Growing lettuce with artificial intelligence (AI) in autonomous greenhouses, by algorithms developed in different parts of the world: today the young lettuce plants of the five international teams that compete in the two final rounds of the Autonomous Greenhouse Challenge were planted in the experimental greenhouses of Wageningen University & Research in Bleiswijk. The goal is to grow these lettuces fully autonomously with an AI algorithm on a cloud platform with good quality and little resource and energy use and without any human interference. The competition and teams' performance can be followed live on an online dashboard. Will the computer be able to complete a fully autonomous growing cycle? Five international teams located around the world will produce a lettuce crop using a fully autonomous algorithm during two growing cycles.

Scientists at EPFL, the Royal Netherlands Institute for Sea Research and Wageningen University & Research have developed a new deep-learning model for counting the number of seals in aerial photos that is considerably faster than doing it by hand. With this new method, valuable time and resources could be saved which can be used to further study and protect endangered species. Ecologists have been monitoring seal populations for decades, building up vast libraries of aerial photos in the process. Counting the number of seals in these photos require hours of meticulous work to manually identify the animals in each image. A cross-disciplinary team of researchers including Jeroen Hoekendijk, a PhD student at Wageningen University & Research (WUR) and employed by the Royal Netherlands Institute for Sea Research (NIOZ), and Devis Tuia, an associate professor and head of the Environmental Computational Science and Earth Observation Laboratory at EPFL Valais, have come up with a more efficient approach to count objects in ecological surveys.

The main goal of the Digital Future Farm project is to create a "digital twin" of arable and dairy farms able to mimic farm process interactions, and allow for the exploration of interventions. To achieve this aim, the project will pull together multifaceted, multiscale data (from remote sensing, IoT sensors, farm-management systems) and process-based models in a common infrastructure. Part of the project ambition is to investigate to what extent machine learning and deep learning models can improve decision-making at a farm-level. Using real and simulated data you will be challenged to design and implement machine learning pipelines for estimating farm yields and nutrients application, and evaluate their performance using the Digital Future Farm project case studies. Your overarching ambition will be a methodological contribution in the area of machine learning applications for farm-level decision making.