India is scaling two major technology initiatives that could redefine how farmers plan sowing, manage risks and respond to climate uncertainty. A new integrated approach combining satellite-based crop monitoring with AI-led monsoon onset forecasting is now being rolled out across multiple states, signalling a shift toward data-rich, predictive agriculture.
Satellite Intelligence Now Central to National Crop Estimates
The Mahalanobis National Crop Forecast Centre (MNCFC) has expanded its satellite-driven forecasting programme, FASAL, which now covers 557 districts across 20 states. Using multispectral and microwave imagery, the system maps 11 major crops—paddy, wheat, cotton, sugarcane, jute, soybean, tur, gram, mustard, lentil and rabi sorghum. Yield modelling integrates vegetation indices, soil moisture signals and weather parameters to generate pre-harvest production estimates.
These datasets form the backbone of several government schemes, most notably the Pradhan Mantri Fasal Bima Yojana (PMFBY). Through YESTech, satellite observations guide the selection of Crop Cutting Experiment sites and generate gram-panchayat-level yield estimates for paddy, wheat and soybean. This approach is already helping insurers and state agriculture departments reduce discrepancies in yield assessments and claims.
The programme’s next phase is being built with ISRO’s support. The two agencies are operationalising a semi-physical modelling framework designed to deliver more accurate, near-real-time yield predictions. ISRO centres are also conducting R&D to include additional crops and refine model performance. State agriculture departments contribute ground-truth datasets used to train and validate mapping algorithms, enabling district-level precision in crop monitoring.
AI Models Provide Hyperlocal Monsoon Onset Forecasts
Alongside satellite-based crop tracking, the government ran a large-scale pilot to test AI-enabled monsoon predictions for Kharif 2025. Developed with the Development Innovation Lab–India, the pilot blended Google’s NeuralGCM, ECMWF’s Artificial Intelligence Forecasting System (AIFS) and 125 years of IMD rainfall data to estimate local monsoon onset dates across 13 states.
Unlike traditional seasonal forecasts, the AI model focused exclusively on the onset window, allowing farmers to adjust sowing schedules with far greater confidence. The forecasts were pushed to 3.88 crore farmers via SMS through the M-Kisan portal in Hindi, Odia, Marathi, Bangla and Punjabi.
Farmer feedback surveys conducted through Kisan Call Centres in Madhya Pradesh and Bihar showed a meaningful behavioural shift: 31–52% of respondents altered planting decisions, including land preparation timing, seed choice and input planning. Despite the large-scale outreach, the pilot was executed without any financial assistance component, relying solely on digital dissemination.
A Unified Data Stack for Climate-Smart Agriculture
Taken together, the satellite-led yield models and AI-enabled monsoon forecasting represent the early foundation of a national agri-intelligence grid. Satellite inputs reduce subjectivity in crop estimation, while AI-driven climate models give farmers actionable lead time before the sowing window. Over time, integrating the two systems could allow for district-level advisories that combine crop health, rainfall onset, pest stress and expected yield, creating a far more responsive agricultural ecosystem.
The initiative reflects India’s broader push to embed digital systems into farm decision-making—moving from post-event reporting to anticipatory, data-driven guidance. With both projects now expanding, the country is building one of the world’s largest operational agri-analytics programmes, combining space, climate science and AI to support millions of farmers in an increasingly volatile climate environment.
