Wheat landraces identification through glumes image analysis

Grillo, Oscar; Blangiforti, Sebastiano; Venora, Gianfranco

doi:10.1016/j.compag.2017.07.024

“…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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“…Grillo et al [17] developed a method for the wheat variety identification using glumes size, shape, color and texture characteristics obtained from image analysis. Moreover, morphometry of maize ears by analyzing digital images has been implemented by Makanza et al [18].…”

Section: Introductionmentioning

confidence: 99%

Morphometry of the Wheat Spike by Analyzing 2D Images

Genaev¹,

Komyshev²,

Smirnov³

et al. 2019

Preprint

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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.

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“…To know the state-ofthe-art in automation of such activities in agriculture field, a survey has been made and the following papers have been cited during the literature survey to understand the different applications of computer vision in allied areas of the present work carried out. (Grillo et al 2017) presented an image analysis method to identify 52 different wheat varieties using 138 morphocolorimetic quantitative variables extracted from the digital images of glumes. The average identification accuracy of 89.7% was obtained using the Linear Discriminant Analysis classifier.…”

Section: Introductionmentioning

confidence: 99%

Automated Paddy Variety Recognition from Color-Related Plant Agro-Morphological Characteristics

Anami¹,

Naveen²,

Surendra³

2019

IJIGSP

8

0

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The paper presents an image-based paddy plant variety recognition system to recognize 15 different paddy plant varieties using 18 color-related agromorphological characteristics. The k-means color clustering method has been used to segment the target regions in the paddy plant images. The RGB, HSI and YC b C r color models have been employed to construct color feature vectors from the segmented images and the feature vectors are reduced using Principal Component Analysis (PCA) technique. The reduced color feature vectors are used as input to back propagation neural network (BPNN) and support vector machine (SVM). The set of six combined agro-morphological characteristics recorded during maturity growth stage has given the highest average paddy plant variety recognition accuracies of 91.20% and 86.33% using the BPNN and SVM classifiers respectively. The work finds application in developing a tool for assisting botanists, Rice scientists, plant breeders, and certification agencies.

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Wheat landraces identification through glumes image analysis

Cited by 25 publications

References 31 publications

Morphometry of the Wheat Spike by Analyzing 2D Images

Morphometry of the Wheat Spike by Analyzing 2D Images

Morphometry of the Wheat Spike by Analyzing 2D Images

Automated Paddy Variety Recognition from Color-Related Plant Agro-Morphological Characteristics

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