AGRIWIZ
Nutritional Recommendations
Research tools to mine data to determine optimal crop production norms
Based on current and historic data analysis
- Plant growth analysis
- Soil analysis
- Leaf analysis
- Water analysis
Fertilizer selection based on
- Irrigation type
- Enrichment system
- Organic fertilizer
- Soil parameters (in some situations)
Weekly resolution
Fertigation tank mixes
- Up to weekly resolution
- Unlimited number of tanks
- Injection rates
- Calculated ECs
- Precipitation potential warnings
- Automatic solubility calculations
- Quantification of fertilizer
- Automatic nutrition correction recommendations
Irrigation
Quantified recommendations are presented that are calculated to take prevent soil moisture depletion not exceeding that of the set Easily Available Water threshold. Depending on client requirements irrigations can be presented in frequencies and durations at daily or weekly intervals considering the current soil moisture content. High accuracy recommendations can be provided calculated to the closest minute taking into amount hourly plant water use soil reservoir EAW tolerance, irrigation delivery rate in root zone. This can be integrated and logged with automatic controllers.
Based on
- Historic data
- Modeling of canopy and root growth (weekly resolution)
- Long-term climatic data
- Daily and or hourly forecasts
- Crop factors
- Field specific parameters
- Soil water holding capacity
Diaries and reports
Data and recommendations can be presented in custom reports or actionable instructions at user, farm, field season or weekly resolutions.
Weekly actions
Soil analysis / leaf analysis
- Interpretation
- History
- Trends
Quantified irrigation and nutrition
Field parameters
- Plant specs (canopy growth, cover)
- Yield
- Plant spacing, density number etc.)
- Irrigation parameters
Monitoring
Field monitoring of nutritional and soil parameters possible through direct monitoring or by field-based sensors (we are currently working on integrating most of the major field sensor data providers with our software).
Visualization via graphs or data superimposed on satellite images
Based on
- Historic data
- Modeling of canopy and root growth (weekly resolution)
- Long-term climatic data
- Daily and or hourly forecasts
- Crop factors
- Field specific parameters
- Soil water holding capacity
AgriWiz is currently focusing on improving user interface navigation and dashboard. We’re also laying the foundation for us to provide a much richer and more intelligent feedback system for the user. This will provide insights to the user based on machine learning, look for anomalies in the data, and permit easy integration of outside data such as soil and plant sensors.
