Course Overview
This 5-module course provides agricultural professionals, data analysts, and researchers with the practical tools and methodologies needed to use predictive analytics for improving crop yield forecasting and decision-making. Through the integration of data science, remote sensing, and machine learning techniques, participants will learn how to develop accurate yield models that can support smarter planning, resource allocation, and food security strategies.
DURATION
5 Days
Target Audience
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Agricultural Data Analysts
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Agronomists and Crop Scientists
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Researchers and Extension Officers
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Government Planners and Policy Advisors
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Agri-Tech Developers and Solution Providers
Course Level: Intermediate to Advanced
Learning Objectives
By the end of this course, participants will:
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Understand the principles of predictive analytics and data-driven agriculture
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Identify key variables and datasets used in crop yield prediction
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Apply statistical and machine learning models for yield forecasting
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Integrate remote sensing and geospatial tools for data acquisition
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Evaluate prediction accuracy and use results to guide farm-level and policy decisions
COURSE OUTLINE
Module 1: Fundamentals of Predictive Analytics in Agriculture
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Overview of predictive analytics and its application in farming
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Data requirements and quality considerations
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Key indicators influencing crop performance
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Case Study: Historical yield trend analysis using local data
Module 2: Data Sources and Preprocessing Techniques
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Remote sensing data (NDVI, rainfall, temperature, soil moisture)
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Use of IoT and sensor-based data collection
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Cleaning, normalization, and feature engineering
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Practical Exercise: Building a clean dataset for a maize yield model
Module 3: Modeling Techniques and Tools
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Linear regression, decision trees, and ensemble models
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Introduction to time series analysis and deep learning approaches
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Software/tools: Python, R, Excel, Google Earth Engine
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Lab Session: Comparing models using historical crop datasets
Module 4: Spatial and Temporal Yield Mapping
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GIS integration for location-based yield forecasting
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Visualizing and interpreting predictive maps
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Precision agriculture and variable rate technology
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Activity: Creating a geospatial yield prediction map
Module 5: Evaluation, Application, and Decision Support
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Model validation and performance metrics (RMSE, R2, MAE)
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Interpreting outputs for farm management or policy formulation
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Real-world implementations of predictive analytics in agriculture
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Final Project: Developing a predictive model for a selected crop scenario
Customized Training
This training can be tailored to your institution needs and delivered at a location of your choice upon request.
Requirements
Participants need to be proficient in English.
Training Fee
The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.
Certification
A certificate from Ideal Workplace Solutions is awarded upon successful completion.
Accommodation
Accommodation can be arranged upon request. Contact via email for reservations.
Payment
Payment should be made before the training starts, with proof of payment sent to outreach@idealworkplacesolutions.org.
For further inquiries, please contact us on details below:
