AI in agriculture promises to improve yields and optimize crops. It analyzes images, weather data, and soil characteristics. This information helps farmers make decisions. However, its performance varies greatly in different regions. The results often remain disappointing outside of the areas where these tools were designed. This situation reveals a fundamental problem. The models do not always take into account the diversity of agricultural realities.
AI in agriculture fails to address the diversity of terrains
AI in agriculture often relies on machine learning. This method allows a program to identify patterns in large databases. It then learns to recognize a plant, soil, or disease. In theory, this logic seems very effective. In practice, it depends entirely on the quality of the data used initially.
However, much of this data comes from Europe or North America. Thus, the models learn to recognize very specific crops, landscapes, and agricultural rhythms. When used elsewhere, errors quickly multiply. A local plant may be misidentified. A mixed field may be misinterpreted. Additionally, a plot cultivated according to other practices may become difficult for the algorithm to analyze.
This weakness becomes apparent when moving beyond Western contexts. In certain regions of Africa or Asia, farms are smaller and more diverse. Crops often coexist on the same plot. Practices also vary depending on rainfall, altitude, or access to markets. As reported by the media Rest of World, some systems fail to recognize commonly found crops. Local teams have therefore collected millions of images themselves to correct these gaps. Without this adaptation work, AI remains unreliable and can worsen disparities between regions.
Improving yields but accentuating inequalities
Applied in this field, AI can provide very concrete services. It helps to detect crop diseases earlier. It can also analyze satellite images, meteorological data, or photos sent by mobile phones. Thus, these tools can adjust irrigation, sowing, or the use of inputs. An input is a product used to support production. It can be fertilizer or a treatment. In the right contexts, these systems improve decision-making accuracy.
This logic extends beyond crops. In several African countries, AI also helps facilitate access to rural credit. It assesses risk based on agricultural, climate, or mobile data. This allows some farmers to receive a quicker response than through traditional banking channels. This development can help farmers excluded from the financial system. It can also support the purchase of seeds, tools, or small equipment.
According to an analysis relayed by Agence Ecofin, these solutions have already shown positive effects. They improve productivity and access to financing. But these benefits remain very unequal. In many rural areas, internet connection remains unreliable. Furthermore, electricity is still lacking or too expensive. Not all farmers have the same digital skills. Thus, rural women and small-scale farmers are often underserved. AI can improve certain situations, but it does not automatically reduce inequalities.
A need to adapt to local realities
But the main question is about its real integration into the territories. Agriculture remains a profoundly local activity. Soil types, rainfall, pests, and challenges vary from one village to another. In such conditions, a standardized model quickly reaches its limits. Thus, useful technology must integrate the agronomic, economic, and social realities of the terrain.
This is why many specialists advocate for tools designed with local stakeholders. They recommend producing data directly on-site. They also encourage the use of languages actually spoken by farmers. Furthermore, they emphasize the importance of trust. A farmer will have difficulty adopting a tool he does not understand. He may also reject it if it does not fit his constraints. AI must therefore integrate into existing human networks. It should support decision-making without abstractly replacing it.
In the long run, the debate goes beyond technical performance alone. It also concerns data sovereignty and economic power. If a few large companies control agricultural data, they can steer usage in their favor. They may prioritize the most profitable crops. On the other hand, they may neglect local systems. AI in agriculture must not remain a technology detached from reality. Its effectiveness depends on its ability to strengthen farmers’ autonomy.
