MapMyCrop: AI-Powered Precision Sugarcane Intelligence; Satellite-Based Crop Monitoring, Sucrose Prediction, and Harvest Optimisation for Sugarcane Farmers and Sugar Mills

India is the world's second-largest sugarcane producer with 5.2 million hectares under cultivation, yet faces a significant productivity paradox. Average yields remain at 98–148 tonnes per hectare, far below the achievable 221–358 tonnes. Farmers lack access to timely intelligence on soil health, irrigation, nutrient management, and optimal harvest timing. Premature harvesting reduces sucrose by 1–3%, impacting farmer Fair and Remunerative Price payments, while mills experience 0.5–1.0% recovery losses.

MapMyCrop's sugarcane intelligence platform converts satellite and field data into farm-level recommendations through an integrated workflow. The system ingests imagery from multiple satellite constellations with 10-day to daily revisits, ensuring continuous monitoring even during cloud-prone monsoons. Proprietary super-resolution models enhance imagery to 1m resolution while integrating hyperlocal weather and soil parameters to create a digital twin of each farm. Proprietary algorithms process more than 30 remote sensing layers to compute vegetation indices including NDVI, EVI, NDWI, and chlorophyll content. A crop stress differentiation model distinguishes pest damage, nutrient deficiency, and water stress, enabling targeted interventions.

Four AI advisory engines generate actionable insights: an AI fertigation engine adjusts NPK dosage based on vegetation-rainfall correlations; an AI germination model predicts emergence from soil moisture and temperature; a pest forecast model triggers seven-day advance alerts for shoot borer, top borer, and whitefly; and an AI ripening model optimises nutrient schedules for maximum sucrose accumulation. A sucrose prediction model trained on more than 50,000 ground-truth samples predicts sugar concentration at multiple stalk positions with up to 95% accuracy. The maturity ratio algorithm determines optimal harvest timing, generating 15-day forward harvest windows for peak Brix levels. API integrations allow mills to coordinate harvest schedules, improving recovery rates. Advisories are delivered via mobile app and WhatsApp in Hindi, Marathi, Kannada, and Tamil.

The platform monitors more than 3,000 sugarcane farms across Maharashtra, Karnataka, Madhya Pradesh, and Tamil Nadu, spanning over 15 districts with partnerships with 25+ sugar cooperatives and FPOs. Documented outcomes show farmers achieved yields of 221–358 tonnes per hectare compared to traditional 98–148 tonnes, representing improvements exceeding 57%. AI fertigation reduced fertiliser use by 20–25% while improving nutrient efficiency. Early pest alerts reduced crop loss by 10–15%. Continuous validation through mill laboratory readings and farmer feedback improved model accuracy from 85% to 95%. Data governance follows consent-based collection with farmers retaining ownership and deletion rights.
For additional context and detailed documentation of this use case, please refer to pages 14-15 in the attached Casebook.