**5.1 Image measurement and computation method at applications**

A digital camera was used for determining dimensions and digital images were evaluated by Myriad image processing software. Measuring and verification process of digital images can be done with the help of this program safely, also digital images can be compared easly (Fig. 12.). This program has two steps for measuring images. First step was calibration of the program. For this purpose software needed a calibration ruler or a calibration plate. Hence a millimetric paper was used for calibration and testing for software (Fig. 13.). For the calibration, a calibration plate with known dimensions was used while taking digital images. Second step was true selection of images (Beyaz, 2009a).

Fig. 11. 3D measurement system that it developed jointly with the National Institute of

between an original 3-D surface and its deformed version. The other important challenge is analysis of 3-D views by using 2-D screens. Image measurements still require identifying the pixels that are connected to each other. In two dimensions, it is necessary to determine

Existing 3D measurement techniques are classified into two major types—active and passive. In general, active measurement employs structure illumination (structure projection, phase shift, moire topography, etc.) or laser scanning, which is not necessarily desirablein many applications. On the other hand, passive 3D measurement techniques based on stereo vision have the advantages of simplicity and applicability, since such

A digital camera was used for determining dimensions and digital images were evaluated by Myriad image processing software. Measuring and verification process of digital images can be done with the help of this program safely, also digital images can be compared easly (Fig. 12.). This program has two steps for measuring images. First step was calibration of the program. For this purpose software needed a calibration ruler or a calibration plate. Hence a millimetric paper was used for calibration and testing for software (Fig. 13.). For the calibration, a calibration plate with known dimensions was used while taking digital

Advanced Industrial Science and Technology (Anonymous, 2011a.).

**5.1 Image measurement and computation method at applications** 

images. Second step was true selection of images (Beyaz, 2009a).

differences between touching pixels.

techniquesrequire simple instrumentation.

**5. Agricultural application gallery** 

Fig. 12. Interface of digital image analysis software Myriad v8.0

Fig. 13. Milimetric paper test of image processing software.

Colour measurements were determined with Minolta Cr200 model colourmeter which is based on L\*a\*b\* measurement system (Fig. 14.). Avarage of three sample points are used as colour value (Fig. 15.). Minolta Cr200 model colourmeter was calibrated with a white reflective plate.

Machine Vision Measurement Technology and Agricultural Applications 147

Kahramanmaras red peppers and the front projection area values (AF) was pointed as a graph at Fig. 16. Similarly top projection area values were presented at Fig. 17. However left projection areas values and the estimation equation can be seen in Fig. 18 (Beyaz et al.,

Real Volume Value (ml) = - 22,96 + 2,099 (AF)

S 20,7203 R-Sq 49,9% R-Sq(adj) 47,1%

S 16,9114 R-Sq 66,6% R-Sq(adj) 64,8%

**AF (cm2)**

**AT (cm2)**

Fig. 17. The regression equation and graph obtained by using AT projection area

30 35 40 45 50 55

Fig. 16. The regression equation and graph obtained by using AF projection area

40 45 50 55 60 65 70

According to this assessment, the real volume value which was obtained from the front projection area shows the regression coefficient has reached 49.9%. The regression coefficient obtained from estimation equations by using of the top projection area was 66.6% (Fig. 17.).The regression coefficient obtained by using left projection area was 63.6% (Fig.

Real Volume Value (ml) = - 33,61 + 2,733 (AT)

2009d).

**Real Volume Value (ml)**

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

150

125

100

75

50

(Beyaz et al., 2009d).

18.).

150

125

100

75

50

Fig. 14. Sample colour measurement of an apple.

Fig. 15. Selection of L\*a\*b\* sample points at a quice by using colourmeter (Beyaz et al., 2011).

#### **5.1.1 Volume determination of Kahramanmaras red pepper (***Capsicum annuum* **L.) by using image analysis technique**

The size of an agricultural product is an important parameter to determine fruit growth and quality. It can be used to determine the optimum harvest time as a maturity index. In this study, the image analysis method was tested on Kahramanmaras red pepper (*Capsicum annuum* L.) which may have a non uniform shape. For this purpose; the front, top and left side of each pepper was taken into account for evaluations and projection areas. The effect of each image and image combination has been used to determine the volume of peppers. The regression coefficients between the projection areas and volume values have also been assessed for volume estimation. The relationship between the real volumes of

Fig. 15. Selection of L\*a\*b\* sample points at a quice by using colourmeter (Beyaz et al., 2011).

**5.1.1 Volume determination of Kahramanmaras red pepper (***Capsicum annuum* **L.) by** 

The size of an agricultural product is an important parameter to determine fruit growth and quality. It can be used to determine the optimum harvest time as a maturity index. In this study, the image analysis method was tested on Kahramanmaras red pepper (*Capsicum annuum* L.) which may have a non uniform shape. For this purpose; the front, top and left side of each pepper was taken into account for evaluations and projection areas. The effect of each image and image combination has been used to determine the volume of peppers. The regression coefficients between the projection areas and volume values have also been assessed for volume estimation. The relationship between the real volumes of

Fig. 14. Sample colour measurement of an apple.

**using image analysis technique** 

Kahramanmaras red peppers and the front projection area values (AF) was pointed as a graph at Fig. 16. Similarly top projection area values were presented at Fig. 17. However left projection areas values and the estimation equation can be seen in Fig. 18 (Beyaz et al., 2009d).

Fig. 16. The regression equation and graph obtained by using AF projection area (Beyaz et al., 2009d).

According to this assessment, the real volume value which was obtained from the front projection area shows the regression coefficient has reached 49.9%. The regression coefficient obtained from estimation equations by using of the top projection area was 66.6% (Fig. 17.).The regression coefficient obtained by using left projection area was 63.6% (Fig. 18.).

Fig. 17. The regression equation and graph obtained by using AT projection area (Beyaz et al., 2009d).

Machine Vision Measurement Technology and Agricultural Applications 149

Fig. 20. shows the regression equation graph which was determined from total value of the

Real Volume Value (ml)= - 36,95 + 1,740 (AF+AL)

S 18,5312 R-Sq 59,9% R-Sq(adj) 57,7%

S 15,7518 R-Sq 71,1% R-Sq(adj) 69,4%

**AF+AL (cm2)**

Fig. 20. The regression equation and graph obtained by using AF + AL projection areas

**AF+AT (cm2)**

Fig. 21. The regression equation and graph obtained by using AF + AT projection areas

Fig. 22. shows estimated regression graph obtained from sum of three (front, left and top) projections area values and also describes the regression coefficient 73.9%. This value for the

60 70 80 90 100 110 120

It can be seen in Fig. 20., the regression coefficient was 59.9%. Fig. 21. shows the sum of the regression graph. Estimation coefficient was obtained as 71.1% depending on the regression

Real Volume Value (ml) = - 54,67 + 1,475 (AF+AT)

50 60 70 80 90

front and left projection areas.

**Real Volume Value (ml)**

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

150

125

100

75

50

regression gave the second highest value.

(Beyaz et al., 2009d).

equation.

150

125

100

75

50

Fig. 18. The regression equation and graph obtained by using AL projection area (Beyaz et al., 2009d).

When the volume estimation obtained from one projection area, top projection area values has the highest regression coefficient (66.6%). The volume estimation has been examined by making double groups from these three projection areas. Fig. 19. represents the regression graph which is obtained from the sum of top and left projection areas. Estimation equation has reached a regression coefficient 74.7% depending on this assessment.

The most appropriate estimation formula has been calculated from the top and the left projection area. The following equation is the most appropriate equation (Beyaz et al., 2009d):

Real Volume Value (ml) =-47,29+2,132 (AT+AL)

Fig. 19. The regression equation and graph obtained by using AT + AL projection areas (Beyaz et al., 2009d).

Real Volume Value (ml) = - 26,58 + 5,963 (AL)

S 17,6677 R-Sq 63,6% R-Sq(adj) 61,6%

S 14,7205 R-Sq 74,7% R-Sq(adj) 73,3%

**AL (cm2)**

Fig. 18. The regression equation and graph obtained by using AL projection area

has reached a regression coefficient 74.7% depending on this assessment.

15,0 17,5 20,0 22,5 25,0 27,5

When the volume estimation obtained from one projection area, top projection area values has the highest regression coefficient (66.6%). The volume estimation has been examined by making double groups from these three projection areas. Fig. 19. represents the regression graph which is obtained from the sum of top and left projection areas. Estimation equation

The most appropriate estimation formula has been calculated from the top and the left projection area. The following equation is the most appropriate equation (Beyaz et al.,

Real Volume Value (ml) = - 47,29 + 2,132 (AT+AL)

**AT+AL (cm2)**

Fig. 19. The regression equation and graph obtained by using AT + AL projection areas

40 50 60 70 80

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

2009d):

150

125

100

75

50

Real Volume Value (ml) =-47,29+2,132 (AT+AL)

150

125

100

75

50

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

Fig. 20. shows the regression equation graph which was determined from total value of the front and left projection areas.

Fig. 20. The regression equation and graph obtained by using AF + AL projection areas (Beyaz et al., 2009d).

It can be seen in Fig. 20., the regression coefficient was 59.9%. Fig. 21. shows the sum of the regression graph. Estimation coefficient was obtained as 71.1% depending on the regression equation.

Fig. 21. The regression equation and graph obtained by using AF + AT projection areas (Beyaz et al., 2009d).

Fig. 22. shows estimated regression graph obtained from sum of three (front, left and top) projections area values and also describes the regression coefficient 73.9%. This value for the regression gave the second highest value.

Machine Vision Measurement Technology and Agricultural Applications 151

classification and packaging parameter. Overall, at irregular shape products such as pepper is expected to be high regression coefficient relationship between volume and weight. However, classification and packaging system by using image analysis techniques according to the quality of the product is also possible. In this regard research has router features. According to the results of image processing method volume of Kahramanmaras red pepper

**5.1.2 Image analyze technique for measurements of apple and peach tree canopy** 

Image analysis measurements and the real manual measurement method had been compared of apple and peach trees canopy volume in this study. The known of the tree canopy volumes are important from the point of views uniform growing, yield estimation, fertilizers and chemical applications. Also this research gives us some information about tree height, skirt height, parallel diameter. And also we can use these parameters for designing harvesting machines. In this research, totally twenty trees which has different canopy heights and volumes have been used. Apple and peach trees randomly have been selected from apple and peach garden belongs to Ankara University Agriculture Faculty. After the taking photographs of all trees by using digital camera, overall canopy height above the ground, canopy diameter parallel to row near ground level, height to point of maximum canopy diameter, height from ground to canopy skirt data have been obtained from these digital images. Apple and peach canopy volume have been calculated by using a formula that is named Albrigo from the prolate spheroid canopy volume values. The real values and image analysis measurements have been provided dimensions for computing the canopy volume where as image analysis measurements gave information that could be used to compute a image analysis canopy volume index. Tables show manual measurements of the 10 trees for each tree varieties and their corresponding canopy volumes, which were computed using prolate spheroid canopy

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,67 1,65 0,67 1,36 2,41 2 2,76 1,48 0,46 2,43 8,68 3 2,65 1,61 0,62 1,73 3,95 4 2,99 1,57 0,70 1,90 5,14 5 2,69 1,58 0,56 2,2 6,68 6 2,66 1,62 0,87 2,49 7,02 7 2,80 1,57 0,64 2,16 6,40 8 2,45 1,58 0,64 2,31 6,36 9 2,45 1,71 0,53 1,6 3,36 10 2,66 1,60 0,56 1,78 4,34 **Min** 2,45 1,48 0,46 1,36 2,41 **Max** 2,99 1,71 0,87 2,49 8,68 **Mean** 2,69 1,60 0,63 2,00 5,43 **S.D.** 0,53 0,06 0,11 0,38 1,92 Table 1. Two dimensions and canopy volumes calculated using prolate spheroid canopy

volume formula from real tree measurements at apple trees (Beyaz et al., 2009c).

seems to be appropriate for volume estimation (Beyaz et al., 2009d).

volume formula (Table 1- 4) (Beyaz et al., 2009c).

**volume** 

Fig. 22. The regression equation and graph obtained by using AF + AT + AL projection areas (Beyaz et al., 2009d).

The volume estimation with two projection areas, the highest rate of regression estimation value was 74.7% which obtained from the sum of the top and left projection areas. It can be explained by the front view of Kahramanmaras red peppers is more non uniform than top view.Sum of left-top projection area equation which gave the highest regression coefficient was used for estimate volume values. Then relationship between the real volume of peppers and estimated volume values has been compared. The results have been showed in Fig. 23 (Beyaz et al., 2009d).

Fig. 23. Comparison of the actual volume and regression equation estimation results by using sum of AT + AL projection areas (Beyaz et al., 2009d).

It has been pointed that Fig. 23 the regression coefficient values between the real peppers volume value and the estimated volume values had been found as 89.7%. This volume estimation rate can be used as, before the harvest as a maturity index, after the harvest as a

Real Volume Value (ml) = - 57,34 + 1,256 (AF+AT+AL)

S 14,9621 R-Sq 73,9% R-Sq(adj) 72,4%

S 10,3641 R-Sq 89,7% R-Sq(adj) 89,2%

**AF+AT+AL (cm2)**

**Estimated Volumes (ml)**

50 75 100 125 150

Fig. 23. Comparison of the actual volume and regression equation estimation results by

It has been pointed that Fig. 23 the regression coefficient values between the real peppers volume value and the estimated volume values had been found as 89.7%. This volume estimation rate can be used as, before the harvest as a maturity index, after the harvest as a

80 90 100 110 120 130 140 150

Fig. 22. The regression equation and graph obtained by using AF + AT + AL projection areas

The volume estimation with two projection areas, the highest rate of regression estimation value was 74.7% which obtained from the sum of the top and left projection areas. It can be explained by the front view of Kahramanmaras red peppers is more non uniform than top view.Sum of left-top projection area equation which gave the highest regression coefficient was used for estimate volume values. Then relationship between the real volume of peppers and estimated volume values has been compared. The results have been showed in Fig. 23

Real Volumes (ml)= 0,977 + 1,201 Estimated Volumes

**Real Volume Value (ml)**

(Beyaz et al., 2009d).

(Beyaz et al., 2009d).

**Real Volumes (ml)**

200

175

150 125

100

75 50

using sum of AT + AL projection areas (Beyaz et al., 2009d).

150

125

100

75

50

classification and packaging parameter. Overall, at irregular shape products such as pepper is expected to be high regression coefficient relationship between volume and weight. However, classification and packaging system by using image analysis techniques according to the quality of the product is also possible. In this regard research has router features. According to the results of image processing method volume of Kahramanmaras red pepper seems to be appropriate for volume estimation (Beyaz et al., 2009d).

#### **5.1.2 Image analyze technique for measurements of apple and peach tree canopy volume**

Image analysis measurements and the real manual measurement method had been compared of apple and peach trees canopy volume in this study. The known of the tree canopy volumes are important from the point of views uniform growing, yield estimation, fertilizers and chemical applications. Also this research gives us some information about tree height, skirt height, parallel diameter. And also we can use these parameters for designing harvesting machines. In this research, totally twenty trees which has different canopy heights and volumes have been used. Apple and peach trees randomly have been selected from apple and peach garden belongs to Ankara University Agriculture Faculty. After the taking photographs of all trees by using digital camera, overall canopy height above the ground, canopy diameter parallel to row near ground level, height to point of maximum canopy diameter, height from ground to canopy skirt data have been obtained from these digital images. Apple and peach canopy volume have been calculated by using a formula that is named Albrigo from the prolate spheroid canopy volume values. The real values and image analysis measurements have been provided dimensions for computing the canopy volume where as image analysis measurements gave information that could be used to compute a image analysis canopy volume index. Tables show manual measurements of the 10 trees for each tree varieties and their corresponding canopy volumes, which were computed using prolate spheroid canopy volume formula (Table 1- 4) (Beyaz et al., 2009c).


Table 1. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formula from real tree measurements at apple trees (Beyaz et al., 2009c).

Machine Vision Measurement Technology and Agricultural Applications 153

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,54 1,72 0,33 3,15 15,08 2 2,77 1,51 0,38 3,17 15,54 3 2,77 1,52 0,64 2,68 9,66 4 1,86 1,04 0,49 1,25 1,34 5 2,52 1,56 0,38 2,48 8,78 6 2,49 1,25 0,47 3,38 14,40 7 1,56 1,01 0,31 1,57 2,06 8 2,22 1,04 0,44 1,16 1,46 9 1,41 0,8 0,3 2,07 3,04 10 1,39 0,87 0,3 1,19 1,01 **Min** 1,39 0,8 0,3 1,16 1,01 **Max** 2,77 1,72 0,64 3,38 15,54 **Mean** 2,153 1,23 0,4 2,21 7,24 **S.D.** 0,53 0,32 0,11 0,88 6,15 Table 4. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at peach trees (Beyaz et al., 2009c).

Table 1. and Table 3. shows that skirt height for most trees was about 0,63 m at apple trees and 0,43 m at peach trees respectively. Tables also suggest that the difference in volumes for tree each 10 trees. That difference was because the rest of the trees had almost the same parallel (D) diameters. Possibly the captured image measurements provide the easy canopy

Real tree volumes and tree image volumes regressions have presented in Fig. 24. and 25. Regression coefficient have been calculated as 95,6% for apple tree volume and 96,6% for

Real Apple Trees Volumes = 0,3239 + 0,9316 Apple Tree Image Volumes

S 0,486189 R-Sq 95,6% R-Sq(adj) 95,2%

**Apple Tree Image Volumes**

Fig. 24. Real apple tree volumes and apple tree image volumes regression table

2 3 4 5 6 7 8 9 10

volume estimation because of fast tree dimensions.

peach tree volume.

(Beyaz et al., 2009c).

**Real Apple Tree Volumes**


Table 2. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at apple trees (Beyaz et al., 2009c).


Table 3. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from real tree measurements at peach trees (Beyaz et al., 2009c).

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,75 1,51 0,67 1,36 2,41 2 2,76 1,54 0,58 2,58 9,26 3 2,34 1,60 0,48 1,66 3,48 4 2,88 1,68 0,66 2,05 6,00 5 2,58 1,40 0,46 2,21 6,62 6 2,53 1,68 0,87 2,49 6,70 7 2,57 1,44 0,64 2,16 5,68 8 2,32 1,41 0,64 2,31 5,76 9 2,22 1,70 0,46 1,60 3,18 10 2,87 1,82 0,48 1,78 5,07 **Min** 2,22 1,40 0,46 1,36 2,41 **Max** 2,88 1,82 0,87 2,58 9,26 **Mean** 2,58 1,58 0,59 2,02 5,42 **S.D.** 0,53 0,14 0,13 0,40 2,00 Table 2. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at apple trees (Beyaz et al., 2009c).

**Tree ID Ht (m) Hc (m) Hs (m) D (m) PSCV (m3)**  1 2,44 1,76 0,40 2,80 11,16 2 2,66 1,45 0,50 3,00 12,41 3 2,70 1,40 0,58 2,58 8,81 4 1,72 1,20 0,60 1,07 0,85 5 2,59 1,55 0,43 2,60 9,62 6 2,35 1,29 0,60 3,20 11,22 7 1,54 1,04 0,39 1,53 1,80 8 2,36 1,08 0,46 1,19 1,63 9 1,40 1,00 0,22 1,29 1,36 10 1,44 0,92 0,28 1,26 1,22 **Min** 1,40 0,92 0,22 1,07 0,85 **Max** 2,70 1,76 0,60 3,20 12,41 **Mean** 2,12 1,27 0,45 2,05 6,01 **S.D.** 0,53 0,27 0,13 0,85 4,98 Table 3. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from real tree measurements at peach trees (Beyaz et al., 2009c).


Table 4. Two dimensions and canopy volumes calculated using prolate spheroid canopy volume formulae from captured image measurements at peach trees (Beyaz et al., 2009c).

Table 1. and Table 3. shows that skirt height for most trees was about 0,63 m at apple trees and 0,43 m at peach trees respectively. Tables also suggest that the difference in volumes for tree each 10 trees. That difference was because the rest of the trees had almost the same parallel (D) diameters. Possibly the captured image measurements provide the easy canopy volume estimation because of fast tree dimensions.

Real tree volumes and tree image volumes regressions have presented in Fig. 24. and 25. Regression coefficient have been calculated as 95,6% for apple tree volume and 96,6% for peach tree volume.

Fig. 24. Real apple tree volumes and apple tree image volumes regression table (Beyaz et al., 2009c).

Machine Vision Measurement Technology and Agricultural Applications 155

damage values 0.97. Total yield loss and topping losses differences related to harvest

Table 5. Descriptive statistics of measured surface area values and image processing surface

**Image processing percentage** 

Mechanical 2.7 0 0.1 0.27 0 Semi-hyd. 18.6 2.32 0.1 1.86 0.23 Full-hyd. 5.01 2.32 0.1 0.50 0.17

Mechanical 12.1 10.81 0.1 1.21 1.08 Semi-hyd. 27.56 25.58 0.1 2.75 2.55 Full-hyd. 26.59 25.58 0.1 2.65 2.83

Mechanical 31.64 29.73 0.05 1.58 1.48 Semi-hyd. 5 30.23 0.05 0 1.51 Full-hyd. 33.33 30.23 0.05 1.66 0

Mechanical 45.46 44.54 0 0 0 Semi-hyd. 48.84 41.86 0 0 0 Full-hyd. 35.07 41.86 0 0 1.66

Mechanical 8.1 9.51 0.1 0.81 0.95 Semi-hyd. 0 0 0.1 0 0 Full-hyd. 0 0 0.1 0 0

Mechanical 0 5.40 0.05 0 0.27 Semi-hyd. 0 0 0.05 0 0 Full-hyd. 0 0 0.05 0 0

Table 6. Topping yield losses related to harvest machines (Beyaz et al., 2009b).

**Percentage** 

**Variable Sugar Beet Harvest Machines Mean SE Mean** 

Mechanical 685.0 161.0 Semi-hydraulic 598.0 143.0 Full-hydraulic 542.2 68.5

Mechanical 540.0 130.0 Semi-hydraulic 439.0 106.0 Full-hydraulic 483.3 63.1

> **Loss factor**

**Yield loss in each group** 

**Image processing yield loss** 

machines are given in Table 6 (Beyaz et al., 2009b).

Measured surface area values (mm2)

Image processing surface area values

**Sugar beet harvest machines** 

area values (Beyaz et al., 2009b).

(mm2)

**Topping quality class** 

**1** 

**2** 

**3** 

**4** 

**5** 

**6** 

Fig. 25. Real peach tree volumes and peach tree image volumes regression table (Beyaz et al., 2009c).

#### **5.1.3 Determination of sugar beet topping slice thickness by using image analysis technique**

Turkey is one of the important sugar beet producers in the world. Turkey has produced 15 488 332 tones sugar beet from 3 219, 806 ha production area and also produced 2 061 000 tones sugar in 2008. Sugar beet harvesting by machine is general in Turkey. During mechanical harvesting several mechanical loads have caused skin and tissue damages of sugar beet. The damages of skin and tissue at sugar beets results in quantitative and qualitative losses. After harvesting, comparisons of the quality of sugar beets as good and bad are important in factory entrance terms of sugar losses. In this study widely used three types of harvesters have been operated. One of these machines is full hydraulic sugar beet harvest machine with arrangeable depth and row, second one is semihydraulic sugar beet harvest machine and the last one is full mechanic sugar beet harvest machine. Harvesters have been tried at the same field conditions during September-October months of 2009. Performance values have been obtained from three different evaluation methods. These methods are topping quality determination, the determination of sugar beet injury rate and the soil removal rate of the sugar beet. The determinations of these factors are important to obtain the optimum harvest performance. Image process and analysis methods have been used for evalutions. Descriptive statistics of image processing and measured damaged surface area features are given in Table 5 (Beyaz et al., 2009b).

Correlation coefficients between image processing and measured values at sugar beet head diameter have been determined as 0.98, between length values 0.96 and between surface

Real Peach Tree Volumes = 0,2372 + 0,7918 Peach Tree Image Volumes

S 0,913531 R-Sq 96,6% R-Sq(adj) 96,3%

**Peach Tree Image Volumes**

Fig. 25. Real peach tree volumes and peach tree image volumes regression table

**5.1.3 Determination of sugar beet topping slice thickness by using image analysis** 

Turkey is one of the important sugar beet producers in the world. Turkey has produced 15 488 332 tones sugar beet from 3 219, 806 ha production area and also produced 2 061 000 tones sugar in 2008. Sugar beet harvesting by machine is general in Turkey. During mechanical harvesting several mechanical loads have caused skin and tissue damages of sugar beet. The damages of skin and tissue at sugar beets results in quantitative and qualitative losses. After harvesting, comparisons of the quality of sugar beets as good and bad are important in factory entrance terms of sugar losses. In this study widely used three types of harvesters have been operated. One of these machines is full hydraulic sugar beet harvest machine with arrangeable depth and row, second one is semihydraulic sugar beet harvest machine and the last one is full mechanic sugar beet harvest machine. Harvesters have been tried at the same field conditions during September-October months of 2009. Performance values have been obtained from three different evaluation methods. These methods are topping quality determination, the determination of sugar beet injury rate and the soil removal rate of the sugar beet. The determinations of these factors are important to obtain the optimum harvest performance. Image process and analysis methods have been used for evalutions. Descriptive statistics of image processing and measured damaged surface area features are given in Table 5 (Beyaz et al.,

Correlation coefficients between image processing and measured values at sugar beet head diameter have been determined as 0.98, between length values 0.96 and between surface

0 2 4 6 8 10 12 14 16

**Real Peach Tree Volumes**

(Beyaz et al., 2009c).

**technique** 

2009b).

damage values 0.97. Total yield loss and topping losses differences related to harvest machines are given in Table 6 (Beyaz et al., 2009b).


Table 5. Descriptive statistics of measured surface area values and image processing surface area values (Beyaz et al., 2009b).


Table 6. Topping yield losses related to harvest machines (Beyaz et al., 2009b).

Machine Vision Measurement Technology and Agricultural Applications 157

**Golden Delicious Granny Smith Stark Crimson Days ΔE Days ΔE Days ΔE**  1. Day 23.48 1. Day 12.81 1. Day 7.44 2. Day 31.52 2. Day 14.11 2. Day 9.32 3. Day 35.59 3. Day 15.61 3. Day 11.45 4. Day 37.60 4. Day 16.85 4. Day 14.02 5. Day 38.30 5. Day 16.96 5. Day 14.65

**Golden Delicious Granny Smith Stark Crimson** 

**Golden Delicious Granny Smith Stark Crimson** 

**Golden Delicious Granny Smith Stark Crimson** 

Day 1 57.40+0.68 Aa 53.60+1.92 Ab 45.63+1.23 Ac Day 2 51.21+0.94 Ba 53.36+0.63 ABa 44.63+1.22 ABb Day 3 48.44+1.17 Cb 52.69+0.76 ABa 43.49+1.26 BCc Day 4 46.96+1.17 Cb 51.37+0.93 Ba 41.70+1.19 CDc Day 5 46.49+1.30 Cb 51.37+0.94 Ba 41.31+1.17 Dc

Day 1 -3.02+0.58 Cb -12.66+0.81 Bc 20.72+0.95 Aa Day 2 0.56+0.69 Bb -11.66+0.75 Bc 19.22+0.92 Ba Day 3 1.95+0.69 Ab -10.10+1.15 Ac 17.88+1.01 Ca Day 4 2.90+0.61 Ab -10.02+0.98 Ac 16.61+1.09 Da Day 5 3.00+0.68 Ab -9.96+0.92 Ac 16.37+1.24 Da

Day 1 35.48+0.54 Aa 31.00+0.15 Ab 22.28+1.25 Ac Day 2 31.77+0.84 Ba 29.71+0.62 ABa 21.53+1.24 ABb Day 3 28.87+1.12 Ca 29.25+0.76 Ba 20.33+1.28 BCb Day 4 27.90+1.27 CDa 28.67+0.94 Ba 18.91+1.27 CDb Day 5 27.27+1.44 Da 28.61+0.90 Ba 18.46+1.39 Db

Table 8. Colour value change for each apple varieties (Beyaz et al., 2010).

**Days Variable** 

Table 9. The interaction between the apple varieties and days for L\* value

**Days Variable** 

Table 10. The interaction between the apple varieties and days for a\* value

**Days Variable** 

Table 11. The interaction between the apple varieties and days for b\* value

(Beyaz et al., 2010).

(Beyaz et al., 2010).

(Beyaz et al., 2010).

As shown in Table 6., the total topping yield loss and image processing measurement values have been evaluated. This results respectively, at the mechanical harvesting machine 3.87% and 3.78%, at the semi-hydraulic machine 4.86% and 4.29%, at the fully hydraulic machine 4.81% and 4.66%. According to IIRB ideal topping quality is group is 4. According to the total topping yield loss values, the best machine is mechanical sugar beet harvesting machine (Beyaz et al., 2009b).
