Researchers at the University of Osaka have developed NeuraLeaf, a revolutionary CG model using deep learning to represent diverse plant species and their leaf deformations. This single model overcomes the limitations of traditional manual modeling by disentangling species-specific shapes from dynamic 3D deformations like wilting or curling. NeuraLeaf allows precise tracking of leaf changes, enhancing growth prediction, disease detection, and agricultural management. Presented at ICCV 2025, this technology promises to advance plant science and contribute to "PlantTwin," a project creating digital twins of plants.

We are thrilled to announce the launch of Agricultural Ecology and Environment (AEE), a new interdisciplinary journal dedicated to the sustainable future of global agriculture and planetary health. AEE brings together cutting-edge research on soil health, water quality, climate resilience, biodiversity, and more—bridging science, policy, and practice to tackle the complex challenges at the intersection of agriculture and ecology.

A research team indicates that treatments with ethylene and 1-methylcyclopropene (1-MCP), especially in combination, dramatically inhibit postharvest sprouting, enhance flavor and color, and extend shelf life.

A research team has developed a new artificial intelligence (AI) model that harnesses unmanned aerial vehicle (UAV) data to predict wheat yields with exceptional accuracy.

A research team has developed Spotibot, a deep learning–powered application available on both web and mobile platforms, designed to automatically detect and measure Botrytis lesions on rose petals.