Researchers from South Dakota State University presented a high-tech system to help farmers optimize crop yields while lowering costs at the 2025 annual meeting of the American Society of Agricultural and Biological Engineers. The system tracks and analyzes crop development through data collected by sensors, biosensors, the Internet of Things and AI.

The Europlanet Science Congress 2025 will be held jointly with the annual meeting of the American Astronomical Society’s Division of Planetary Science (EPSC-DPS2025) from 7–12 September 2025 at Finlandia Hall, Helsinki, Finland. EPSC-DPS2025 covers the full spectrum of planetary research and technology across 74 scientific sessions, with topics including the latest results on Solar System, exoplanetary and interstellar bodies, current and upcoming missions, ground-based observations, the use of artificial intelligence and machine learning in planetary science, planetary defence, and the emergence of life in our Solar System and beyond. Around 1900 oral and poster presentations have been submitted and almost 2000 planetary scientists from Europe and around the world are expected to attend the conference.

Decoding cosmic evolution depends on accurately predicting the complex chemical reactions in the harsh environment of space. Traditional methods for such predictions rely heavily on costly laboratory experiments or expert knowledge, both of which are resource-intensive and limited in scope. Recently, a research team developed an innovative AI tool that predicts astrochemical reactions with high accuracy and efficiency, demonstrating that deep learning techniques can successfully address data limitations in astrochemistry. Titled “A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions,” this research was published May 15 in Intelligent Computing, a Science Partner Journal.

Large surface waves produced by powerboats are a mainstay for recreational watersports. A new study from the University of Minnesota Twin Cities shows that beneath the surface, factors such as propeller thrust and other types of waves can impact delicate lakebed ecosystems.

While Japan is renowned among mountaineers for its challenging mountain terrain, it is also known for its high number of mountaineering accidents. Therefore, researchers developed a deep learning model that uses contextual information such as time of day, weather, mountain characteristics, and climbers’ details to accurately identify and predict four major categories of climbing accidents. This enables climbers and rescue teams to be better informed and prepared with safety measures at the planning stage. While Japan is renowned among mountaineers for its challenging mountain terrain, it is also known for its high number of mountaineering accidents. Therefore, researchers developed a deep learning model that uses contextual information such as time of day, weather, mountain characteristics, and climbers’ details to accurately identify and predict four major categories of climbing accidents. This enables climbers and rescue teams to be better informed and prepared with safety measures at the planning stage.