Country : Guatemale
Client : Franz Hentze, Pantaleon
This project focused on improving the DRIS (Diagnosis and Recommendation Integrated System) calculations provided by the client by applying low-level machine learning algorithms informed by recent academic research.
The goal was to enhance the accuracy and adaptability of nutrient analysis by leveraging real-world plant and soil data, allowing the system to better reflect field conditions and variability.
The output was an automated fertilizer recommendation engine capable of dynamically suggesting both type and quantity of fertilizer based on interpreted crop health indicators and soil nutrient profiles, streamlining decision-making for precision agriculture.