In the state of Kerala, the ANNAM.AI system is being implemented to adapt agriculture to the climate.

The project's implementation is part of the government's 100-day plan. The goal of the initiative is to create a sustainable agricultural ecosystem by providing farmers with reliable data for planning sowing, irrigation, plant protection, and harvesting. According to Pushpendra Singh, director of the ANNAM.AI project and Dean of IIT Ropar, the use of such stations will reduce production costs and introduce science-based farming methods.

Kerala was chosen as the site due to the region's high climate sensitivity: it experiences sharp microclimatic fluctuations against a backdrop of heavy annual rainfall exceeding 3000 mm. Most local farms are less than 1 hectare in size and specialize in multi-layered cropping systems of coconuts, pepper, bananas, spices, and vegetables, which require precise control of humidity, sunlight, and precipitation levels.

The ANNAM.AI technology, developed at IIT Ropar, differs from previous automatic weather station systems by analyzing seven parameters: temperature, humidity, precipitation volume, wind speed and direction, solar radiation, and soil moisture. The system ensures forecast accuracy with a 3 km resolution, which allows for data acquisition at the village level. Based on this information, AI algorithms generate operational recommendations for pest and disease control, as well as alerts for responding to natural disasters.

Kerala's experience is being viewed as a foundation for creating an all-India template for implementing climate intelligence in agriculture. The data obtained will help build advisory models for various crops, develop risk-response algorithms for high-rainfall regions, and demonstrate the effectiveness of AI tools in a decentralized agricultural sector.

In the coming growing seasons, the state's farmers will be able to use more accurate forecasts for work planning, timely warnings about pathogens, soil moisture data for irrigation optimization, and information that helps reduce losses during post-harvest storage.