Product Name:Agronomic Modeling Prediction Setup
Agronomic Modeling Prediction Setup includes the selection and deployment of a trained agronomic model for making future predictions (access to to a trained model is a prerequisite). The primary steps in this process include (but are not necessarily limited to):
- i) selection of previously trained agronomic model(s) for delivering future predictions (includes determination of the response variables and the features to use)
- ii) determination of geographical or temporal constraints for which to deploy each selected model
- iii) loading any new proprietary agronomic Customer data needed before, during, or after new predictions are made
- iv) prediction feature engineering setup based on the specific features required by the trained model.
Recommended Use Cases
- All Agronomic Modeling Products
- Performance statistics for selected trained agronomic model(s).
- Performance visualization for selected trained agronomic model(s).
- Requires Agronomic Modeling Training and Evaluation (71550-00)
- "Agronomic Modeling Prediction Plan" completed prior to project start date
- "Agronomic Modeling Customer Data Contribution" provided by Customer to Company
- All "Integrated Project Team and Program Management" members identified
- Requires FieldAgent Enterprise License (61006-##) or Enterprise Analytics Upload/Export Surcharge (71010-09)
Agronomic Modeling Prediction Plan
Prior to beginning the project (i.e. prior to any predictions), Customer will fill out an Agronomic Modeling Prediction Plan provided by Sentera.
The purpose of the Agronomic Modeling Prediction Plan is to determine:
- Identify Features used for generating prediction(s) for each Response Variable
- Planned date(s) for generating prediction(s)
- Method for sharing any customer data required for generating prediction(s)
Agronomic Modeling Customer Data Contribution
All customer data identified as part of the Agronomic Modeling Prediction Plan will be provided by Customer to Company.
Integrated Project Team and Program Management
Sentera is experienced in delivering complex projects for its Customers using best practices in project management and execution, including the formation of a dedicated Integrated Project Team to ensure project success through regular status and update meetings.
Sentera proposes an Integrated Project Team to execute this project with team member assignments including:
- Sentera Project Manager
- Customer Project Lead
- Customer Field Supervisors or Technicians (point of contact for each site)
The Sentera project manager will provide overall management and coordination for Sentera's effort on this project. The Sentera project manager will work directly with the customer's assigned project lead(s).
After an initial kick-off meeting, short weekly or bi-weekly status calls will be held by the integrated project team leading up to and during key phases of the project. This ensures project success through scheduling data collections to match conditions on the ground and provide useful feedback throughout the course of the project. Project schedules will be updated throughout the project duration.
|Model Selection (89002-00)|
Model Selection (89002-00)
DescriptionModel Selection selects the trained agronomic models to use for generating future predictions, and provides both performance statistics for the selected trained agronomic model and descriptive statistics for selected response variables and selected features.
|Selected Trained Agronomic Model Performance Statistics||CSV||Goodness-of-fit statistics for each selected trained agronomic model|
|Selected Trained Agronomic Model Performance Visualization||PNG||Visualization of 1:1 line with measured vs. predicted values for each selected trained agronomic model|
Selected Trained Agronomic Model Performance Statistics
|Attribute||Description||Units Metric||Units Imperial|
|model_name||Name of trained agronomic model||-||-|
|model_type||Algorithm used for training agronomic model||-||-|
|n_features||Number of features used in the trained agronomic model||-||-|
|feature_list||Ranking of features by importance for trained agronomic model||-||-|
|response||Name of response variable||-||-|
|n||Total number of observations available for training||-||-|
|n_train||Number of observations actually used for training||-||-|
|r2_train||Coefficient of Determination [R2] for the "train" split||–||–|
|rmse_train||Root Mean Squared Error [RMSE] for the "train" split||–||–|
|rmse_rel_train||Relative Root Mean Squared Error [RMSE] for the "train" split||%||%|
|mae_train||Mean Absolute Error [MAE] for the "train" split||–||–|
|mae_rel_train||Relative Mean Absolute Error [MAE] for the "train" split||%||%|