Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping

Komyshev, Evgenii G.; Genaev, Mikhail A.; Afonnikov, D. A.

doi:10.3389/fpls.2016.01990

“…The efficiency of plant phenotyping can be increased by technologies of digital image analysis [10][11][12]. These technologies were applied for both kernel size and shape morphometry [13][14][15][16] and analysis of the spike traits [17][18][19][20].The methods for digital image analysis of spike characteristics are also developed and allow for solving of different problems. Grillo et al [17] developed a method for the wheat variety identification using glumes size, shape, color, and texture characteristics obtained from image analysis.…”

mentioning

confidence: 99%

Morphometry of the Wheat Spike by Analyzing 2D Images

Genaev

¹

,

Komyshev

²

,

Smirnov

³

et al. 2019

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Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.Agronomy 2019, 9, 390 2 of 22 the more so as the modern experiments involve tens of thousands of plants [5,9]. Correspondingly, automation of this laborious and time-consuming process is relevant for the science and breeding. The efficiency of plant phenotyping can be increased by technologies of digital image analysis [10][11][12]. These technologies were applied for both kernel size and shape morphometry [13][14][15][16] and analysis of the spike traits [17][18][19][20].The methods for digital image analysis of spike characteristics are also developed and allow for solving of different problems. Grillo et al. [17] developed a method for the wheat variety identification using glumes size, shape, color, and texture characteristics obtained from image analysis. Makanza et al. [18] designed the software allowing for determination of ear length and width as well as estimation of maize grain size and weight. Pound et al. [19] used deep learning to count wheat spikes and assess the number of spikelets per spike using images of wheat plants taken in glasshouse conditions. However, Pound et al. in their work did not estimate the morphological characteristics of spikes, such as their length, width, and type. As for the deep learning algorithms, their use, in turn, requires a large number of annotated spike images (several tens of thousands) to train the neural network parameters. Hughes et al. [20] determined wheat spike and grain morphometric parameters from X-ray micro computed tomography data. This method is highly accurate when determining the fine characteristics of spike and grain shapes but requires a special device for recording tomographic images. Kun et al. [21] proposed morphometry of wheat spikes via image processing. The authors utilized 2D images to assess various characteristics, such as spike length and awn number and length, and classified the spike shape type according to its length-to-width ratio. The spike parameters were used for their classification according t...

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“…1). The images were captured with a Canon EOS 50D camera (resolution, 4752 × 3168) according to the imaging protocol for the Seed-Counter application (Komyshev et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Analysis of images by SeedCounter. The tuber images were analyzed by a desktop variant of the SeedCounter application (Komyshev et al, 2017; http://wheatdb.org/ seedcounter), modified for recognition of potato tubers instead of wheat grains. In addition to the length, width, and projected area, the following indices of tuber contour shape were also computed (Cervantes et al, 2016): • Length to width ratio, L/W, which reflects one of the main tuber shape characteristics (Si et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Here, we describe the experiment on growing wild potato species under local vegetation conditions (Novosibirsk region, Russia) during the season typical for growing the cultivated potato. In order to accelerate and standardize the estimation of tuber formation and phenotype of the wild potato species, the produced yield was phenotyped by digital imaging and image processing with the help of the SeedCounter mobile application (Komyshev et al, 2017) adapted to analyzing the wild potato tubers.…”

Section: Introductionmentioning

confidence: 99%

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Image-based analysis of quantitative morphological characteristics of wild potato tubers using the desktop application SeedСounter

Иванова

¹

,

Komyshev

²

,

Genaev

³

et al. 2019

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The development of quantitative digital phenotyping methods for evaluation of wild potato (section Petota Dumort., genus Solanum L.) tuberization is required for annotation of genebank collections and selection of the suitable donor material for potato breeding. There are no available methods specifically designed for the quantitative analysis of wild potato tuber morphology. The current study is devoted to evaluation of wild potato tubers’ morphological characteristics using a digital image processing technique. For this purpose, the mobile application SeedSounter developed previously for grain analysis was specifically adapted for tuber phenotyping. The application estimates the number and shape of objects scattered on a standard sheet of white paper (i. e. A3 or A4). Twelve accessions from the VIR genebank collection belonging to nine Petota species were grown in pots protected with garden fabric during the growing season of cultivated potato (Novosibirsk region). Tubers were collected form plants of nine genotypes. Three genotypes did not produce tubers. The weight of tubers collected from each plant was measured. The tuber yield from each plant was analyzed using SeedCounter (http://wheatdb.org/seedcounter). The number of tubers per plant was counted; the following characteristics were extracted from the images of individual tubers: length, width, projected area, length to width ratio, сircularity, roundness, rugosity and solidity. One-way ANOVA showed a significant effect of genotype on all measured characteristics. A pairwise comparison of nine Petota accessions using all measured parameters revealed statistically significant differences between 86 % of pairs. The overall tuber yield volume for each plant was calculated as a sum of volumes of individual tubers; tuber volume was calculated from its length to width ratio and projected area. A strong correlation between the evaluated tuber yield volume and yield weight was shown. We propose tuber yield volume as a characteristic for a general evaluation of tuberization for wild potato, implementing the four-step scale from 0 to 3. According to this characteristic, the twelve wild potato accessions studied could be divided into four groups with different tuberization abilities. The evaluated tuberization ability is partially in accordance with previously obtained VIR data. The results presented demonstrate the possibility to use SeedCounter for wild potato collections phenotyping.

show abstract

“…The efficiency plant phenotyping can be increased by technologies of digital image analysis [10][11][12]. These technologies were applied both for kernel size and shape morphometry [13][14][15][16] and analysis of the spike traits [17][18][19][20].…”

mentioning

confidence: 99%

Morphometry of the Wheat Spike by Analyzing 2D Images

Genaev¹,

Komyshev²,

Smirnov³

et al. 2019

Preprint

Self Cite

0

View full text Add to dashboard Cite

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.

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Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping

Cited by 72 publications

References 33 publications

Morphometry of the Wheat Spike by Analyzing 2D Images

Morphometry of the Wheat Spike by Analyzing 2D Images

Image-based analysis of quantitative morphological characteristics of wild potato tubers using the desktop application SeedСounter

Morphometry of the Wheat Spike by Analyzing 2D Images

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