founder Arti Dhar to lead a manual annotation effort that labelled roughly 55 000 trees across 80 drone‑captured parcels. The team then fine‑tuned Meta’s Detectron2 framework on the newly created dataset, enabling accurate 3D mapping of individual trees, canopy dimensions and estimated carbon sequestration. This information, Dhar explained, can be monetised through emerging carbon‑credit schemes, providing a supplemental income stream for smallholders.

The divergent outcomes in Kenya, Brazil and India illustrate a central lesson for investors eyeing the $30 billion agritech market that recorded in 2025, with forecasts projecting a rise to $84 billion by 2034. Tech giants such as Google, Microsoft, Amazon, IBM and Alibaba have already launched AI‑focused agricultural platforms, betting on the sector’s growth. Yet experts caution that without careful localisation, these solutions risk becoming a form of digital colonialism. The International Panel of Experts on Sustainable Food Systems warned that large agribusinesses could extract data from vulnerable communities, train proprietary models, and then sell back services that prioritize high‑value commodity crops—corn, rice, wheat, soy and potatoes—over diversified, nutrition‑secure food systems.

For policymakers, the imperative is to foster data‑sovereignty and ensure that AI tools are co‑created with the people who will use them. Both Gandhi and Dhar stress that trust and equity are non‑negotiable. An AI system that assumes universal literacy, reliable connectivity or top‑down decision‑making will inevitably favour better‑resourced farmers, widening the gap between large commercial operations and smallholders. Moreover, models that optimise solely for short‑term yields can overlook longer‑term ecological constraints such as water scarcity, soil degradation and energy consumption, undermining resilience.

The emerging consensus among agritech practitioners is that technology must be a means to amplify farmer agency, not replace it. As Nakalembe concluded, "The most accurate AI in the world is useless if it isn’t embedded in a system of trust and aligned with the real‑world economic needs of farmers." For investors, this translates into a need to scrutinise not only the technical sophistication of AI solutions but also the governance frameworks, data ownership models and on‑the‑ground partnerships that underpin them. The next wave of agricultural AI will likely be defined as much by its inclusivity and adaptability as by its algorithmic performance, shaping the geopolitical balance of food security and the distribution of digital value across the Global South.