Plant Phenotyping Through Image Analysis Using Nature Inspired Optimization Techniques

2018 ◽  
pp. 165-187
Author(s):  
S. Lakshmi ◽  
R. Sivakumar
Keyword(s):  
Image Analysis ◽  
Optimization Techniques ◽  
Plant Phenotyping

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 PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Special issue on computer vision and image analysis in plant phenotyping

2016 ◽  
Vol 27 (5) ◽  
pp. 607-609 ◽  
Author(s):  
Hanno Scharr ◽  
Hannah Dee ◽  
Andrew P. French ◽  
Sotirios A. Tsaftaris
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Plant Phenotyping ◽  
Special Issue

scholarly journals Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1287
Author(s):  
Cody S. Bekkering ◽  
Jin Huang ◽  
Li Tian
Keyword(s):  
Image Analysis ◽  
Basic Research ◽  
Wheat Breeding ◽  
Plant Phenotyping ◽  
World Population ◽  
Seed Traits ◽  
Phenotypic Information ◽  
Wheat Improvement ◽  
Organ Level ◽  
And Performance

Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.

scholarly journals AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Plant Methods ◽  
2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Michael P. Pound ◽  
Susan Fozard ◽  
Mercedes Torres Torres ◽  
Brian G. Forde ◽  
Andrew P. French
Keyword(s):  
Image Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Analysis Approach ◽  
Plant Phenotyping

scholarly journals HTPheno: An image analysis pipeline for high-throughput plant phenotyping

2011 ◽  
Vol 12 (1) ◽  
pp. 148 ◽  
Author(s):  
Anja Hartmann ◽  
Tobias Czauderna ◽  
Roberto Hoffmann ◽  
Nils Stein ◽  
Falk Schreiber
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Analysis Pipeline

scholarly journals Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]

2015 ◽  
Vol 32 (4) ◽  
pp. 126-131 ◽  
Author(s):  
Massimo Minervini ◽  
Hanno Scharr ◽  
Sotirios A. Tsaftaris
Keyword(s):  
Image Analysis ◽  
Plant Phenotyping

scholarly journals Growth of pineapple plantlets during acclimatisation can be monitored through automated image analysis of the canopy

2020 ◽  
Vol 4 (4) ◽  
pp. 223-229
Author(s):  
Guillermo Soto ◽  
Gustavo Lorente ◽  
Jessica Mendoza ◽  
Evelio Dany Báez ◽  
Carlos Manuel Lorenzo ◽  
...  
Keyword(s):  
Image Analysis ◽  
Automated Image Analysis ◽  
Plant Phenotyping ◽  
Tropical Fruit ◽  
Planting Material ◽  
Non Destructive Evaluation ◽  
Plantlet Growth ◽  
Canopy Area ◽  
Mathematical Relationships ◽  
Non Destructive

AbstractPineapple is an economically important tropical fruit crop, but the lack of adequate planting material limits its productivity. A range of micropropagation protocols has been developed over the years to address this shortfall. Still, the final stage of micropropagation, i.e. acclimatisation, remains a challenge as pineapple plantlets grow very slowly. Several studies have been conducted focusing on this phase and attempting to improve plantlet growth and establishment, which requires tools for the non-destructive evaluation of growth during acclimatisation. This report describes the use of semi-automated and automated image analysis to quantify canopy growth of pineapple plantlets, during five months of acclimatisation. The canopy area progressively increased during acclimatisation, particularly after 90 days. Regression analyses were performed to determine the relationships between the automated image analysis and morphological indicators of growth. The mathematical relationships between estimations of the canopy area and the fresh and dry weights of intact plantlets, middle-aged leaves (D leaves) and roots showed determination coefficients (R2) between 0.84 and 0.92. We propose an appropriate tool for the simple, objective and non-destructive evaluation of pineapple plantlets growth, which can be generally applied for plant phenotyping, to reduce costs and develop streamlined pipelines for the assessment of plant growth.

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