In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction.

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RootNav 2.0：用于复杂植物根结构自动导航的深度学习

近年来，根系生长的定量分析作为探索高温和干旱等非生物胁迫对植物吸收水分和养分能力的影响的一种方法变得越来越重要。从图像中分割和提取植物根部的特征对计算机视觉提出了重大挑战。根图像包含复杂的结构、大小变化、背景、遮挡、杂乱和光照条件的变化。我们提出了一种新的图像分析方法，可以在不同的成像设置中从一系列植物物种中全自动提取复杂的根系结构。在现代深度学习方法的推动下，RootNav 2.0 用极深的多任务卷积神经网络架构取代了以前的手动和半自动特征提取。该网络还定位种子、一阶和二阶根尖，以驱动搜索算法在整个图像中寻找最佳路径，无需用户交互即可提取准确的架构。