Zhou Shuo, Chai Xiujuan, Yang Zixuan, Wang Hongwu, Yang Chenxue, Sun Tan
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St., Beijing, 100081, China.
Key Laboratory of Big Agri-Data, Ministry of Agriculture, Beijing, China.
Plant Methods. 2021 Apr 29;17(1):48. doi: 10.1186/s13007-021-00747-0.
Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets.
On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625.
The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
玉米（Zea mays L.）是世界上最重要的粮食来源之一，几个世纪以来一直是植物遗传学和表型研究的主要对象。观察和分析玉米生长过程中的各种形态表型特征对于遗传和育种研究至关重要。通常大量的样本会产生海量的高分辨率图像数据。虽然高通量植物表型分析平台在玉米育种试验中越来越多地被使用，但合理需要能够自动识别玉米植株视觉表型特征并对图像数据集进行批量处理的软件工具。
在计算机视觉和植物科学的交叉领域，我们利用基于卷积神经网络的先进深度学习方法来助力玉米表型分析工作流程。本文介绍了Maize-IAS（玉米图像分析软件），这是一款支持一键分析玉米表型的集成应用程序，嵌入了多种功能：（I）投影，（II）颜色分析，（III）节间长度，（IV）高度，（V）茎直径和（VI）叶片计数。以玉米的RGB图像作为输入，该软件提供了用户友好的图形交互界面，并能快速计算多个重要的表型特征，包括叶鞘点检测和叶片分割。在叶片计数功能中，预测值与真实值之间差异的均值和标准差分别为1.60和1.625。
Maize-IAS易于使用，既不需要计算机视觉专业知识也不需要深度学习专业知识。它整合了所有批量处理功能，能够在大型数据集上实现对玉米生长性状的记录、测量和定量分析的自动化及减少人工的任务。我们证明了我们的技术和软件在基于图像的植物研究中的效率和潜在能力，这也展示了人工智能技术在农业和植物科学中应用的可行性和能力。
