This study explores the integration of AI, particularly large language models (LLMs) like ChatGPT, into educational settings, focusing on the implications for teaching and learning. Through interviews with course coordinators from data science courses at Wageningen University, this research identifies both the benefits and challenges associated with AI in the classroom. While AI tools can streamline tasks and enhance learning, concerns arise regarding students' overreliance on these technologies, potentially hindering the development of essential cognitive and problem solving skills. The study highlights the importance of responsible AI usage, ethical considerations, and the need for adapting assessment methods to ensure educational outcomes are met. With careful integration, AI can be a valuable asset in education, provided it is used to complement rather than replace fundamental learning processes.

EPFL scientists have developed METEOR – an application that can train algorithms to recognize new objects after being shown just a handful of images. Images taken by drones and satellites give scientists a wealth of information. These snapshots provide crucial insight into the changes taking place on the Earth's surface, such as in animal populations, vegetation, debris floating on the ocean surface and glacier coverage. In addition, experts can train neural networks to sort through the images at dizzying speed and spot and classify individual objects. "However, none of the AI programs currently available can immediately switch from recognizing one type of object to another – like from debris to a tree or building," says Professor Devis Tuia, the head of EPFL's Environmental Computational Science and Earth Observation Laboratory.

Our society relies heavily on plastic products and the amount of plastic waste is expected to increase in the future. If not properly discarded or recycled, much of it accumulates in rivers and lakes. Eventually it will flow into the oceans, where it can form aggregations of marine debris together with natural materials like driftwood and algae. A new study from Wageningen University and EPFL researchers, recently published in Cell iScience, has developed an artificial intelligence-based detector that estimates the probability of marine debris shown in satellite images. This could help to systematically remove plastic litter from the oceans with ships.

Five teams from the Netherlands, South Korea and China have advanced to the final stage of a competition to see who can grow fresh tomatoes in greenhouses remotely using artificial intelligence. The second Autonomous Greenhouse Challenge, which is organised by Dutch academic powerhouse Wageningen University & Research (WUR) and Chinese multinational conglomerate Tencent, began in September with a 24-hour hackathon involving 21 international teams and more than 200 participants from 26 countries. The five winning teams – Netherlands-based AiCU, The Automators and Automatoes, China'sIUA.CAAS and Korea'sDigilog – will each be given six months' access to a real greenhouse in the Dutch town of Bleiswijk, where from December onwards they will attempt to control and produce a tomato crop from afar by employing AI algorithms to keep inputs like water, nutrients and energy at sustainable levels. September's hackathon, held at WUR, saw an international jury award points to each team based on their composition and competence, their application of AI technology and the net profit they made during a virtual tomato production game. During their pitches, the teamswere given access to a climate model and a tomato crop growth model previously developed by researchers at WUR.

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.

Evaluating and quantifying the beauty of a landscape, an ecosystem and its effects on a person's well-being has become a central issue for public authorities. With this in mind, scientists from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and Wageningen University in the Netherlands have developed a new indicator based on deep learning and several million photos posted on the social network Flickr. An article was recently published in Nature Scientific Reports. When we walk in nature, whether in the mountains, in a forest or by the sea, we feel things, a certain well-being. Numerous studies have highlighted the benefits of such activities for our health, both physical and mental.

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.

Wageningen University and Research in the Netherlands is digging even deeper into how artificial intelligence can be integrated into greenhouse production. A new research project focuses on whether it might be possible to predict cucumber harvest, and what information artificial intelligence needs to make correct predictions. To answer this, the Business Unit Greenhouse Horticulture at Wageningen University & Research is working on the development of an AI yield prediction model and associated database. A greenhouse is a complex system with several components such as crop, climate, and irrigation set-up. As a result, the plant itself acts as a sensor of its own biological status and its environment.

Last summer, the Dutch national television made a small report on a large organic arable farm on the young sea clay in the province of Flevoland. Men and women were working in organic parsley, which was due to be harvested mechanically the next day. It still had to be manually stripped of weeds, such as polygonum. The question was whether field robots are already capable of taking over this unpleasant and labour-intensive (i.e. The farm's owner shares his thoughts about a robot taking over these chores in his fields in front of the camera.

Five teams from the Netherlands, South Korea and China have advanced to the final stage of a competition to see who can grow fresh tomatoes in greenhouses remotely using artificial intelligence. The second Autonomous Greenhouse Challenge, which is organised by Dutch academic powerhouse Wageningen University & Research (WUR) and Chinese multinational conglomerate Tencent, began in September with a 24-hour hackathon involving 21 international teams and more than 200 participants from 26 countries. The five winning teams – Netherlands-based AiCU, The Automators and Automatoes, Korea's IUA.CAAS and China's Digilog – will each be given six months' access to a real greenhouse in the Dutch town of Bleiswijk, where from December onwards they will attempt to control and produce a tomato crop from afar by employing AI algorithms to keep inputs like water, nutrients and energy at sustainable levels. September's hackathon, held at WUR, saw an international jury award points to each team based on their composition and competence, their application of AI technology and the net profit they made during a virtual tomato production game. During their pitches, the teams were given access to a climate model and a tomato crop growth model previously developed by researchers at WUR.