Crop monitoring system utilizing IoT, AI and other tech showcased at ASABE
Reports and Proceedings
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 29-Dec-2025 22:12 ET (30-Dec-2025 03:12 GMT/UTC)
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.
A team from the Hebrew University of Jerusalem has developed a low-cost, non-invasive method to estimate total leaf area in dwarf tomato plants using 3D reconstruction from standard video footage. The study applies structure-from-motion techniques and machine learning to predict plant growth with remarkable accuracy. This innovative approach eliminates the need for expensive sensors or destructive sampling, making precision agriculture more accessible. The method holds promise for scaling crop monitoring across greenhouses and open fields alike.
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.