Crop Forecasting Platform
Make Better Decisions to Increase Profit
In an increasingly risky environment, planning and forecasting are at the core of any farming business success. SatFarming’s crop modeling platform supports farmers and agricultural enterprises in their daily decision making by bringing accurate and valuable inputs from the sky and the field. Our proprietary software uses a cutting-edge, multi-dimensional calculation engine combining satellite imagery, weather information, daily data collected by soil and crop sensors to provide real-time results about growth stages, biomass, nitrogen index, water balance and yield. Accurate and powerful, SatFarming makes planning, crop strategy and farming management easier.
SatFarming Processing
Benefits From Using Our Platform
Unparalleled Calculation Performance
Gain Insight Previously Unattainable
Expertise By Your Side
Minimize Uncertainty, Increase Profit
SatFarming Processing
How It Works
Real-Time Results
Test By Yourself SatFarming Performances
Yes, we mentioned EASY, FAST and POWERFUL! But it is more convincing if you see it for yourself. Contact us if you are:
- A corporation interested using SatFarming solutions.
- A Partner looking to integrate SatFarming in to your services.
- An individual farmer in need of additional feedback to manage your crops.
Blog
Recent articles
Biomasses colza avec Sentinel 2
En ce début d'année, les colzas bien développés ont consommé une bonne partie du reliquat azoté du précédent cultural, la scénescence des feuilles entraine leur coloration jaune et violette indiquant une mise en sommeil de la plante. Elle repartiront au printemps à la...
Maize yield map generation using Sentinel 2
Not all harvesters have a yield sensor allowing the development of geo-referenced harvest data. The use of an agro-physiological model in conjunction with satellite data makes it possible to approach the variability of the intra-plot yield. The satellite used is...
Création de cartes de rendement sur culture de maïs avec Sentinel 2
Toutes les moissonneuses ne disposent pas de capteur de rendement permettant l'élaboration de données de récolte géo-référencées.L'utilisation d'un modèle agro-physiologique en conjonction avec des données satellitaire permet d'approcher la variabilité du rendement...