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

Minervini, Massimo; Scharr, Hanno; Tsaftaris, Sotirios A.

doi:10.1109/msp.2015.2405111

Cited by 213 publications

(128 citation statements)

References 14 publications

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“…In fact, most experts now agree that lack of reliable and automated algorithms to extract fine-grained information from these vast datasets forms a new bottleneck in our understanding of plant biology and function [39]. We must accelerate the development and deployment of such computer vision algorithms, since according to the Food and Agriculture Organization of the United Nations (FAO), large-scale experiments in plant phenotyping are a key factor in meeting agricultural needs of the future, one of which is increasing crop yield for feeding 11 billion people by 2050.…”

Section: Introductionmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

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Image-based plant phenotyping is a growing application domain of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first of its kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge (LSC) of the Computer Vision Problems in Plant Phenotyping (CVPPP) workshop in 2014. Four methods are presented: three segment leaves via processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be achieved with satisfactory accuracy (>90% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Besides, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (http://www.plantphenotyping.org/CVPPP2014-dataset) to support future challenges beyond segmentation within this application domain.

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Section: Introductionmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

Self Cite

234

181

View full text Add to dashboard Cite

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“…Both of these approaches will be instrumental for increasing the rate of crop improvement, and both approaches are facilitated by advances in image-based phenotyping; multiple plant measurements can be acquired rapidly from images, and data from image-based phenotyping approaches also can inform performance prediction (Spalding and Miller, 2013;Pound et al, 2014). As such, the development of image-based phenotyping platforms for agriculturally important plant species is a high priority for plant biology and crop improvement (Minervini et al, 2015).…”

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confidence: 99%

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

2016

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Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits.

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“…Best imaging practices for given biological questions such as plant growth [1], plant architecture [2], pathogen detection [3,4] and plant physiology [5] are now identified and often presented under the form of review articles [6,7]. The new bottleneck for these questions is now moving in the direction of image processing in order to efficiently and automatically extract quantitative phenotypic traits from the acquired data [8]. In this perspective, emerging works exploit advanced machine learning techniques to automatically determine the best feature spaces enabling one to address given informational tasks [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition

et al. 2018

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Seed imbibition is a very important process in plant biology by which, thanks to a simple water income, a dry seed may turn into a developing organism. In natural conditions, this process occurs in the soil, e.g., with difficult access for a direct observation. Monitoring the seed imbibition with non-invasive imaging techniques is therefore an important and possibly challenging task if one tries to perform it in natural conditions. In this report, we describe a set of four different imaging techniques that enable to addressing this task either in 3D or in 2D. For each technique, the following items are proposed. A detailed experimental protocol is provided to acquire images of the imbibition process. With the illustration of real data, the significance of the physical quantities measured in terms of their relation to the income of water in the seed is presented. Complete image analysis pipelines are then proposed to extract dynamic information on the imbibition process from such monitoring experiments. A final discussion compares the advantages and current limitations of each technique in addition to elements concerning the associated throughput and cost. These are criteria especially relevant in the field of plant phenotyping where large populations of plants are imaged to produce quantitatively significative traits after image processing.

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

Cited by 213 publications

References 14 publications

Leaf segmentation in plant phenotyping: a collation study

Leaf segmentation in plant phenotyping: a collation study

3D sorghum reconstructions from depth images identify QTL regulating shoot architecture

Evaluation of 3D/2D Imaging and Image Processing Techniques for the Monitoring of Seed Imbibition

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