Narisetti Narendra, Henke Michael, Neumann Kerstin, Stolzenburg Frieder, Altmann Thomas, Gladilin Evgeny
Molecular Genetics, Leibniz Institute for Plant Genetics and Crops (IPK), Seeland, Germany.
Automation and Computer Sciences Department, Harz University of Applied Sciences, Wernigerode, Germany.
Front Plant Sci. 2022 Jul 13;13:906410. doi: 10.3389/fpls.2022.906410. eCollection 2022.
Automated analysis of large image data is highly demanded in high-throughput plant phenotyping. Due to large variability in optical plant appearance and experimental setups, advanced machine and deep learning techniques are required for automated detection and segmentation of plant structures in complex optical scenes.
Here, we present a GUI-based software tool (DeepShoot) for efficient, fully automated segmentation and quantitative analysis of greenhouse-grown shoots which is based on pre-trained U-net deep learning models of arabidopsis, maize, and wheat plant appearance in different rotational side- and top-views.
Our experimental results show that the developed algorithmic framework performs automated segmentation of side- and top-view images of different shoots acquired at different developmental stages using different phenotyping facilities with an average accuracy of more than 90% and outperforms shallow as well as conventional and encoder backbone networks in cross-validation tests with respect to both precision and performance time.
The DeepShoot tool presented in this study provides an efficient solution for automated segmentation and phenotypic characterization of greenhouse-grown plant shoots suitable also for end-users without advanced IT skills. Primarily trained on images of three selected plants, this tool can be applied to images of other plant species exhibiting similar optical properties.
高通量植物表型分析对大图像数据的自动化分析有很高要求。由于植物外观和实验设置的光学特性差异很大，在复杂光学场景中对植物结构进行自动检测和分割需要先进的机器和深度学习技术。
在此，我们展示了一种基于图形用户界面（GUI）的软件工具（DeepShoot），用于对温室种植的嫩枝进行高效、全自动分割和定量分析，该工具基于拟南芥、玉米和小麦在不同旋转侧视图和顶视图下的预训练U-net深度学习模型。
我们的实验结果表明，所开发的算法框架能够对在不同发育阶段使用不同表型分析设备获取的不同嫩枝的侧视图和顶视图图像进行自动分割，平均准确率超过90%，并且在交叉验证测试中，在精度和性能时间方面均优于浅层以及传统和编码器骨干网络。
本研究中提出的DeepShoot工具为温室种植的植物嫩枝的自动分割和表型特征分析提供了一种高效解决方案，也适用于没有先进信息技术技能的终端用户。该工具主要在三种选定植物的图像上进行训练，可应用于具有相似光学特性的其他植物物种的图像。
