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.

A research team has revealed how gritty stone cells form and expand in pear fruit flesh by using a cutting-edge imaging technique based on bioorthogonal click chemistry.