<ul><li>The AgroLens project aims to develop Machine Learning-based methodologies for predicting soil nutrient levels without relying on laboratory tests.</li><li>The project utilizes the LUCAS Soil dataset and Sentinel-2 satellite imagery to estimate key soil properties including phosphorus, potassium, nitrogen, and pH levels.</li><li>Supplementary features like weather data, harvest rates, and Clay AI-generated embeddings are integrated to enhance the soil nutrient prediction model.</li><li>Implementation of advanced algorithms such as Random Forests, Extreme Gradient Boosting (XGBoost), and Fully Connected Neural Networks (FCNN) results in robust model performance and accurate nutrient prediction.</li></ul>

Machine Learning Models for Soil Parameter Prediction Based on Satellite, Weather, Clay and Yield Data

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