Grimshaw democratizes precision agriculture using AI-processed satellite imagery to provide field-level insights via smartphone. Monitor crop health, predict yields, detect diseases before visible symptoms, optimize irrigation scheduling, and receive precise fertilizer recommendations—all updated every 3 days using free Sentinel-2 satellite imagery.
Grimshaw transforms raw satellite data into actionable agricultural insights. Our AI-powered platform processes multi-spectral imagery from Sentinel-2 satellites to deliver precision agriculture tools that were previously available only to large commercial operations—now accessible to every farmer via smartphone.
Advanced machine learning models analyze vegetation indices, growth patterns, and historical data to predict crop yields with remarkable accuracy. Our algorithms process NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), and chlorophyll content measurements to forecast harvest quantities weeks in advance. This enables better planning for storage, logistics, and market timing decisions.
Detect crop diseases and stress conditions before they become visible to the naked eye. Our spectral analysis identifies subtle changes in plant reflectance patterns that indicate disease onset, nutrient deficiencies, or pest infestations. By monitoring red-edge bands and analyzing vegetation health indices, we can alert you to problems 5-10 days before symptoms appear, giving you crucial time to respond.
Optimize water usage with data-driven irrigation scheduling based on real-time soil moisture estimates and evapotranspiration rates. Our platform calculates precise water requirements by analyzing vegetation water content through shortwave infrared bands, combined with local weather data and soil characteristics. Reduce water waste by up to 30% while maintaining optimal crop health.
Receive zone-specific fertilizer recommendations based on crop vigor and nutrient status analysis. By measuring chlorophyll content and biomass through spectral indices, our system identifies areas of varying nutrient needs within your fields. Apply the right amount of fertilizer in the right place, reducing costs and environmental impact while maximizing crop nutrition.
Grimshaw leverages the European Space Agency's Sentinel-2 satellite constellation, part of the Copernicus Earth observation program. This twin-satellite system provides unprecedented access to high-resolution, multi-spectral imagery covering every land surface on Earth every 3-5 days—completely free of charge.
Sentinel-2 satellites carry Multi-Spectral Instruments (MSI) that capture imagery across 13 spectral bands, ranging from visible light to shortwave infrared. This goes far beyond what the human eye can see, revealing critical information about plant health, water content, and soil conditions that would otherwise remain invisible.
Grimshaw's automated pipeline transforms raw satellite data into actionable insights without requiring any technical expertise from you. Simply define your fields once, and receive continuous monitoring and recommendations directly on your phone.
Every 3-5 days, the Sentinel-2 satellite constellation passes over your location, capturing high-resolution multi-spectral imagery. Our system automatically downloads this data from the European Space Agency's servers within hours of acquisition. The satellites capture 13 spectral bands ranging from visible light through near-infrared to shortwave infrared, providing a comprehensive view of your fields' condition.
Our proprietary AI algorithms process the raw satellite imagery through multiple analysis layers. First, atmospheric correction removes interference from clouds, haze, and atmospheric conditions. Then, specialized neural networks calculate vegetation indices including NDVI, EVI, NDMI, and custom spectral signatures. Machine learning models trained on thousands of fields identify patterns indicating crop health, growth stage, stress conditions, and potential diseases. The system also integrates historical data, weather information, and soil maps to provide context-aware analysis.
Within hours of satellite data acquisition, processed insights appear in the Grimshaw mobile app on your smartphone. You receive visual maps showing field health variations, numerical metrics for key indicators, and specific recommendations for action. Alerts notify you of urgent conditions like disease outbreaks or water stress. All information is presented in an intuitive interface designed for use in the field—no data science background required. Historical trends let you track improvements over time and compare current conditions to previous seasons.
Grimshaw provides a complete precision agriculture solution, from field boundary definition to harvest planning. Our platform integrates satellite imagery analysis with agronomic expertise to deliver insights you can act on immediately.
Monitor crop health through scientifically-proven vegetation indices calculated from multi-spectral satellite data. Each index reveals different aspects of plant physiology and field conditions.
The most widely used vegetation index. NDVI measures the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs). Values range from -1 to 1, with healthy vegetation typically showing values between 0.6 and 0.9. Use NDVI to assess overall crop vigor, identify areas of poor growth, and estimate biomass.
An improved vegetation index that corrects for atmospheric conditions and soil background. EVI is particularly useful in areas with high biomass where NDVI may saturate. It provides better sensitivity in dense canopy conditions and reduces atmospheric and soil noise through the use of the blue band.
NDMI detects moisture levels in vegetation using the contrast between near-infrared and shortwave infrared bands. Since water strongly absorbs shortwave infrared radiation, NDMI is sensitive to vegetation water content and soil moisture. Values range from -1 to 1, with higher values indicating higher moisture content.
NDRE uses the red edge band (around 710nm), which is particularly sensitive to chlorophyll content variations. This index excels at detecting subtle changes in plant health and is especially useful for nitrogen status assessment. It's more sensitive than NDVI to chlorophyll variations in mid- to late-season crops.
SAVI minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared wavelengths. It's particularly useful in early-season monitoring when vegetation cover is sparse and soil background has a significant impact. The L factor (typically 0.5) adjusts for soil brightness.
LAI represents the total one-sided area of leaf tissue per unit ground surface area. It's a key parameter for understanding crop growth dynamics, light interception, and photosynthetic capacity. Our AI models estimate LAI using multiple spectral bands and empirical relationships validated against ground truth data.
Our AI models analyze spectral signatures to identify disease patterns before they're visible to the human eye. By monitoring changes in chlorophyll content, leaf structure, and water content across multiple spectral bands, we can detect fungal infections, bacterial diseases, and viral conditions in their earliest stages.
Predict harvest quantities 4-8 weeks in advance using machine learning models trained on historical yield data and satellite observations. Our system analyzes vegetation indices, biomass accumulation, flowering patterns, and environmental conditions to generate field-specific yield estimates with 90-95% accuracy.
Generate prescription maps for variable rate application of fertilizers, pesticides, and growth regulators. Our system identifies management zones within fields based on crop vigor variations, allowing you to apply inputs precisely where needed—reducing costs and environmental impact while optimizing crop nutrition.
Track crop development through key phenological stages using temporal analysis of vegetation indices. Our algorithms identify emergence, vegetative growth, flowering, grain fill, and maturity stages automatically, helping you time interventions optimally and plan harvest operations efficiently.
Optimize irrigation with field-specific water requirement calculations based on crop water stress indices, evapotranspiration estimates, and soil moisture modeling. Our system identifies areas experiencing water stress and provides irrigation recommendations tailored to your specific crop, soil, and weather conditions.
Access years of historical satellite imagery and vegetation index data for your fields. Compare current season performance against previous years, identify long-term trends in soil productivity, and understand how different management practices have affected crop performance over time.
From small family farms to large commercial operations, Grimshaw provides actionable insights for all major crop types. Our platform adapts to your specific needs, whether you're growing cereals, oilseeds, vegetables, or specialty crops.
Monitor cereal crops from planting through harvest with comprehensive vegetation indices and growth stage tracking. Detect nitrogen deficiencies early during tillering, optimize fungicide timing based on disease risk assessments, and predict yield weeks before harvest for better market planning.
Maximize corn yield with precise nitrogen management based on NDVI and NDRE measurements during critical growth stages. Monitor emergence uniformity, detect pest pressure early, optimize sidedress nitrogen applications, and predict final yields to coordinate harvest and storage operations.
Track soybean development through vegetative and reproductive stages with specialized vegetation indices. Identify iron deficiency chlorosis early, monitor for sudden death syndrome, detect insect defoliation, and optimize harvest timing based on canopy senescence patterns analyzed from satellite data.
Monitor cotton from planting through boll development with multi-spectral analysis. Detect early-season stress, optimize irrigation during critical flowering and boll filling stages, identify nutrient deficiencies, and track crop maturity to coordinate defoliation timing and harvest operations.
Manage rice cultivation with specialized indices adapted for flooded conditions. Monitor transplanting establishment, track tillering progress, detect nitrogen stress during vegetative growth, optimize water levels throughout the season, and predict heading dates for precise harvest planning.
Track high-value vegetable crops with frequent monitoring and precise recommendations. Detect disease pressure in dense canopies, optimize fertigation schedules, monitor crop uniformity for coordinated harvesting, and ensure quality standards through continuous health assessment from planting to harvest.
Join farmers worldwide who are leveraging satellite intelligence for precision agriculture. Grimshaw provides professional-grade crop monitoring tools at a fraction of traditional costs, powered by free Sentinel-2 satellite imagery and cutting-edge AI analysis.