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Product Specification

Product Name:Agronomic Modeling Training and Evaluation
Product SKU:71550-00
Revision Date:2023-11-18

Product Overview

Description

Agronomic Modeling Training and Evaluation includes a tailored consultation with Sentera's Agronomic Modeling Scientists to achieve the overall goal of training and cross-validating one or more customized agronomic model(s) that meet your specific objective(s). The primary steps in this process include (but are not necessarily limited to):

  • i) exploratory analysis on available data (to identify the expected environmental and management constraints of the trained model)
  • ii) feature engineering based on your hypotheses and/or available feature data
  • iii) hyperparameter tuning
  • iv) model experimentation, including training, cross-validation, and testing for the desired statistical algorithms and cross-validation strategies
  • v) reporting/communication of the deliverables.
  • All Agronomic Modeling Products

Deliverables Summary

  • Performance statistics for each trained agronomic model.
  • Descriptive statistics for response variables and candidate features.

Requirements

  • "Agronomic Modeling Training and Evaluation Plan" completed prior to project start date
  • "Agronomic Modeling Customer Data Contribution" provided prior to project start date
  • All "Integrated Project Team and Program Management" members identified
  • Requires FieldAgent Enterprise License (61006-##) or Enterprise Analytics Upload/Export Surcharge (71010-09)

Agronomic Modeling Training and Evaluation Plan

Prior to beginning the project (i.e. prior to any data sharing or model training), Customer will fill out an Agronomic Modeling Training and Evaluation Plan provided by Sentera.

The purpose of the Agronomic Modeling Training and Evaluation Plan is to gain an understanding of the following:

  • Agronomic objectives (what is Customer trying to solve?)
  • Historical data inventory (what types of data are available for addressing the agronomic modeling objectives?)
    • Response variable(s)
    • Feature data
    • Number of observations
    • Number of years, field trials, locations, etc. represented
    • All shared data observations must include explicit spatial and temporal information (i.e., where and when was it collected/measured?)
  • Data sharing plan (for Customer proprietary data)
  • Additional requirements may be identified based on the exact nature of agronomic modeling objectives and/or historical data inventory

Agronomic Modeling Customer Data Contribution

All customer data identified as part of the Agronomic Modeling Training and Evaluation Plan will be provided prior to beginning the project.

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.

Deliverables

Analytics

Analytic
Model Training and Evaluation (89001-00)

Model Training and Evaluation (89001-00)

Description

Model Training and Evaluation provides both performance statistics for trained agronomic model and descriptive statistics for response variables and features.

Deliverable List

DeliverableFormatDescription
Trained Agronomic Model Performance StatisticsCSVGoodness-of-fit statistics for each trained agronomic model
Trained Agronomic Model Performance VisualizationPNGVisualization of 1:1 line with measured vs. predicted values for each trained agronomic model
Response Variable and Feature Descriptive StatisticsCSVDescriptive statistics for each response variable and feature
Trained Agronomic Model Performance Statistics
AttributeDescriptionUnits MetricUnits Imperial
model_nameName of trained agronomic model--
model_typeAlgorithm used for training agronomic model--
n_featuresNumber of features used in the trained agronomic model--
feature_listRanking of features by importance for trained agronomic model--
responseName of response variable--
nTotal number of observations used--
cv_strategyCross validation strategy used for training agronomic model--
n_trainNumber of observations in the "train" split--
n_testNumber of observations in the "test" split--
r2_trainCoefficient of Determination [R2] for the "train" split
r2_testCoefficient of Determination [R2] for the "test" split
rmse_trainRoot Mean Squared Error [RMSE] for the "train" split
rmse_testRoot Mean Squared Error [RMSE] for the "test" split
rmse_rel_testRelative Root Mean Squared Error [RMSE] for the "test" split%%
mae_trainMean Absolute Error [MAE] for the "train" split
mae_testMean Absolute Error [MAE] for the "test" split
mae_rel_testRelative Mean Absolute Error [MAE] for the "test" split%%
Response Variable and Feature Descriptive Statistics
AttributeDescriptionUnits MetricUnits Imperial
variableName of response variable or feature--
meanMean value of response variable or feature
medianMedian value of response variable or feature
std_devStandard deviation of response variable or feature
minMinimum value of response variable or feature
maxMaximum value of response variable or feature