Dataset Information

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

ABSTRACT: Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and is presumed to function during leaf rolling, thereby reducing water loss during temperature extremes and drought. In this study, epidermal leaf impressions were collected from a genetically and anatomically diverse population of maize inbred lines. Subsequently, convolutional neural networks were employed to measure microscopic, bulliform cell-patterning phenotypes in high-throughput. A genome-wide association study, combined with RNAseq analyses of the bulliform cell ontogenic zone, identified candidate regulatory genes affecting bulliform cell column number and cell width. This study is the first to combine machine learning approaches, transcriptomics, and genomics to study bulliform cell patterning, and the first to utilize natural variation to investigate the genetic architecture of this microscopic trait. In addition, this study provides insight toward the improvement of macroscopic traits such as drought resistance and plant architecture in an agronomically important crop plant.

SUBMITTER: Qiao P

PROVIDER: S-EPMC6893188 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

Qiao Pengfei P Lin Meng M Vasquez Miguel M Matschi Susanne S Chamness James J Baseggio Matheus M Smith Laurie G LG Sabuncu Mert R MR Gore Michael A MA Scanlon Michael J MJ

G3 (Bethesda, Md.) 20191203 12

Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and is presumed to function during leaf rolling, thereby reducing water loss during temperature extremes and drought. In this study, epidermal leaf impressions were collected from a genetically and anatomically diverse population of maize inbred lines. S ...[more]

PMID: 31645422

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Project description:During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.

Project description:BackgroundMaize (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.ResultsOn 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.ConclusionThe 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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Dataset Information

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

Publications

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

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