Deep Planet: Advancing Planetary Sustainability with Geospatial AI

Deep Planet is an innovative geospatial AI platform developed by an Oxford University startup, designed to drive agricultural and environmental sustainability through intelligent remote sensing. It delivers real-time, actionable insights by combining high-resolution satellite imagery, ground sensors, and historical crop and soil data using proprietary machine learning models trained on unique ground truth datasets. Tailored for farmers, cooperatives, supply chains, landowners, investors, governments, and utilities, Deep Planet enables precision monitoring of crop health, soil conditions, water stress, biodiversity, and climate risks—empowering smarter decisions that reduce inputs by up to 40%, minimize waste, and support carbon insetting initiatives. With specialized tools like VineSignal, SoilSignal, and Season Indices, and capabilities in variable rate application, pest detection, and harvest forecasting, it stands at the forefront of sustainable land management.

Key Features:

• Crop Health Monitoring: Real-time insights using NDVI and NDWI metrics with near-daily satellite updates at 0.5m, 3m, and 10m resolutions.
• Soil Health & Sustainability: Calibrated nutrient maps for nitrogen, phosphorus, and potassium; long-term Soil Organic Carbon (SOC) tracking for carbon insetting.
• Water Stress Monitoring: Satellite-based assessments integrated with ground probe data for irrigation optimization, leak detection, and water scarcity analysis.
• Pests & Disease Detection: Per-pixel analysis of Downy Mildew, Powdery Mildew, Botrytis, and Phylloxera to enable targeted pesticide application.
• Maturity & Harvest Logistics Forecasting: Precision algorithms predict optimal harvest dates and support split picking decisions based on spatial sugar level variation.
• Variable Rate Application Maps: AI-generated maps for optimized use of fertilizers and other inputs, reducing costs and environmental impact.
• Biodiversity & Habitat Classification: Advanced species-level vegetation analysis and habitat tracking for ecological monitoring.
• Climate Event Analytics: Real-time detection and analysis of floods, frost, hail, and other climate risks to enhance resilience.
• Regional Scans & Crop ID: Large-scale crop boundary identification and area monitoring across diverse landscapes.
• Customized Sampling Plans: Bespoke strategies based on spatial variability to reduce sampling costs and improve accuracy.
• Global Scalability: Operates across the UK, Australia, USA, France, and Saudi Arabia with localized expertise and infrastructure.
• Demo Booking: Users can schedule personalized demos to explore platform capabilities.

Pricing: Deep Planet offers a tailored pricing model with a demo booking option available—ideal for organizations seeking customized solutions. While specific pricing details are not publicly listed, the platform supports enterprise-level engagement through flexible, usage-based and subscription-based plans, with a strong emphasis on accessibility via trial and consultation.

Conclusion: Deep Planet is a transformative geospatial AI tool that equips agriculture and environmental stakeholders with intelligent, scalable insights to build resilient, sustainable systems. Its cutting-edge technology and global reach make it a vital partner in the fight against climate change and resource inefficiency.