Plant and Soil Boron deficiency precisely identified on growth stage V4 of maize crop using texture image analisys. --Manuscript Draft-Manuscript Number: Full Title:
Boron deficiency precisely identified on growth stage V4 of maize crop using texture image analisys.
Article Type:
Manuscript
Keywords:
plant nutrition; nutrient solution; Zea mays L.; micronutrient; image analysis; pattern classification.
Corresponding Author:
Pedro Henrique de Cerqueira Luz, Ph.D. Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) - Universidade de São Paulo Pirassununga, São Paulo BRAZIL
Corresponding Author Secondary Information: Corresponding Author's Institution:
Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) - Universidade de São Paulo
Corresponding Author's Secondary Institution: First Author:
Pedro Henrique de Cerqueira Luz, Ph.D.
First Author Secondary Information: Order of Authors:
Pedro Henrique de Cerqueira Luz, Ph.D. Mário Antonio Marin, MSc. Fernanda de Fátima da Silva Devechio, MSc. Liliane Maria Romualdo, Ph.D. Celso Eduardo Bonafé Peres, MSc. Alvaro Manuel Gómez Zúñiga, MSc. Marcos William da Silva Oliveira, MSc. Valdo Rodrigues Herling, Ph.D. Odemir Martinez Bruno, Ph.D.
Order of Authors Secondary Information: Abstract:
Aims: The aim of this study was to evaluate the diagnostic imaging approach to identify the deficiency of boron (B) in leaves of maize grown under hydroponia in greenhouse. Methods: The experiment was conducted in a greenhouse under a hydroponic system in solution nutrition. The treatments were four levels of boron in solution nutrition (zero, 0.12, 0.24 and 0.60 mg L-1), combined with three growing stages of maize plant (V4, V7 and R1). Plant parts sampled included index leaf (IL) and new leaf (NL) to chemical analysis and texture image analysis. Our proposal is to aplly these texture analysis and pattern classification scheme to identify the different leves of B nutition. Results: The proposal achieved 98% of accucary when differentiating leaves properly fertilized and with any deficiency, in V4. In all tests with IL success rate was bigger than 80%, and around 90% for NL. Conclusions: The image analysis techiniques applied on leaves are able to efficiently identify boron deficiences in younger maize plants; and this showed to be efficient to automatic nutricional diagnosis.
Suggested Reviewers:
K Raja Reddy, Ph.D. Research Professor, Mississipi State University
[email protected]
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He is a research professor in the area of environmental control of plant growth and development, crop simulation modeling and applications, and remove sensing applications in natural resource management. Huan Y. Jiang, Ph.D. Professor Research, College of Biosystems Engineering and Food Science Zhejiang University - Hangzhou China
[email protected] Is inserted within the study area oh the manuscript. Jindong Wu, Ph.D. University of Minnesota
[email protected] Is inserted within the study area of the manuscript. A. Camargo, Ph.D. School of Computing - University of East Anglia, England UK
[email protected] Is inserted within the study area of manuscript. Maria Villamil, Ph.D. Assistant Professor, College of Agricultural, consumer and environmental Sciences University of Illinois at Urbana-Champaign
[email protected] Is a research professor in the area Bioinformatics and Statistics and crop sciences.
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Covering Letter
UNIVERSIDADE DE SÃO PAULO
Faculdade de Zootecnia e Engenharia de Alimentos
Pirassununga, March 19, 2015. Dear Editor
We are forwarding the manuscript "Boron deficiency precisely identified on growth stage V4 of maize crop using texture image analysis" This present study addresses a promising theme, considering that the use of pattern classification based on images analysis applied in agriculture has been used in recent years.
We thank you and we are at your disposal
Best regards,
Authors
Manuscript Click here to download Manuscript: Article_Boro.doc Click here to view linked References 1
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Boron deficiency precisely identified on growth stage V4 of maize crop using texture image
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analysis
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Pedro Henrique de Cerqueira Luz1,4, Mário Antonio Marin1,5, Fernanda de Fátima da Silva
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Devechio1,6, Liliane Maria Romualdo1,7, Celso Eduardo Bonafé Peres1,8, Alvaro Manuel
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Gómez Zuñiga2,9, Marcos William da Silva Oliveira2,10, Valdo Rodrigues Herling1,11, Odemir
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Martinez Bruno3,12.
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1
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(USP/FZEA), Pirassununga, SP, Brazil. 2Department of Physics, Scientific Computing Group, University of São
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Paulo – Institute of Physics (USP/IFSC), São Carlos, SP, Brazil,
Department of Animal Sciences, University of São Paulo – College of Animal Science and Food Engineering
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5
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Elite, FZEA/USP ,
[email protected],
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10
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of the FZEA/USP and IFSC/USP. It was supported by FAPESP.
14 15 16 17 18 19 20 21 22 23 24 25
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Corresponding author*
[email protected],
[email protected],
[email protected], Avenue Duque de Caxias Norte, 225, Jardim
[email protected],
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[email protected],
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[email protected],
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[email protected],
[email protected]. This study is part of project 2010/18233-3
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1
Key words: plant nutrition; nutrient solution; Zea mays L.; micronutrient; image analysis;
2
pattern classification.
3 4
Abstract
5
Aims: The aim of this study was to evaluate the diagnostic imaging approach to identify the
6
deficiency of boron (B) in leaves of maize grown under hydroponia in greenhouse. Methods:
7
The experiment was conducted in a greenhouse under a hydroponic system in solution
8
nutrition. The treatments were four levels of boron in solution nutrition (zero, 0.12, 0.24 and
9
0.60 mg L-1), combined with three growing stages of maize plant (V4, V7 and R1). Plant
10
parts sampled included index leaf (IL) and new leaf (NL) to chemical analysis and texture
11
image analysis. Our proposal is to aplly these texture analysis and pattern classification
12
scheme to identify the different leves of B nutition. Results: The proposal achieved 98% of
13
accucary when differentiating leaves properly fertilized and with any deficiency, in V4. In all
14
tests with IL success rate was bigger than 80%, and around 90% for NL. Conclusions: The
15
image analysis techiniques applied on leaves are able to efficiently identify boron deficiences
16
in younger maize plants; and this showed to be efficient to automatic nutricional diagnosis.
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Abbreviations: B, boron; IL, index leaf; NL, new leaf; LBP, Local Binary Pattern.
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1
Introduction
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Maize (Zea mays L.) is one of the most important food crops in the world composing
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the basis of animal nutrition, and it is widely used in industrial products, including biofuls
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(Shiferaw et al., 2011). It is a crop very demanding in terms of mineral nutrition (Fancelli,
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2008), among which, the boron (B) is one of the micronutrients limiting for their yield. In
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Brazil, the boron deficiency is the most common between the micronutrients, in both crops,
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in annual and perennial crops (Taiz and Zeiger, 2009) and it has also been reported in several
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continents (Bell and Dell, 2008).
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A feature that contributes to its importance in agricultural production is that B is
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essential for cell wall structure (Brown et al. 2002; Bonilla et al., 2009; Berry, 2010) and
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growth of plants, thus its proper supply is required to achieve high yield and crop quality
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(Tanaka and Fujiwara, 2007). In addition, its deficiency inhibits tissues growth, especially the
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reproductive structures in young portions and plant growth (Brown et al. 2002; Miwa and
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Fujiwara, 2010), leading to necrosis of young leaves and terminal buds, stiff and brittle stems
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and loss of apical dominance (Dell and Huang, 1997). Other problems caused such deficiency
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are the reducing of pollen grain viability (Lima Filho and Malavolta, 1998; Lordkaew et al.,
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2013) and changes in the level of transcription of a range of genes involved in various
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physiological processes (Camacho-Cristóbal et al., 2011).
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For maize crop, knowing exactly how to boron deficiency affects the functions of the
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reproductive organs and other plant parts, is useful to guide the management of B deficiency
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through its diagnosis (Lordkaew et al., 2011). Currently, the methods for identifying possible
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nutritional deficiencies in plants consist primarily of leaf tissue analysis and visual diagnosis
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(Prado et al., 2008; Aref, 2012). On the first case, time consuming and costly analytical
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methods are used to determine the nutrient content (Guimarães et al., 1999). Furthermore, this
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deficiency identification occurs in very advanced stage of development, and the information
4
1
may not be useful for deficiency correction in that production cycle (Wu et al., 2007).
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Otherwise, the visual diagnosis is not accurate because its limitation and error-prone once it
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depends on the examiner’s accuracy (Baesso et al., 2007). In most cases, it is only possible to
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diagnose visually such a deficiency when this occurs in its acute form, likely when significant
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part of yield is already compromised. In addition, deficiency symptoms of various nutrients
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are similar when in advanced stage of development, confusing diagnosis (Fontes, 2004).
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The difficulties of evaluating the nutritional status of in plants on the same crop cycle
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are the motivation to propose additional approaches in different nutrients. The different
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methods of computer vision are able to extract quantitative information from the images of
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the leaves, such as features related to color and texture, that can contribute to a more precise
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and careful analysis of the morphology and physiology of plants (Plotze and Bruno, 2009;
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Casanova, 2009; Rossatto, 2011). An example of using of digital images for nutritional
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analysis can be found in Baesso et al. (2007), were digital images analysis are used to detect
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nitrogen levels in bean plants. In other case, Lukina et al. (1999) estimates vegetation
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coverage in wheat using also digital images. Romualdo et al. (2014) and Silva et al. (2014)
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evaluated different methods for feature extraction in images of maize leaves at different stages
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of development of maize, grown in greenhouse under nutritional deficiency induced of
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nitrogen and magnesium, respectively. In both cases, the authors observed that feature
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extraction of texture and colors in the leaves, provided information which were possible to
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identify different levels of N and Mg deficiences in the initial stages of crop, which may
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contribute to possible corrections of plant nutrition, in a timely manner.
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Improving the use of plant parameters already known, as well as the development of
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new parameters that assist in fertilizer management are fundamental to contribute in increased
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fertilizer use efficiency, reduce the crop production cost and minimize environmental
5
1
contamination of soil and water (Rambo et al. 2004). In this sense, this work aimed a study of
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using images analysis as a tool to identify boron nutrition deficiences in the maize leaves.
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Materials and methods
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Greenhouse Experiment
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The experiment was performed in the Animal Science and Food Engineering College
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(FZEA) of University of São Paulo (USP) in Pirassununga (Sao Paulo State/Brazil). The crop
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tested was maize (Zea mays L.), hybrid DKB 390®, grown under hydroponic system in
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greenhouse. Seeding was performed in plastic trays filled with washed sand. After emergence,
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two plants were selected and transferred to each pot (3.6L) and supported by foam on top of
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each pot filled with Hoagland and Arnon (1950) nutrient solution (50% of total volume)
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modified to achieve the different levels of B concentration, during five days. After this time
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span, pots were completely filled with the nutrient solution and replaced weekly, and
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completely replaced weekly. The pH was monitored between 5.0 and 6.0 pH units and the
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average temperature approximately 28ºC. De-ionized water was used for to prepare all
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solutions. Each solution was renewed weekly. Each pot had air bobbling during 10 seconds
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with 30 seconds interval.
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Four levels of B were tested: 0.0; 0.12 (20% of a full dose); 0.24 (40% full dose) and
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0.60 mg L-1 (full dose – 100%) of Boron (B). Plant and leaf images were sampled at three
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main stages of maize development: V4 (plants with four leaves fully developed), V7 (plants
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with seven leaves fully developed) and R1 (complete flowering: early confirmation of
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productivity). Experimental design was fully randomized in 4 x 3 factorial arrangement (4 B
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levels and 3 sampling events) with four replications. In each established collection period, 16
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pots were sampled (destructive samples).
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Sampling and plant analysis
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1
Plant parts sampled were Index Leaf (IL: V4 = leaf 4; V7 = leaf 7, and R1 = opposite
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leaf below the first ear) and New Leaf (NL); both to chemical analysis and digital image
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capture. Shoot dry mass and roots dry mass were also measured. For chemical analysis, all
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material was washed, dried in oven with air circulation at 65 ºC, grinded and placed in plastic
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bags for further nutrient analyses. For B determination, samples were digested by dry
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pathway, according to methodology described in Bataglia et al. (1983).
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Statistical analysis
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The statistical data analysis was conducted using analysis of variance and Tukey test
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at 5% of probability. Such analysis was applied to data from B concentration in plants (mg kg-
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1
), shoot dry mass (g plant-1) and roots dry mass (g plant-1). In cases where the F test was
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significant (P≤0.05) for interaction between B levels and stages of plant development, the
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unfolding had the objective to study the levels inside the effect of maize in different stages of
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development. In those cases, one regression analysis was performed to each stages of
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development (three in total).
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Image analysis
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One of the targets in image analysis is to represent and describe an image in order to
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make an useful data useful to a computer (Gonzalez and Woods, 2006). In this area, feature
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extraction methods may highlight information relevant from images that are often not
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interpreted by the human visual system. Thus, a feature vector represents the image and it
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may be classified considering their proximity on set of all feature vectors.
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In the present study, texture is the visible attribute chosen once it is the key feature in
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leaf classification (Rossatto, 2011; Casanova, 2009). The leaf surface has a complex but
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persistent patterns characterized as natural texture (Kaplan, 1999, Rossatto, 2011). This
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property can be attributed to the structural pattern of the cells that is affected by nutritional
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deficiency.
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The first step of analysis is the digitalization of the leaf. A simple image acquisition
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process was performed using a conventional table-scanner (HP Scanjet 3800). Each leaf was
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cut from the plant, digitalized with 1,200 dpi resolutions, and stored in uncompressed digital
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format. The high resolution and the uncompressed storage allow for windowing-based
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approach in which small leaf epidermal structures are analyzed. Namely, a pixel of the image
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is about 450 μm2 in the leaf, with 1,200dpi resolution. Each image is cut out in sub-images
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with 80x80 pixels. The sub-images can be called windows and they are sampled from
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different positions of the leaf, discarding leaf defects as damages and holes, according to
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Casanova et al. (2009) and Rossatto et al. (2011). Figure 1 shows the process of windowing
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and the random selection of sub-images.
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The next step is the features extraction. A texture descriptor is used to extract a
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numeric vector that represents the sub-image in the feature space. On the last step, a pattern
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classification scheme separates the feature space to classify the samples. Different texture
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descriptors were used separately to demonstrate our proposal. The methods used were
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Fourier descriptors, Fractal volumetric descriptors, Gabor descriptors, Local binary pattern
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and Fractal volumetric on Gabor space, which are discussed below.
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Fourier descriptors extract information from image on Fourier domain, after apply the
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Fourier Transform (Gonzalez and Woods, 2006). A digital image is normally visualized on
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the spatial domain. The Fourier Transform changes the spatial domain to frequency domain or
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Fourier domain. Repetitive lines and other patterns are accumulated in a few pixels by Fourier
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transform, which highlights these characteristics. Formally, the Fourier transform is defined
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as
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F (u, v)
1 MN
M 1 N 1
ux
vy
f ( x, y) exp j 2 ( M N ) , x 0 y 0
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1
where f represents the image with M ´ N pixel, j is the imaginary unit, (x, y) are in the
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spatial domain and (u, v) are in the frequency domain. After the transformation, each feature
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vector value is the sum of absolute values on the circular rings centered on the frequency-
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image with different radius. Figure 1 illustrates this approach using different color for each
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circular ring.
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Fractal volumetric descriptors are based on the characterization of natural objects that
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cannot be described using Euclidean geometry, but using Fractal dimension (Zuñiga et al.,
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2014; Backes et al, 2009). Fractal objects have characteristics of self-similarity and infinite
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complexity. This modeling allows analyzing of complex patterns in different scales of the
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image. The image is considered as a surface on space, it is the same a 3D-plot of a discrete
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function. The points in space are centers of spheres whose radii vary in a range to generate
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Bouligand-Minkowski dilations. This approach is based on techniques to estimate the fractal
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dimension using the equation log( N (r )) , r 0 log(r )
D lim
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where N(r) is the number of spheres required to cover the object, and r is the radius of the
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spheres which in practice varies within a limited range. According to distribution of surface
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points, the spheres deform itself. The feature vector of fractal volumetric descriptors are
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composed by the volume of the structure formed in each dilation, i.e. the number N(r) with
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fixed each radius r.
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For Gabor descriptors, the convolution operation is performed in the image and a bank
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of Gabor filters, whose results are similar to receptive fields response of prestriated visual
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cortex present in the human visual system (Casanova, et al., 2009; Daugman and Downing,
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1995). The Gabor filters are Gaussians functions modulated by sinusoidal waves with
9
1
different scales and orientations. The following equations define the Gabor function g(x,y)
2
and the filters gmn(x,y) on spatial domain: g ( x, y )
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gmn ( x, y) a m g (a m ( x cos y sin ), a m ( y cos x sin )),
4 5
1 x2 y 2 exp 2 2 2 jWx , 2 x y 2 x y 1
where a 1 , m and n are integers, n / K and K is the total number of orientation.
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Each value of featured vector of Gabor descriptors is the entropy calculated from the
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resulting matrix in the spectral domain. The entropy is the sum of normalized histogram
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values extracted from the image. This process extracts spectral features as the Fourier
9
descriptors. However, here the Gabor filters are responsible for emphasizing the patterns.
10
Proposed by Zuñiga et al. (2014), the Fractal volumetric descriptors on Gabor space is
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a method to enhance the Gabor descriptors extracting a fractal signature of the magnitude
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space. The process is similar to Gabor descriptors. The image is transformed to Gabor space
13
using the convolution. On the resulting matrix, the Fractal volumetric approach is applied.
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The feature vector characterizes the complexity of Gabor space of texture image.
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Local binary pattern, proposed by Ojala et al. (1996) and Ojala et al. (2002), still
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simple and an efficient operator to analyze texture images. This operator compares each
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image pixel to its neighbors. For each comparison, the operator sets the value zero if the
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center pixel is greater than the neighbor; and value one otherwise. The local binary
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codification of each (i,j) pixel is obtained by P 1 1, x 0, LBP(i, j ) s( g p g c )2 p , where s( x) p 0 0, x 0,
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gc is the gray level of the center pixel (i,j) and gp is the gray level of the p neighbor (p=0,…,P-
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1).
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1
This procedure determines a binary code of each pixel with the size of the number of
2
neighbors and changes the binary code in decimal number (Figure 2). A code map stores the
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number for each pixel (i,j) as a code matrix. Then the feature vector is then formed by the
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histogram of the code matrix.
5
All of these feature extraction methods are traditional in the literature. More details are
6
beyond the scope of this paper of techniques application. Cited references should be consulted
7
for further details of each approach. The implementations are available by its authors or can
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be implemented with the aid of image processing toolbox in softwares as Matlab®.
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Results
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B concentration in leaves
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The B concentration in plant tissue of NL and IL increased significantly (p
