**Marker Assisted Breeding**

The present book consists of six chapters devoted to marker-assisted breeding, i.e., wheat, soybean, and vegetables, together with RNA-seq and gene editing in plants. All of these chapters are written by scientists who have experience in biotechnological plant breeding. I would like to thank all of the authors not only for making a valuable contribution to recent resources in modern plant breeding but also for enabling this "Open Acess" publication to reach many scientists, teachers, and students working in the field. Moreover, I would also like to thank Intech Publishing Company, especially Ms. Ivana Glavic, Author Service Man‐ ager, and the technical editor of the book. Finally, I would like to thank my beloved husband, Mr. Burak Çiftçi; my two lovely sons, İbrahim Tuna and Kuzey Kenan; my dear parents, Öz‐ den and Çiftçi family; and my research team for all their patience, support, and courage.

VIII Preface

**Prof. Yelda Özden Çiftçi** Gebze Technical University

Kocaeli, Turkey

Department of Molecular Biology and Genetics

**Chapter 1**

**Provisional chapter**

**Marker-Assisted Breeding in Wheat**

**Marker-Assisted Breeding in Wheat**

DOI: 10.5772/intechopen.74724

Selection is an integral component in plant breeding, which ensures the progressive values of the breeding material, in terms of yield and quality. However, selection is influenced by the environment in any given growing season. The observed phenotype is a product of the genotype (G), the environment (E), and/or genotype × environment (G×E). Therefore, phenotypic selection is not always the best predirector of the genotype. Therefore, an environment-independent method is preferred by the breeder. The development of molecular markers in plants has facilitated marker-assisted selection (MAS). MAS requires the establishment of correlation between a desired trait such as disease resistance and molecular marker(s). This can be obtained, e.g., by phenotyping a genetic mapping population followed by QTL analysis. Initially, this process was slow due to the laborious nature of the first DNA molecular marker system, such as restriction fragment length polymorphism (RFLP). Later, with the discovery of various marker systems amenable to automation and the development of genotyping techniques and instruments, MAS has become a standard procedure in plant breeding. In wheat breeding, MAS helped to accelerate the introgression of many genes that contribute to improve quality and resistance.

**Keywords:** wheat, marker-assisted breeding, molecular markers, wheat diseases,

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Wheat is one of the most important sources of food worldwide. Data from FAOSTAT indicate that the need is still growing, indicated by the steadily increasing yield since 1961 (**Figure 1**). The need for an enhanced wheat production combined with stagnation in the area cultivated (**Figure 1**) leads to a demand for a more effective and higher yielding wheat production.

Nana Vagndorf, Peter Skov Kristensen, Jeppe Reitan Andersen, Ahmed Jahoor and

Nana Vagndorf, Peter Skov Kristensen, Jeppe Reitan Andersen, Ahmed Jahoor and

http://dx.doi.org/10.5772/intechopen.74724

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Jihad Orabi

Jihad Orabi

**Abstract**

wheat quality

**1. Introduction**

**Chapter 1 Provisional chapter**

### **Marker-Assisted Breeding in Wheat Marker-Assisted Breeding in Wheat**

Nana Vagndorf, Peter Skov Kristensen, Jeppe Reitan Andersen, Ahmed Jahoor and Jihad Orabi Nana Vagndorf, Peter Skov Kristensen, Jeppe Reitan Andersen, Ahmed Jahoor and Jihad Orabi

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.74724

### **Abstract**

Selection is an integral component in plant breeding, which ensures the progressive values of the breeding material, in terms of yield and quality. However, selection is influenced by the environment in any given growing season. The observed phenotype is a product of the genotype (G), the environment (E), and/or genotype × environment (G×E). Therefore, phenotypic selection is not always the best predirector of the genotype. Therefore, an environment-independent method is preferred by the breeder. The development of molecular markers in plants has facilitated marker-assisted selection (MAS). MAS requires the establishment of correlation between a desired trait such as disease resistance and molecular marker(s). This can be obtained, e.g., by phenotyping a genetic mapping population followed by QTL analysis. Initially, this process was slow due to the laborious nature of the first DNA molecular marker system, such as restriction fragment length polymorphism (RFLP). Later, with the discovery of various marker systems amenable to automation and the development of genotyping techniques and instruments, MAS has become a standard procedure in plant breeding. In wheat breeding, MAS helped to accelerate the introgression of many genes that contribute to improve quality and resistance.

DOI: 10.5772/intechopen.74724

**Keywords:** wheat, marker-assisted breeding, molecular markers, wheat diseases, wheat quality


**1. Introduction**

At ~860 Ma, Indian plate was broken from Antarctica and then it moved along with African plate until 184 Ma [1, 2]. Subsequently, it moved toward North until it got separated from Madagascar at 88 Ma [3]. At 65 Ma, it moved over the Reunion hotspot, which led to outpouring of Deccan volcanism resulting in the Deccan volcanic province occupying an area of 0.5 million km2 with 2000 m thick basaltic lava in the western and central parts of the Indian subcontinent within a short duration of time [4].

Occurrence of the initial outpouring of Deccan volcanism in Pakistan (at ~72–73 Ma) has been evidenced by the available 40Ar-39Ar age data of the Reunion Island like alkali lavas [3]. The presence of relatively younger basaltic intrusions (~68.5 Ma) in the northern India [5] has supported the fact that India moved

toward south. Also, younger basalts have been found further south [6]. Finally, at 65 Ma, the 95% basaltic outpouring of the Deccan volcanism took place in the western and central India [7]. This Deccan plume model has got further support from the presence of a low-velocity anomaly in the upper mantle extending up to a depth of 600 km below the north-western India as modeled by the regional mantle P-wave seismic tomography [8]. The presence of high 3 He/4 He ratio in Rajasthan has provided further support for the Deccan plume model [5]. Adding credence to this plume theory, local earthquake tomography, surface wave dispersion and receiver function modeling studies have imaged crustal mafic underplating, Moho upwarping and asthenospheric thinning underlying the 2001 Bhuj earthquake region [9, 10]. Recently, the shear-wave splitting study suggested that the upper mantle anisotropy in the KRZ is contributed both by lithospheric frozen anisotropy and asthenospheric flow induced anisotropy, which could be inherited from the plumelithosphere interaction during the Deccan/Reunion plume episode (~65 Ma) [11]. These observations suggest that imprints of the Deccan/Reunion mantle plume are still present in the crust and upper mantle below the north-western region.

The Kachchh region has been experiencing earthquakes since historical times [12]. The region has already experienced seven M6 earthquakes including two Mw 7.7 earthquakes in 1989 and 2001. The latter event has claimed a death toll of 20,000 people. The aftershock activity of this 2001 earthquake is continuing until today, with regular occurrences of Mw 3 events and occasional occurrences of Mw 4 events. The aftershock activity of the 2001 Bhuj earthquake is still continuing that includes 15 Mw ≥ 5, about 300 Mw ≥ 4 and about 6000 Mw ≥ 3 events. We feel that the enigmatic seismicity associated with the Kachchh rift zone is linked with its abovediscussed unique Geodynamic history. Aiming at understanding the influence of crustal-mantle structure in the genesis of uninterrupted occurrences of earthquakes since the occurrence of the 26 January Mw 7.7 Bhuj earthquake, in this chapter, the crust corrected P-wave residuals are estimated at 14 broadband stations and a 3-dimensional P-wave teleseismic tomography is performed using the estimated crust corrected residuals. Finally, modeling results are interpreted concerning the geodynamical processes responsible for generating intraplate earthquakes occurring in the Kachchh region.

### **2. Seismic network and data**

For the present study, broadband digital waveforms of 241 teleseismic events from 14 three-component broadband stations in Kachchh, Gujarat are used (**Figure 1a** and **b**). The sampling rate of recording is 50 sps. Station spacing in the above seismic network is 30–100 km, however, for our study we use data from 20 broadband stations consisting of 14 NGRI and 6 ISR stations as shown by a square in **Figure 2**. The station spacing for the network consisting of these 20 stations is 30–60 km. We selected 241 teleseismic events with magnitude ranging from 5.9 to 8.2, with epicentral distances of 29–90° (**Figure 1b**) with reference to the center of the network.

First, each trace of an event is used to pick the arrival time of the first P-wave maximum amplitude (either peak or trough). Then, these picks are correlated within the network. Following this procedure, the first P-wave onset times are picked from selected highest-quality traces recorded at different stations for one event. Here, the uncertainty of the picking is used to decide a quality factor for each measurement. Note that most of the measurements are found to be having an uncertainty of ±0.05 s. Finally, these quality factors are used to estimate the average data error, which is found to be ±0.06 s.

**33**

**Figure 1.**

*P-Wave Teleseismic Tomography: Evidence of Imprints of Deccan Mantle Plume…*

*(a) Elevation (in m) map showing station distribution in Kachchh, Gujarat. Filled red triangles mark the broadband seismograph stations (SIV—Sivlok; VJP—Vajepar; KNM—Kanmer; BCH—Bhachau; BEL—Bela; GDD—Gadhada; JUM—Jumkunaria; MTP—Motapaya; NGR—Nagor; BHU—Bhuj; NPR—Narayanpar; TPM—Tapar Mundra; MND—Mandvi; VGH—Vaghura; TPR—Tapar Anjar; and NDD—New Dudhai). The inset shows the key map for the area, where the study area is shown by a red open square. K and KL mark Kachchh, Gujarat and Killari, Maharashtra, respectively. An arrow shows the location of Cambay. The black filled portion marks the areal extent of Deccan volcanic province (DVP) in India. (b) Epicentral plot of 241 teleseismic events of Mw 6.0–8.4, whose broadband data are used for our P-wave teleseismic tomography study. A red triangle and green diamond symbols mark the center of our network (lat. 70°, long. 23°) and epicenters of*

*selected teleseismic events. The size of the diamond symbols vary depending on their sizes.*

*DOI: http://dx.doi.org/10.5772/intechopen.83738*

*P-Wave Teleseismic Tomography: Evidence of Imprints of Deccan Mantle Plume… DOI: http://dx.doi.org/10.5772/intechopen.83738*

#### **Figure 1.**

*Forecasting Volcanic Eruptions*

ring in the Kachchh region.

**2. Seismic network and data**

data error, which is found to be ±0.06 s.

toward south. Also, younger basalts have been found further south [6]. Finally, at 65 Ma, the 95% basaltic outpouring of the Deccan volcanism took place in the western and central India [7]. This Deccan plume model has got further support from the presence of a low-velocity anomaly in the upper mantle extending up to a depth of 600 km below the north-western India as modeled by the regional mantle

provided further support for the Deccan plume model [5]. Adding credence to this plume theory, local earthquake tomography, surface wave dispersion and receiver function modeling studies have imaged crustal mafic underplating, Moho upwarping and asthenospheric thinning underlying the 2001 Bhuj earthquake region [9, 10]. Recently, the shear-wave splitting study suggested that the upper mantle anisotropy in the KRZ is contributed both by lithospheric frozen anisotropy and asthenospheric flow induced anisotropy, which could be inherited from the plumelithosphere interaction during the Deccan/Reunion plume episode (~65 Ma) [11]. These observations suggest that imprints of the Deccan/Reunion mantle plume are

still present in the crust and upper mantle below the north-western region.

The Kachchh region has been experiencing earthquakes since historical times [12]. The region has already experienced seven M6 earthquakes including two Mw 7.7 earthquakes in 1989 and 2001. The latter event has claimed a death toll of 20,000 people. The aftershock activity of this 2001 earthquake is continuing until today, with regular occurrences of Mw 3 events and occasional occurrences of Mw 4 events. The aftershock activity of the 2001 Bhuj earthquake is still continuing that includes 15 Mw ≥ 5, about 300 Mw ≥ 4 and about 6000 Mw ≥ 3 events. We feel that the enigmatic seismicity associated with the Kachchh rift zone is linked with its abovediscussed unique Geodynamic history. Aiming at understanding the influence of crustal-mantle structure in the genesis of uninterrupted occurrences of earthquakes since the occurrence of the 26 January Mw 7.7 Bhuj earthquake, in this chapter, the crust corrected P-wave residuals are estimated at 14 broadband stations and a 3-dimensional P-wave teleseismic tomography is performed using the estimated crust corrected residuals. Finally, modeling results are interpreted concerning the geodynamical processes responsible for generating intraplate earthquakes occur-

For the present study, broadband digital waveforms of 241 teleseismic events

First, each trace of an event is used to pick the arrival time of the first P-wave maximum amplitude (either peak or trough). Then, these picks are correlated within the network. Following this procedure, the first P-wave onset times are picked from selected highest-quality traces recorded at different stations for one event. Here, the uncertainty of the picking is used to decide a quality factor for each measurement. Note that most of the measurements are found to be having an uncertainty of ±0.05 s. Finally, these quality factors are used to estimate the average

from 14 three-component broadband stations in Kachchh, Gujarat are used (**Figure 1a** and **b**). The sampling rate of recording is 50 sps. Station spacing in the above seismic network is 30–100 km, however, for our study we use data from 20 broadband stations consisting of 14 NGRI and 6 ISR stations as shown by a square in **Figure 2**. The station spacing for the network consisting of these 20 stations is 30–60 km. We selected 241 teleseismic events with magnitude ranging from 5.9 to 8.2, with epicentral distances of 29–90° (**Figure 1b**) with reference to the center of

He/4

He ratio in Rajasthan has

P-wave seismic tomography [8]. The presence of high 3

**32**

the network.

*(a) Elevation (in m) map showing station distribution in Kachchh, Gujarat. Filled red triangles mark the broadband seismograph stations (SIV—Sivlok; VJP—Vajepar; KNM—Kanmer; BCH—Bhachau; BEL—Bela; GDD—Gadhada; JUM—Jumkunaria; MTP—Motapaya; NGR—Nagor; BHU—Bhuj; NPR—Narayanpar; TPM—Tapar Mundra; MND—Mandvi; VGH—Vaghura; TPR—Tapar Anjar; and NDD—New Dudhai). The inset shows the key map for the area, where the study area is shown by a red open square. K and KL mark Kachchh, Gujarat and Killari, Maharashtra, respectively. An arrow shows the location of Cambay. The black filled portion marks the areal extent of Deccan volcanic province (DVP) in India. (b) Epicentral plot of 241 teleseismic events of Mw 6.0–8.4, whose broadband data are used for our P-wave teleseismic tomography study. A red triangle and green diamond symbols mark the center of our network (lat. 70°, long. 23°) and epicenters of selected teleseismic events. The size of the diamond symbols vary depending on their sizes.*

#### **Figure 2**

*(a) Crust corrected relative P-residuals in Gujarat. (b) Directional mean of P-residuals by subtracting an average residual from the eight stations in Kachchh. Red open circles mark negative residuals while blue open circles show positive residuals. CRZ marks the Cambay rift zone, which is shown by dotted lines. Black dotted elliptical area mark the central Kachchh rift zone characterized by negative residuals. And, black square area shows our study area considered for our P-wave teleseismic tomography.*


**Author details**

Hester Carina Schönfeldt\*, Nicolette Hall and Beulah Pretorius

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Animal and Wildlife Sciences, Institute of Food, Nutrition and Well‐being, University of Pretoria, Pretoria, South Africa


**Author details**

*Advances in the Molecular Understanding of Colorectal Cancer*

HNPCC hereditary non-polyposis colon cancer

hsc70 heat-shock cognate protein 70 kDa IGF2 insulin-like growth factor 2

iNOS inducible nitric oxide synthase

KRAS Kirsten rat sarcoma 2 viral oncogene LAMP2A lysosomal-associated membrane protein 2A

HS3ST2 (3OST2) heparan sulfate-glucosamine 3-sulfotransferase 2

IGFBP3 insulin-like growth factor binding protein 3

MGMT O-6-methylguanine-DNA methyltransferase

PMS2 PMS1 homolog 2, mismatch repair system component

STAT3 signal transducer and activator of transcription 3

TMEFF2 transmembrane protein with EGF-like and two follistatin-like domains 2

WRN Werner syndrome ATP-dependent helicase

SUV39H1 suppressor of variegation 3–9 homolog 1 TIMP3 tissue inhibitor of metalloproteinases 3

TNF-α tumor necrosis factor alpha UNC5C Unc-5 netrin receptor C Wif-1 WNT inhibitory factor 1

Hnf1b HNF1 homeobox B

IL-6 interleukin 6

KDM lysine demethylase

LOH loss of heterozygosity mCRC metastatic colorectal cancer

MINT Munc-18-interacting

MSH2, 6 MutS protein homolog 2, 6 MSI microsatellite instability MSS microsatellite stable Nat10 N-acetyltransferase 10

p300/CBP p300/CREB-binding protein

PRMT protein arginine methyltransferase PRMT5 protein arginine methyltransferase 5 PTEN phosphatase and tensin homolog RASSF1A ras association domain family member 1 RASSF2A ras association domain family member 2 RUNX3 runt-related transcription factor 3 SAHA suberoylanilide hydroxamic acid SETD1A SET domain-containing protein 1A SFRP1 secreted frizzled related protein 1 SFRP5 secreted frizzled related protein 5 SMYD3 SET and MYND domain containing 3 SOCS1 suppressor of cytokine signaling 1

miRNA microRNA MLH1 mutL homolog 1

NEUROG1 neurogenin 1 nRNA noncoding RNA

p53 or TP53 tumor protein 53 PCDH10 protocadherin 10 PD-1 programmed death-1 PDL-1 programmed death ligand-1

Plks polo-like kinases

SOX17 SRY-Box 17

WNT wingless type

**96**

Antja-Voy Hartley1 , Matthew Martin1 and Tao Lu1,2,3\*

1 Department of Pharmacology and Toxicology, Indiana University School of Medicine, USA

2 Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, USA

3 Department of Medical and Molecular Genetics, Indiana University School of Medicine, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**6. Exemplar industrial applications**

The quality control in industrial applications is used to monitor and to guarantee the quality of the processes.

Fuzzy Inference Systems Applied to Image Classification in the Industrial Field 253

Two features are selected to be fed as inputs to the fuzzy system namely the vehicle weight and speed. The output of the fuzzy system is a weighting factor which is used to modify the

Triangular membership functions have been defined to represent each linguistic value. Finally the fuzzy rule basis have been created with the help of expert's heuristic knowledge,

> Slow Very light Zero Medium Very light Negative small Fast Very light Negative big Slow Light Positive small Medium Light Zero Slow Medium Positive big Medium Medium Positive small Fast Medium Zero Slow Heavy Positive big Medium Heavy Positive big Fast Heavy Positive small

On the basis of the rules the output of the fuzzy system is evaluated through an inference system of Mandani type (Mandani, 1974) and a defuzzification operation. Finally the

where *LM* is the modified length, *L* represents the measured length and *WF* is the weighting factor in output from the fuzzy system. The modified length is used as input to a classifier in order to obtain the vehicle classification. Experimental results demonstrate that the proposed algorithm using fuzzy approach significantly outperforms the conventional vehicle classification algorithm (Kim et al., 1998; Kim et al., 1999). The classification error using a conventional algorithm is 12.78% (74 errors on 579 vehicles analysed), while adopting the proposed fuzzy approach the classification error decreases to 6.56% (38 errors

Borselli et al. (Borselli et al., 2011) propose a fuzzy-based classification in order to classify a particular class of defects that can be present on the surface of some flat steel products. In the steelmaking industry a lot of steel rolling mills are equipped with an Automatic

*LM = L \* [1 + (WF /100)]* (3)

**Speed Weight Length Modification**

length value. Inputs and output are interpreted as linguistic values in this manner:

which is described in Tab.1.

Table 1. Rule basis

on 579 vehicles).

modified vehicle length is calculated as follows:

**6.2 Classification of surface defects on flat steel products**

Actually many industries have adopted vision systems for improve the quality control of products (Amiri & Shanbehzadeh, 2009; Ferreira et al., 2009). The quality control includes the ability tackling problems of several scientific areas, such as signal acquisition, signal processing, features extraction, classification and so on. Many approaches have been proposed in order to design intelligent signal and visual inspection systems for the quality control using fuzzy theory, that has been recognized in the literature as a good tool to achieve these goals. In the following a description of some exemplar fuzzy-based methods is proposed.

#### **6.1 Vehicle classification exploiting traffic sensors**

Kim et al. (Kim et al., 2001) propose an algorithm for vehicle classification based on fuzzy theory. Many approaches have been proposed in order to identify vehicles through traffic sensors; one of the most typically adopted technologies for vehicle classification is the combined loop and piezoelectric sensor system (Kim et al., 1998; Kim et al., 1999). A heuristic knowledge about vehicle speed or shape, once the vehicle length is known, is available, but in the loop/piezo detector there is not a precise mathematic association between the vehicle length and speed or shape. Also this heuristic knowledge could be well formalised using the *if-then* fuzzy rules.

The main idea of the proposed method is to modify the output length value from the loop sensor and use it to classify each vehicle. The general scheme of the proposed algorithm is shown in Fig.4.

Fig. 4. Scheme of the system for vehicles classification.

Two features are selected to be fed as inputs to the fuzzy system namely the vehicle weight and speed. The output of the fuzzy system is a weighting factor which is used to modify the length value. Inputs and output are interpreted as linguistic values in this manner:

252 Fuzzy Inference System – Theory and Applications

Actually many industries have adopted vision systems for improve the quality control of products (Amiri & Shanbehzadeh, 2009; Ferreira et al., 2009). The quality control includes the ability tackling problems of several scientific areas, such as signal acquisition, signal processing, features extraction, classification and so on. Many approaches have been proposed in order to design intelligent signal and visual inspection systems for the quality control using fuzzy theory, that has been recognized in the literature as a good tool to achieve these goals. In

Kim et al. (Kim et al., 2001) propose an algorithm for vehicle classification based on fuzzy theory. Many approaches have been proposed in order to identify vehicles through traffic sensors; one of the most typically adopted technologies for vehicle classification is the combined loop and piezoelectric sensor system (Kim et al., 1998; Kim et al., 1999). A heuristic knowledge about vehicle speed or shape, once the vehicle length is known, is available, but in the loop/piezo detector there is not a precise mathematic association between the vehicle length and speed or shape. Also this heuristic knowledge could be well

The main idea of the proposed method is to modify the output length value from the loop sensor and use it to classify each vehicle. The general scheme of the proposed algorithm is

the following a description of some exemplar fuzzy-based methods is proposed.

**6.1 Vehicle classification exploiting traffic sensors**

Fig. 4. Scheme of the system for vehicles classification.

formalised using the *if-then* fuzzy rules.

shown in Fig.4.

Triangular membership functions have been defined to represent each linguistic value. Finally the fuzzy rule basis have been created with the help of expert's heuristic knowledge, which is described in Tab.1.

Table 1. Rule basis

On the basis of the rules the output of the fuzzy system is evaluated through an inference system of Mandani type (Mandani, 1974) and a defuzzification operation. Finally the modified vehicle length is calculated as follows:

$$\text{LIM} = \text{L}^\* \left\{ 1 + \left( \text{V} \text{F} / 100 \right) \right\} \tag{3}$$

where *LM* is the modified length, *L* represents the measured length and *WF* is the weighting factor in output from the fuzzy system. The modified length is used as input to a classifier in order to obtain the vehicle classification. Experimental results demonstrate that the proposed algorithm using fuzzy approach significantly outperforms the conventional vehicle classification algorithm (Kim et al., 1998; Kim et al., 1999). The classification error using a conventional algorithm is 12.78% (74 errors on 579 vehicles analysed), while adopting the proposed fuzzy approach the classification error decreases to 6.56% (38 errors on 579 vehicles).

#### **6.2 Classification of surface defects on flat steel products**

Borselli et al. (Borselli et al., 2011) propose a fuzzy-based classification in order to classify a particular class of defects that can be present on the surface of some flat steel products. In the steelmaking industry a lot of steel rolling mills are equipped with an Automatic

Fuzzy Inference Systems Applied to Image Classification in the Industrial Field 255

The output is a fuzzy set, called probability of LPI defect, which can assume two values: *low*

**Small** High High Small Or low **High** Small Small High Or high

All the considered membership functions are Gaussian and are tuned by exploiting the data, as the adopted FIS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) (Jang,

In order to demonstrate the effectiveness of the proposed method a comparison with other common classifiers has been made. The tested classifiers are: a Multi Layer Perceptron (MLP) classifier (Werbos, 1974), a Decision Tree (DT) (Argentiero et al., 1982) based on C4.5 algorithm (Quinlan, 1993), a Support Vector Machine (SVM) (Yan et al., 2009), a Learning Vector Quantization-based (LVQ) classifier (Elsayad, 2009). The method has also been compared to a previous system developed by the same authors (Borselli et al., 2010), which

The experimental data involve 212 images provided by an Italian steelmaking industry which have been randomly divided in two groups: a training set which includes the 75% of

> μ = ∑ ���(�) � ���

� = �∑ (���(�)��) � � ���

where *Acc(i)* is the accuracy calculated during test *i-*th. *N* is the number of tests (in this

Results show that the proposed ANFIS-based approach outperforms the other approaches providing the highest accuracy (94.4%) and a low standard deviation values (2.1 %). This approach outperforms the other ones mainly because it is based on the knowledge of the

exploits a non-adaptive FIS whose parameters have been heuristically tuned.

The performance has been quantified in terms of average accuracy

, that are calculated on 30 tests and are defined as follows:

**Shape Brigthness Operator Probability**

� (4)

� (5)

and standard deviation

**of LPI defect**

The classification is based on a FIS. Four fuzzy sets are considered:

The inference rules that have been adopted are described in Table 2.

> **Maximum Width**

data and a validation set with the remaining 25%.

and *high*.

**Number of regions**

Table 2. Rules

1991; Jang, 1993).

example *N=30*).

technical personnel.

Inspection System (ASIS) (Stolzenberg & Geisler, 2003). Such system contains a lighting system to illuminate the two faces of the moving strip and a set of cameras catch the images related to the steel surface each time a potential defect is detected. The defect is classified, when possible, and the images are stored in a database. Due to large amount of images and to the time constraints, often an on line classifier can misclassify some defects or it is not able to classify particular defects. An offline analysis of the non classified defects through more powerful although time-consuming techniques concerning this class of defects can greatly enhance the quality monitoring. In (Borselli et al., 2011) an off line classifier is proposed, that is capable to distinguish two types of particular defects called *Large Population of Inclusions* (LPI) and *Rolled In*, which are very similar. Before classification process an image cleaning is carried out through two filtering operations in order to improve the image quality. In particular a Sobel filter (Weng & Zhong, 2008) and a binarization are applied. Sobel filter is used to detect the edge while the binarization is a filter used to point out the regions having a different lightness with respect to the background. The two filters are independently applied to the original image and then the resulting images are summed in order to achieve a binary image where relevant defects are more visible. Depending on the nature of the considered defects, four features are extracted from the image which are: number of the regions where a defect is focused, the maximum width, the shape and the brightness of the considered regions. The four features are fed as inputs to classifier and the output, a value lying in the range [0,1], represents the probability that the analyzed defect is effectively a LPI defect. Finally a threshold comparison determines whether the defect belong to the LPI class or to the Rolled In class. The threshold is set to 0.5 and output values greater than 0.5 are considered LPI defects, Rolled In otherwise. The general scheme of the proposed approach is shown in Fig.5.

Fig. 5. Diagram of the proposed approach.

The classification is based on a FIS. Four fuzzy sets are considered:

254 Fuzzy Inference System – Theory and Applications

Inspection System (ASIS) (Stolzenberg & Geisler, 2003). Such system contains a lighting system to illuminate the two faces of the moving strip and a set of cameras catch the images related to the steel surface each time a potential defect is detected. The defect is classified, when possible, and the images are stored in a database. Due to large amount of images and to the time constraints, often an on line classifier can misclassify some defects or it is not able to classify particular defects. An offline analysis of the non classified defects through more powerful although time-consuming techniques concerning this class of defects can greatly enhance the quality monitoring. In (Borselli et al., 2011) an off line classifier is proposed, that is capable to distinguish two types of particular defects called *Large Population of Inclusions* (LPI) and *Rolled In*, which are very similar. Before classification process an image cleaning is carried out through two filtering operations in order to improve the image quality. In particular a Sobel filter (Weng & Zhong, 2008) and a binarization are applied. Sobel filter is used to detect the edge while the binarization is a filter used to point out the regions having a different lightness with respect to the background. The two filters are independently applied to the original image and then the resulting images are summed in order to achieve a binary image where relevant defects are more visible. Depending on the nature of the considered defects, four features are extracted from the image which are: number of the regions where a defect is focused, the maximum width, the shape and the brightness of the considered regions. The four features are fed as inputs to classifier and the output, a value lying in the range [0,1], represents the probability that the analyzed defect is effectively a LPI defect. Finally a threshold comparison determines whether the defect belong to the LPI class or to the Rolled In class. The threshold is set to 0.5 and output values greater than 0.5 are considered LPI defects,

Rolled In otherwise. The general scheme of the proposed approach is shown in Fig.5.

Fig. 5. Diagram of the proposed approach.

The output is a fuzzy set, called probability of LPI defect, which can assume two values: *low* and *high*.

The inference rules that have been adopted are described in Table 2.

Table 2. Rules

All the considered membership functions are Gaussian and are tuned by exploiting the data, as the adopted FIS is an Adaptive Neuro-Fuzzy Inference System (ANFIS) (Jang, 1991; Jang, 1993).

In order to demonstrate the effectiveness of the proposed method a comparison with other common classifiers has been made. The tested classifiers are: a Multi Layer Perceptron (MLP) classifier (Werbos, 1974), a Decision Tree (DT) (Argentiero et al., 1982) based on C4.5 algorithm (Quinlan, 1993), a Support Vector Machine (SVM) (Yan et al., 2009), a Learning Vector Quantization-based (LVQ) classifier (Elsayad, 2009). The method has also been compared to a previous system developed by the same authors (Borselli et al., 2010), which exploits a non-adaptive FIS whose parameters have been heuristically tuned.

The experimental data involve 212 images provided by an Italian steelmaking industry which have been randomly divided in two groups: a training set which includes the 75% of data and a validation set with the remaining 25%.

The performance has been quantified in terms of average accuracy and standard deviation , that are calculated on 30 tests and are defined as follows:

$$
\mu = \frac{\sum\_{l=1}^{N} Acc(l)}{N} \tag{4}
$$

$$
\sigma = \sqrt{\frac{\sum\_{l=1}^{N} (Acc(l) - \mu)^2}{N}} \tag{5}
$$

where *Acc(i)* is the accuracy calculated during test *i-*th. *N* is the number of tests (in this example *N=30*).

Results show that the proposed ANFIS-based approach outperforms the other approaches providing the highest accuracy (94.4%) and a low standard deviation values (2.1 %). This approach outperforms the other ones mainly because it is based on the knowledge of the technical personnel.

Fuzzy Inference Systems Applied to Image Classification in the Industrial Field 257

In order to limit the number of membership functions and rules the classification task is developed through two different fuzzy systems operating parallel. The first fuzzy classification system is used to provide a percentile index of acceptability of the weld, while the second fuzzy system detects the simultaneous signal patters directly connected with a specific defect. The first FIS gives a real time estimate of the quality of each weld, also for each time-window the system analyzes the indices through a rule-based method. Firstly a partition of the range of each observed input three fuzzy set is been made basing on the set of reference welds used as training set. The three introduced fuzzy sets are referred to quality: Optimal (OPT), acceptable (ACC) and unacceptable (UNA) and the membership functions have a trapezoidal shape. Finally the fuzzy system uses the welding time (*Time*) as an input to let the classification ignore the first seconds of process when the welding equipment is warming up. To describe the time interval a single linear piecewise increasing membership function regime (*REG*) is introduced. The output is represented by three membership functions as well. In this case the membership function are represented by

Once the membership functions have been defined, a few generic rules are introduced. The first rules refer to obvious conditions; then, each time, another rule is included and the overall classification performance is evaluated in order to adjust membership function and rule weights. The output is so defined as an index of weld quality, in particular 0% (UNA) indicates the occurrence of defect while 100% (OPT) represents an optimal weld, values in the range 0-100% represent intermediate acceptability. Subsequently a threshold of acceptability is introduced in order to convert the fuzzy degree of quality in a binary

*2. If Time is REG, NSO (IR) is OPT and CNSO (IR) is OPT and RSN (IR) is OPT and NSO (UV) is OPT and CNSO (UV) is OPT and RSN (UV) is OPT and NSO (VIS) is OPT and CNSO (VIS) is OPT and RSN (VIS) is OPT and NSO (Te) is OPT and CNSO (Te) is OPT*

*3. If NSO (IR) is UNA or CNSO (IR) is UNA or RSN (IR) is UNA or NSO (UV) is UNA or CNSO (UV) is UNA or RSN (UV) is UNA or NSO (VIS) is UNA or CNSO (VIS) is UNA or RSN (VIS) is UNA or NSO (Te) is UNA or CNSO (Te) is UNA or RSN (Te) is UNA then the*

*4. If NSO (IR) is ACC or CNSO (IR) is ACC or RSN (IR) is ACC or NSO (UV) is ACC or CNSO (UV) is ACC or RSN (UV) is ACC or NSO (VIS) is ACC or CNSO (VIS) is ACC or RSN (VIS) is ACC or NSO (Te) is ACC or CNSO (Te) is ACC or RSN (Te) is ACC then the*

*5. If Time is REG, NSO (IR) is ACC and CNSO (IR) is ACC and RSN (IR) is ACC and NSO (UV) is ACC and CNSO (UV) is ACC and RSN (UV) is ACC and NSO (VIS) is ACC and CNSO (VIS) is ACC and RSN (VIS) is ACC and NSO (Te) is ACC and CNSO (Te) is ACC*

The fuzzy rules used by the quality estimation system can be described as follow:

three singletons as follows:

decision: good or defective weld.

*OUTPUT is UNA. (weigth =0.1).*

*OUTPUT is ACC. (weigth =0.2).*

*1. If Time is not REG then the output is OPT. (weigth=1).*

*and RSN (Te) is OPT then the OUTPUT is OPT. (weigth =1).*

*and RSN (Te) is ACC then the OUTPUT is ACC. (weigth =0.8).*


**6. Digestive system**

Owls have evolved to eat their smaller prey whole and unlike other birds, they do not have a crop (**Figure 9**). This system reduces the owl's need to drink water, as much of its liquid intake comes directly from the body fluids of its prey. The whole prey is passed head first straight down the oesophagus and into the proventriculus (glandular stomach).

Digestion begins in the proventriculus, which produces digestive enzymes and stomach acid. The food mass, along with the digestive enzymes, then passes into the second part of the stomach, the ventriculous or gizzard (muscular stomach) where the chemical digestion started in the proventriculus continues and manual digestion begins. The gizzard uses strong muscular contractions to aid in digestion. The soft and digestible parts of the food are allowed to continue along with the digestive system into the small intestine [27]. The indigestible parts (fur, feathers, claws, bones etc.) are retained in the gizzard and compacted into an oval-shaped pellet (oval due to the gizzard's shape). The digestion process up to this point takes several hours (**Figure 10**). The pellet is then passed back into the proventriculus where it will remain for several hours more before finally being regurgitated. Additional digestive enzymes are likely digesting any remaining digestible

**Figure 9.** *Juvenile tawny owl (*Strix aluco*) swallowing rodent prey. Photo: Alan Sieradzki.*

#### **Figure 10.**

*The digestive system of an owl. Image: Alan Sieradzki.*

#### **Figure 11.**

*Owl pellet species comparison. L-R: Tawny owl (*Strix aluco*) barn owl (*Tyto alba*), short-eared owl (*Asio flammeus*). Photo: Alan Sieradzki.*

material during this time [28]. Because the stored pellet partially blocks the owl's digestive system, new prey cannot be swallowed until this pellet is ejected.

Regurgitation often signifies that the owl is ready to eat again. When the owl eats more than one prey item within several hours, the various remains are consolidated into one pellet. When the digestive process is finished, the owl will regurgitate the pellet by the process of reverse peristalsis, where smooth muscular contractions push the pellet up the oesophagus and back into the mouth. This process is different from coughing or retching

and can prove to be quite strenuous for the owl, especially with larger pellets—this is why an owl will often take on a pained expression when producing a pellet and the reason why owls cannot produce pellets in flight. At the moment of expulsion, the neck is stretched up and forward, the beak is opened, and the pellet simply drops out (**Figure 11**).