Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

文献类型： 外文期刊

第一作者： Zhou, Shuo

作者： Zhou, Shuo;Chai, Xiujuan;Yang, Zixuan;Yang, Chenxue;Sun, Tan;Wang, Hongwu;Zhou, Shuo;Chai, Xiujuan;Sun, Tan

作者机构：

关键词： Maize phenotyping; Instance segmentation; Computer vision; Deep learning; Convolutional neural network

期刊名称：PLANT METHODS （ 影响因子：3.61； 五年影响因子：4.266 ）

ISSN：

年卷期： 2021 年 17 卷 1 期

页码：

收录情况： SCI

摘要： Background 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. Results 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. Conclusion 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.

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