Concept and evolution of crop advisory systems – Types: rule-based, model-based and AI-based – Role in precision agriculture and climate resilience – Components: data acquisition, analytics and delivery – Stakeholders and benefits – Global examples: DSSAT, APSIM, PlantVillage – Indian platforms: mKisan, Kisan Suvidha, Meghdoot – Exposure visit to KVK/digital demonstration farm or agri-start-up

Weather data from AWS and satellite sources – Soil and plant sensors (moisture, NPK, NDVI), IoT architecture in agriculture – Mobile-based data collection and crowd sourcing – Cloud platforms and data integration

Crop simulation models: InfoCrop, WOFOST, Oryza – Pest and disease forecasting models – Machine learning for yield prediction and pest identification – Deep learning and computer vision applications – Role of Generative AI in creating farmer advisories and chatbots – Hybrid models and explainable AI – Case studies: Plantix, FarmBeats, AgroStar

Multi-channel delivery Localized and personalized advisories in regional languages – Use of images, videos and voice messages – Integration with farm machinery and input suppliers – Feedback mechanisms and continuous improvement – Successful models: IFFCO Kisan, DeHaat, BigHaat, Ninjacart

Digital Agriculture Mission (DAM) and key schemes – Government apps: Meghdoot, Kisan Sarathi, FASAL, CHAMAN – Role of ICAR, SAUs and KVKs in digital extension – Leading Indian agri-tech start-ups (2024᎓2025) – Global best practices: Climate FieldView, John Deere Operations Center – Emerging trends: 5G/6G, digital twins, AI voice assistants – Vision 2030 for digital crop advisory in India