Every product traces back to a published paper or funded research project. No black boxes — only transparent, validated intelligence.
Six precision tools — each one traceable to a published paper, validated dataset, or funded research initiative. Deploy individually or as a unified stack.
A generalized AI framework for cross-crop disease detection. Powered by our flagship PlantCareNet architecture, the engine natively identifies 38+ crop conditions from standard mobile imagery with research-validated accuracy.
Ultra-lightweight neural networks optimized for resource-constrained environments. Deploy directly to mobile devices and edge hardware with no cloud dependency — achieving 98%+ accuracy even on 2G networks.
RESTful and gRPC endpoints for automated crop health assessment. Feed aerial or ground-level imagery and receive structured JSON reports with disease classification, severity scoring, and treatment recommendations.
Deep learning models that monitor and classify crop growth stages in real-time. Optimize planting schedules, predict harvest windows, and detect developmental anomalies before they compound.
AI-driven rapid assessment of plant responses to climate-induced stress including drought, heat, and salinity. Built on funded UGC Bangladesh research for multi-species stress forecasting.
Curated, research-grade datasets for training and benchmarking agricultural AI models. Includes high-quality annotated imagery and the industry's first large-scale VQA dataset for plant science.
Choose the pathway that fits your operation. Every solution is backed by the same research-grade intelligence.