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Fuzzy controller in the selection of sugarcane and energy cane ideotypes

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Abstract

In response to the new requirements of the sugar-energy sector, sugarcane breeding programs are addressing the selection of clones for different purposes, e.g., sucrose production, ethanol production for first-generation (1G) and second-generation (2G) biofuels. Consequently, agronomic variables such as fiber content, sucrose content and biomass yield, become more relevant in the selection process, for underlying the definition of conventional, high biomass and multipurpose ideotypes. The relation between fiber and sucrose contents and biomass quantity should be used to differentiate thee different clone types: conventional clones, with high sucrose content and high biomass; high biomass, with high fiber content and high biomass; or multipurpose, with high fiber and sucrose contents and high biomass. In view of the difficulty of selecting different ideotypes from the same population, the objective of this study was to develop a method of selecting sugarcane clones for the different purposes. A population with 220 clones derived from crosses involving parents of different Saccharum species were subjected to the new methodology of clone selection with a fuzzy controller programmed to classify experimental clones in three ideotypes. Apart from the selection involving real data, the fuzzy controller was tested on 26 simulated populations. The controller proved to be efficient in the classification and selection of clones from the three ideotypes with real and simulated data.

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Author information

Authors and Affiliations

  1. Deptment of Crop Science, Universidade Federal Do Paraná, Paraná, 80035-050, Brazil

    Amaro Afonso Campos de Azeredo, Ricardo Augusto de Oliveira, João Carlos Bespalhok Filho & Bruno Portela Brasileiro

  2. Deptment of Biology, Universidade Federal de Viçosa, Minas Gerais, 36570-900, Brazil

    Leonardo Lopes Bhering & Cosme Damião Cruz

Authors
  1. Amaro Afonso Campos de Azeredo
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  2. Leonardo Lopes Bhering
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  3. Ricardo Augusto de Oliveira
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  4. Cosme Damião Cruz
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  5. João Carlos Bespalhok Filho
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  6. Bruno Portela Brasileiro
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Corresponding author

Correspondence to Amaro Afonso Campos de Azeredo.

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Appendices

Appendix 1: General commands for the fuzzy controller in software Matlab

Appendix 2: Families, clones and variables: fiber content (FIB), apparent sucrose content (PC) and tons of stalks per hectare (TSH), used as input variables. Output variables of fuzzy controller with ranking for the selection of conventional (CO), multipurpose (MP) and high biomass (HB) clones

Family

Clone

Input variables

Fuzzy controller output

Fuzzy selection ranking

FIB (%)

PC (%)

TSH

CO

MP

HB

CO

MP

HB

RB946022

RB92579

PRBIO198

12.41

12.48

165.74

52.7856

28.717

28.717

1

  

RB867515

US85-1008

PRBIO202

12.27

12.59

152.3

51.9627

28.5673

28.5673

2

  

RB946022

RB92579

PRBIO41

12.99

13.19

149.95

49.9515

29.3377

29.3377

3

  

Co453

IAC50/134

PRBIO68

12.28

13.38

148.19

48.2707

28.5779

28.5779

4

  

RB946022

RB92579

PRBIO37

11.12

12.11

199.98

46.7464

27.3657

27.3657

5

  

RB01649

IN84-58

PRBIO162

11.95

13.08

146.11

46.3591

28.2292

28.2292

6

  

RB946022

RB92579

PRBIO110

11.77

12.02

199.18

45.3347

28.0409

28.0409

7

  

B70710

RB72910

PRBIO89

13.9

11.97

154.54

44.5723

30.3275

30.3275

8

11

 

RB946022

RB92579

PRBIO39

13.73

12.51

143.72

44.2781

30.1431

30.1431

9

14

 

Co453

IAC50/134

PRBIO203

16.28

13.07

140.63

41.791

33.8086

33.8086

10

1

7

RB928064

US74-103

PRBIO223

13.21

11.93

139.32

40.5118

29.5769

29.5769

11

  

Co62175

IAN 48–21

PRBIO123

13.03

13.88

137.99

39.8587

29.3803

29.3803

12

  

RB93509

Co453

PRBIO217

13.30

11.59

189.26

39.4142

29.6726

29.6726

13

  

RB946022

RB92579

PRBIO111

12.34

12.67

137.12

39.2614

28.6426

28.6426

14

  

RB867515

US7614

PRBIO139

14.39

12.25

136.57

38.8939

30.8686

30.8686

15

5

 

RB93509

Co453

PRBIO212

12.37

12.96

135.19

38.006

28.6752

28.6752

16

  

RB867515

IM76-228

PRBIO221

11.90

11.43

285.57

37.6169

28.177

28.177

17

  

RB946022

RB92579

PRBIO40

12.41

12.07

134.19

37.3926

28.717

28.717

18

  

RB946022

RB92579

PRBIO36

13.64

11.38

148.85

37.1015

30.0428

30.0428

19

18

 

RB93509

Co285

PRBIO49

13.72

11.34

153.58

36.7067

30.1318

30.1318

20

15

 

CTC9

UM69-001

PRBIO32

13.49

11.49

127.99

34.1277

29.881

29.881

21

20

 

Co62175

IAN 48–21

PRBIO55

11.7

11.01

144.39

33.98

27.9671

27.9671

22

  

RB93509

Co285

PRBIO116

17.65

11.01

127.39

32.263

32.263

33.858

 

2

6

RB867522

IM76-235

PRBIO148

16.27

11.1

122.65

31.8553

31.0223

31.0223

 

3

 

IM76-228

US85-1008

PRBIO92

14.88

10.49

151.05

31.339

30.9056

31.4158

 

4

16

RB867515

US85-1008

PRBIO11

14.46

10.29

161.84

30.7406

30.6014

30.9459

 

6

 

Co453

IAC50/134

PRBIO149

20.8

10.49

125.32

30.586

30.586

32.9836

 

7

11

RB867520

IM76-233

PRBIO58

14.52

10.43

126.95

30.7018

30.3993

30.3993

 

8

 

RB011941

US85-1008

PRBIO193

15.08

10.69

126.73

31.1774

30.3766

30.3766

 

9

 

RB867523

IM76-236

PRBIO172

14.66

10.11

140.09

30.3473

30.3289

31.1686

 

10

21

CTC9

UM69-001

PRBIO180

13.9

10.96

147

33.6445

30.3275

30.3275

 

12

 

RB011941

US85-1008

PRBIO147

13.81

10.35

147.83

30.9013

30.2296

30.2296

 

13

 

RB867515

US7614

PRBIO26

13.72

10.44

126.39

30.6444

30.1318

30.1318

 

16

 

IM76-228

RB867515

PRBIO143

16.78

9.94

169.38

30.0732

30.0732

38.3824

 

17

2

RB93509

Co285

PRBIO117

13.57

10.18

154.6

30.4876

29.9665

29.9665

 

19

 

RB01649

IN84-58

PRBIO5

18.59

9.84

122.33

29.8442

29.8442

31.8667

 

21

15

RB928064

US74-103

PRBIO222

14.43

11.15

121.99

31.7495

29.8025

29.8025

 

22

 

RB83102

IM76-229

PRBIO53

17.15

9.39

181.85

29.2541

29.2541

41.7136

  

1

RB011941

US85-1008

PRBIO2

16.49

7.95

171.58

27.173

27.173

36.3334

  

3

RB92579

IM76-229

PRBIO99

17.13

7.76

135.19

26.9162

26.9162

35.7221

  

4

RB867515

US7614

PRBIO27

16.75

9.59

136.23

29.5511

29.5511

34.4932

  

5

RB867519

IM76-232

PRBIO57

17.4

9.16

126.95

28.9139

28.9139

33.6646

  

8

RB011941

US85-1008

PRBIO62

16.01

6.73

143.51

25.7251

25.7251

33.582

  

9

RB92579

IM76-229

PRBIO230

15.81

8.49

161.32

27.9396

27.9396

33.1924

  

10

IAC87-3396

US85-1008

PRBIO199

15.62

5.02

141.99

24.3149

24.3149

32.6467

  

12

RB867515

US7614

PRBIO132

15.82

8.49

139.39

27.9396

27.9396

32.5885

  

13

RB867525

IM76-238

PRBIO174

17.1

8.12

123.99

27.4121

27.4121

32.4661

  

14

CB38-22

B70710

PRBIO228

14.85

6.24

161.68

25.2567

25.2567

31.3836

  

17

RB92579

IM76-229

PRBIO14

15.43

9.34

133.19

29.1787

29.1787

31.3093

  

18

RB867515

US7614

PRBIO61

14.75

8.13

137.59

27.4263

27.4263

31.2687

  

19

Co453

IAC50/134

PRBIO191

16.99

10.09

120.45

29.6296

29.6296

31.2445

  

20

RB867521

IM76-234

PRBIO59

14.65

5.97

137.85

25.0217

25.0217

31.1577

  

22

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de Azeredo, A.A.C., Bhering, L.L., de Oliveira, R.A. et al. Fuzzy controller in the selection of sugarcane and energy cane ideotypes. Euphytica 216, 96 (2020). https://doi.org/10.1007/s10681-020-02626-6

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