In the context of sustainable agriculture, artificial intelligence (AI)-based decision support systems (DSS) can be customized to meet the specific agronomic needs of different millet species, while taking into account the socio-economic and environmental constraints faced by smallholder farmers. Different millet species, such as pearl millet, finger millet, foxtail millet, and barnyard millet, have different requirements in terms of soil type, water requirement, sensitivity to temperature and light, and resistance to pests and diseases. Therefore, DSS systems need to be trained using species-specific agronomic data, including historical yield data, soil test results, local weather patterns, and crop growth cycles in a particular region. This approach can also be enhanced by the use of crop models such as DSSAT and ontology-based knowledge structures.

Socio-economic constraints faced by smallholder farmers, such as limited access to quality agricultural inputs, financing, low digital literacy, and limited land ownership, should be part of the system design considerations. AI-based DSS can provide economically relevant agricultural advice by integrating local price trends, input availability, and predicted returns on investment. In addition, the system should have a simple interface and support local languages, including through text messaging or voice. The recommendations should also prioritize low-cost and locally resource-based practices, such as intercropping millet with legumes to improve soil fertility.

Environmental aspects such as soil degradation, erratic rainfall, and climate change are major challenges in millet cultivation. DSS can be designed to integrate remote sensing data and geospatial data, such as the natural vegetation index (NDVI) and soil moisture, to provide advice based on real-world conditions. The system can also leverage climate models to suggest appropriate planting times, drought-tolerant varieties, and irrigation schedules. Training the AI ​​based on environmental stress responses of different millet varieties will also improve the relevance of the recommendations to farmers.

For AI systems to be truly adaptive and contextual, approaches that support local learning, such as federated learning, are needed so that models can be continuously updated based on data from different regions without compromising privacy. The system should also support human intervention, especially agronomists or agricultural extension workers, who can validate and adjust recommendations to better suit real-world conditions. Transfer learning from similar crop data or from neighboring areas can also help address data scarcity in certain areas.

In addition, the DSS should be integrated into existing institutional and policy systems. This includes collaboration with field extension workers to distribute the DSS, linkages to government input subsidy or agricultural insurance programs, and the implementation of open data policies so that the system can be interoperable across regions and crops. As an illustration, a platform called AI4Millets can be developed with a modular interface that presents information in stages, from recommendations based on soil tests, variety selection, planting time, pest predictions, to marketing advice. The platform can function offline, using crowdsourcing to detect plant disease symptoms through image recognition, and providing a dashboard for NGOs or agricultural agencies to monitor impacts across villages.

Challenges that may arise include data scarcity for minor millets, low user trust in new technologies, and limited digital infrastructure. This can be addressed through synthetic data generation, participatory testing with farmers, co-design approaches that involve users from the start, and the use of software that can function offline and use renewable energy sources such as solar panels. Overall, AI in DSS for millet cultivation by smallholder farmers should be inclusive, local, and collaborative. AI should not replace traditional knowledge, but rather be a strategic partner in creating farming systems that are more resilient to climate change, more productive, and socially and environmentally sustainable.