scholarly journals High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis

Plant Methods ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Céline Rousseau ◽  
Etienne Belin ◽  
Edouard Bove ◽  
David Rousseau ◽  
Frédéric Fabre ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chlorophyll Fluorescence ◽  
High Throughput ◽  
Plant Resistance ◽  
Fluorescence Image

scholarly journals High-Throughput Corn Image Segmentation and Trait Extraction Using Chlorophyll Fluorescence Images

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Augusto Souza ◽  
Yang Yang
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Chlorophyll Fluorescence ◽  
High Throughput ◽  
Morphological Characteristics ◽  
Plant Phenotyping ◽  
Proof Of Concept ◽  
Corn Plant ◽  
Fluorescence Images ◽  
Corn Plants

Plant segmentation and trait extraction for individual organs are two of the key challenges in high-throughput phenotyping (HTP) operations. To address this challenge, the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University utilizes chlorophyll fluorescence images (CFIs) to enable consistent and efficient automatic segmentation of plants of different species, age, or color. A series of image analysis routines were also developed to facilitate the quantitative measurements of key corn plant traits. A proof-of-concept experiment was conducted to demonstrate the utility of the extracted traits in assessing drought stress reaction of corn plants. The image analysis routines successfully measured several corn morphological characteristics for different sizes such as plant height, area, top-node height and diameter, number of leaves, leaf area, and angle in relation to the stem. Data from the proof-of-concept experiment showed how corn plants behaved when treated with different water regiments or grown in pot of different sizes. High-throughput image segmentation and analysis basing on a plant’s fluorescence image was proved to be efficient and reliable. Extracted trait on the segmented stem and leaves of a corn plant demonstrated the importance and utility of this kind of trait data in evaluating the performance of corn plant under stress. Data collected from corn plants grown in pots of different volumes showed the importance of using pot of standard size when conducting and reporting plant phenotyping data in a controlled-environment facility.

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

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract 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.

scholarly journals An LED-based multi-actinic illumination system for the high throughput study of photosynthetic light responses

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5589
Author(s):  
João Serôdio ◽  
William Schmidt ◽  
Jörg C. Frommlet ◽  
Gregor Christa ◽  
Matthew R. Nitschke
Keyword(s):  
Chlorophyll Fluorescence ◽  
High Throughput ◽  
Light Stress ◽  
Limiting Factor ◽  
Phenotypic Traits ◽  
Light Responses ◽  
Illumination System ◽  
Digital Manipulation ◽  
Multiple Samples

The responses of photosynthetic organisms to light stress are of interest for both fundamental and applied research. Functional traits related to the photoinhibition, the light-induced loss of photosynthetic efficiency, are particularly interesting as this process is a key limiting factor of photosynthetic productivity in algae and plants. The quantitative characterization of light responses is often time-consuming and calls for cost-effective high throughput approaches that enable the fast screening of multiple samples. Here we present a novel illumination system based on the concept of ‘multi-actinic imaging’ of in vivo chlorophyll fluorescence. The system is based on the combination of an array of individually addressable low power RGBW LEDs and custom-designed well plates, allowing for the independent illumination of 64 samples through the digital manipulation of both exposure duration and light intensity. The illumination system is inexpensive and easily fabricated, based on open source electronics, off-the-shelf components, and 3D-printed parts, and is optimized for imaging of chlorophyll fluorescence. The high-throughput potential of the system is illustrated by assessing the functional diversity in light responses of marine macroalgal species, through the fast and simultaneous determination of kinetic parameters characterizing the response to light stress of multiple samples. Although the presented illumination system was primarily designed for the measurement of phenotypic traits related to photosynthetic activity and photoinhibition, it can be potentially used for a number of alternative applications, including the measurement of chloroplast phototaxis and action spectra, or as the basis for microphotobioreactors.

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