Today’s session in our Satellite Data for Agricultural Economists course focused on building a spatial machine learning model using the new Google Embeddings dataset to map tea plantations in Kenya. Participants learned how to generate embeddings in Google Earth Engine, prepare training data, train multi-method ensemble models in R with the sdm package, create binary presence/absence maps, and estimate total tea area—highlighting how high-dimensional embeddings can improve crop segmentation compared to traditional spectral bands.

A Logistic Regression Approach is used to investigate which physiological and demographic variables significantly influence diabetes status.

Analisis pada project ini dilakukan secara terstruktur mengikuti tahapan Data Mining/CRISP-DM, dimulai dari Business Understanding, Data Cleaning, Exploratory Data Analysis, Data Preparation, Modeling, hingga Model Evaluation. Setiap tahap memiliki tujuan yang jelas dan saling melengkapi untuk menghasilkan pemahaman mendalam terhadap pola transaksi dan faktor-faktor yang memengaruhi pendapatan (Revenue).

Lucy

In today’s Satellite Data for Agricultural Economists session, we applied a pre-trained spatial machine learning model to map tea plantations in a different year—using Google Earth Engine composites, Area of Applicability diagnostics, and ensemble predictions to assess temporal transferability and quantify changes in tea area between 2020 and 2025.

Today’s session in our Satellite Data for Agricultural Economists course walked participants through building a spatial machine learning model in R to map tea plantations using Sentinel-2 imagery, training data, and ensemble methods for robust, high-resolution crop classification.

Three models were developed to predict monthly energy cost using building characteristics, occupancy, customer type, and regional information. A multiple linear regression (MLR), a Random Forest (RF) model, and an Extreme Gradient Boosting (XGBoost) model. Model performance was evaluated using the Root Mean Square Error (RMSE) and the Coefficient of Determination (R²)

Teaching a hands-on module on Satellite Data for Agricultural Economists, covering Sentinel-2 preprocessing, cloud masking, canopy height integration, and building machine-learning training data in Google Earth Engine. Practical, coding-oriented, and focused on real agricultural applications.