A research team has developed 3D-NOD, a spatiotemporal deep learning framework that leverages 3D point cloud data to identify new plant organs with exceptional precision.

A research team has developed VRoot, an immersive virtual reality (VR) platform that enables users to manually reconstruct root systems from MRI scans with greater precision and usability than traditional desktop tools.

A research team has developed a hybrid evaluation method that integrates a radiative transfer model with multidimensional imaging data, offering more precise monitoring of rice canopy traits linked to water stress.

A research team has developed an advanced deep learning model, LKNet, to improve the accuracy of rice panicle counting in dense crop canopies.

A research team has developed a cost-effective method to measure wheat plant height in unprecedented detail using drone-based cross-circling oblique (CCO) imaging and 3D canopy modeling.