Crop Yield Prediction and Optimization
Applied agricultural data science exploring machine-learning methods for crop-yield prediction and agricultural planning.
Placeholder - Licensed agricultural field photography or actual GIS maps to be added
Overview
This work involved agricultural research including data preprocessing, Python/TensorFlow model development, statistical modeling, data visualization, and GIS-based agricultural mapping during a research placement at IITA in Cotonou in 2023.
Our Approach
Apply machine learning and statistical methods to agricultural datasets, incorporating GIS data for spatial analysis of crop yield patterns.
Methods & Technology
- Agricultural data preprocessing
- TensorFlow model development
- Statistical yield modeling
- GIS-based agricultural mapping
- Data visualization for agricultural insights
Limitations & Considerations
Research experience from IITA placement. Exact project title, outputs, and institutional attribution require confirmation before detailed presentation.
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