Plant Segmentation

Plant segmentation, the automated identification and delineation of plant structures in images or point clouds, aims to accelerate plant phenotyping and improve agricultural practices. Current research heavily utilizes deep learning, employing convolutional neural networks (like U-Net) and transformer architectures, often enhanced with techniques like efficient self-attention mechanisms, to achieve accurate segmentation across diverse plant species and imaging modalities (e.g., multispectral imagery, point clouds from laser scanning, minirhizotron images). These advancements enable applications ranging from disease detection and precision agriculture to urban vegetation mapping and root system analysis, ultimately contributing to more efficient and sustainable plant breeding and resource management.