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Welcome to the Smart Droplets Knowledge Base!

Here you will find the Smart Droplet Academy's resources available on-demand.

The Smart Droplets Academy is an initiative designed to provide education and training on cutting-edge technologies with applications in the agricultural sector. The Academy is part of the Smart Droplets project, which aims to develop an intelligent crop spraying system.

The Academy offers a variety of learning opportunities that cover topics such as:

The Knowledge Base is divided into dedicated sections of seminars organised by each Smart Droplets partner. These sections feature a wide array of educational resources, including webinars, presentations, case studies, code notebooks, and other materials. Each partner brings unique expertise, allowing participants to explore innovations and technologies tailored to the agricultural sector.

The webinars, workshops, and seminars are designed for a broad audience, including students, farmers, researchers, and IT professionals. The instructors are experts from leading universities and research institutions.

The Academy’s goal is to empower stakeholders in the agri-food sector with the knowledge and skills they need to adopt new technologies, improve agricultural practices, and contribute to building a more sustainable and efficient future for farming.

Below, you’ll find resources for the Academy courses, including short descriptions of each course, video lectures, presentations, and code notebooks with the code examples provided in the lectures.

This series of lectures explores the application of AI in agricultural modeling, with a specific focus on reinforcement learning and digital twins. We delve into how AI can be integrated with traditional process-based models to enhance decision-making and predictive capabilities.

Key topics include:

  • Digital Twins in Agriculture: Creating virtual representations of real-world agricultural systems to simulate and optimize various scenarios.
  • Crop Growth Modeling with AI: Utilizing AI to improve the accuracy and precision of crop growth models, leading to better yield predictions and resource management.
  • Reinforcement Learning for Agricultural Decision Making: Implementing reinforcement learning algorithms to learn optimal decision-making policies for agricultural tasks such as fertilization.