DeepCrop🛰️🌾🌽

We are creating an AI system for nation-wide agricultural production monitoring based on crowd-sourced and remote-sensing.

My role

I am the Director of this project, which is a collaboration between the University of Chile, (the Centre of Artificial Intelligence)[https://www.cenia.cl], (the Center of Natural Ressources)[https://www.ciren.cl] as principal institutions, and the Technical University of Munich, the European Commission’s Joint Research Center, and the École Polytechnique Fédérale de Lausanne.

This is a 4 years Tecnologia Avanzada project funded to the tune of 660,000,000 CLP¹ coming as grant from the National Research and Development Agency (ANID).

The project

This project aims to develop a crop map (like land-use but for crop i.e., which crop are cultivated where) at the country-level. To this aim we will leverage the capacity of general purpose model that we will trained over Chile. This is quite fun as Chile is a very long country with many different climates, making it the perfect place to test a model claiming to be general.

The objectives are:

• Gathering existing general crop data at the polygon- and pixel-level from open-source and in-house datasets
• Collecting Chilean crop data at the polygon- and pixel-level, including yield
• Implementing a parcel delineation model, with a polygon-level crop classifier
• Pre-training a large vision model on Worldwide, South American, and Chilean multimodal and multiresolution data
• Train the model to learn to learn various tasks using meta-learning algorithms
• Implement a dashboard using the model’s predictions