An integrated precision agriculture platform combining satellite imagery analysis, machine learning, and conversational AI for data-driven farm management and decision intelligence.
ML models analyze satellite vegetation indices (NDVI, EVI, SAVI), weather patterns, and historical data to forecast yields. Leverages time-series analysis and regression models for high-accuracy predictions.
Convolutional Neural Networks (CNNs) analyze crop imagery to detect visual symptoms like discoloration, lesions, and abnormal growth patterns. Trained on extensive datasets for high-precision diagnosis.
Computer vision systems capture and analyze field images to identify pests and assess damage levels. ML algorithms classify pest types and recommend targeted intervention strategies.
AI-powered systems distinguish crops from weeds using visual characteristics. Guides precision sprayers and robotic weeders for targeted treatment, minimizing herbicide use.
RAG-powered conversational AI provides expert guidance, answers questions, and recommends interventions. Combines LLM capabilities with domain-specific agricultural knowledge base.
AI models predict climate impacts on crop phenology and productivity. Recommends adaptive strategies including cultivar selection and optimal planting schedules.
Production-grade pipeline from data ingestion to actionable insights