Application of Neural Networks and Image ...

Erwerbs-Obstbau DOI 10.1007/s10341-012-0162-y

O r i g i n a l A rt i c l e

Application of Neural Networks and Image Visualization  for Early Forecast of Apple Yield Rozman Črtomir · Cvelbar Urška · Tojnko Stanislav · Stajnko Denis · Pažek Karmen · Martin Pavlovič · Vračko Marjan

Received: 26 April 2012 / Accepted: 3 May 2012 © Springer-Verlag 2012

Abstract Early information on yield has a special importance in the intensive apple production. Since the majority of older forecast methods are labor, time, organization and cost intensive a hybrid model based on image analysis and neural network was developed. From the end of fruit thinning in June till harvesting digital images of 120 trees of yellow-skin ‘Golden Delicious’ (four times) and 120 trees of red-skin ‘Braeburn’ (five times) were captured from intensive orchards. Firstly, each image was processed by image analysis algorithm to receive the data on number of fruits and a yield forecast, for each sampling period separately, which served as the input information for modeling the yield with the artificial neural network (ANN). The forecast of the hybrid method showed a higher accuracy than the image analysis for both varieties, since the new procedure managed to increase the correlation between the forecasted and weighed yield from 0.73 to 0.83 for ‘Golden Delicious’ and from 0.51 to 0.78 for ‘Braeburn’. The standard deviation/image was decreased from 4.79 to 2.83  kg for ‘Golden Delicious’ and from 3.64 to 2.55  kg for ‘Braeburn’. To introduce the new method in practice, additional tests on various locations including all important apple varieties are recommended.

Keywords  Apple · Image analysis · Neural networks · Forecasting

Anwendbarkeit neuronaler Netze und der Bildanalyse zur frühzeitigen Vorhersage des Ertrages von Äpfeln

C. Urška Fruit production Blanca, Blanca 7, 8283 Sevnica, Slovenia

Zusammenfassung  Frühzeitige Vorhersagen über den zu erwartenden Ertrag von Apfelanlagen haben verschiedene Vorteile. Da die Mehrheit der älteren Prognose-Methoden arbeits-, zeit-, organisations- und kostenintensiv sind, wurde ein Hybrid-Modell, basierend auf der Bildanalyse und neuronalen Netzen entwickelt und getestet. Nach der Fruchtausdünnung im Juni bis zur Ernte wurden digitale Bilder von je 120 Bäumen der Sorten ‘Golden Delicious’ und ‘Braeburn’ erfasst. Zunächst wurde jedes Bild durch einen Bild-Analyse-Algorithmus verarbeitet, um die Daten über die Anzahl von Früchten und einer Ertragsvorhersage für jede Abtastperiode getrennt zu empfangen, die als Eingangsinformationen für die Modellierung des Ertrags mit Hilfe von künstlichen neuronalen Netzwerks (ANN) benutzt wurden. Die Prognose des Hybridverfahrens zeigte im Vergleich zur Bildanalyse bei beiden Sorten eine höhere Genauigkeit. Der Zusammenhang zwischen dem prognostizierten und tatsächlichen Ertrag wurde von r = 0.73 auf r = 0.83 bei ‘Golden Delicious’ und von r = 0.51 auf r = 0.78 bei ‘Braeburn’ erhöht. Die Standardabweichung/Bild wurde von 4,79 auf 2,83  kg bei ‘Golden Delicious’ und von 3,64 bis 2,55 kg bei ‘Braeburn’ verringert. Vor Einführung der neuen Methode in die Praxis, sind zusätzliche Tests an verschiedenen Standorten mit allen wichtigen Apfelsorten erforderlich.

V. Marjan National Institute of Chemistry Slovenia, Hajdrihova 19, 1001 Ljubljana, Slovenia

Schlüsselwörter  Apfel · Bild-Analyse · Neuronale Netze · Ertragsprognose

R. Črtomir () · T. Stanislav · S. Denis · P. Karmen · M. Pavlovič Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoče, Slovenia e-mail: [email protected]

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Introduction With the annual yield of round 10 million tons apple represents one of the most important deciduous fruit in EU-27 (WAPA 2010). The early information on apple yield and its quality has an important value for future planning and decision making and furthermore it forms the basis for forecasting orchard economics and expected net returns. According to Hester and Cacho (2003), until now, considerable research has been conducted in order to develop a viable method for early apple yield prediction. Although newer methods have been developed, the ‘Bavendorf’ Crop Forecast model (Winter 1986) remains the most important in the middleEurope. It is based on the yield capacity of the observed growing unit (trees, variety, rootstock, orchard age, slope, elevation and area), the fruit-set density of the growing unit in the given year and the average fruit mass at a harvesting date (Winter 1986). But the main disadvantage of the method still remains the time-consuming counting measurements of input parameters, which is unable to predict the future yield in every individual orchard separately and therefore it may result in significant differences between the forecast and harvested yield. As seen from the WAPA annual report for 2011, differences between forecasts and harvested yield varied from − 24.8 to + 30.7 % per hectare in 2010, depending on the apple variety and country growing region (Lieberz 2011). Potential enhancements of widespread forecasting method can be achieved by applying visual techniques to collect sample data. In fruit growing, vision algorithms are commonly used for automatic fruit inspection during sorting processes and for guiding mechanical equipment of harvesting machines or robotic hands (Jimenez et al. 1999; Ye et al. 2007). However, due to the varying orchard conditions the second process is successfully carried out only in experimental cases under controlled lighting conditions. In spite of the varying lighting conditions in the orchard, Stajnko and Čmelik (2005) reported a close correlation between the number of fruits obtained from image analysis and manually counted fruits, while detecting yellow and green—yellow ‘Golden Delicious’ fruit on the sunny side of apple trees in a four-year old orchard. However, it was shown the fruit detection on images captured on the shadow as well sunny side of the tree required artificially light source to sufficiently control illumination for the accurate fruit detection. To overcome the negative effect of lighting on the processing algorithm, Stajnko et al. (2003, 2004) significantly improved the quality of input images by applying thermal camera for capturing images of trees. Described system was demonstrated as a potential method for estimation of number and diameter of apple fruits in an orchard during the growing season as a replacement for tiresome manual measurements and an alternative to other detection

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R. Črtomir et al.

methods. Similar results are reported by Stajnko and Blanke (2011). However, the variability of the results leaves room for further improvements. In the last decade machine learning algorithms (such as artificial neural networks—ANN) was proved in estimating various field and crop conditions from images (Uno et al. 2005). The use of image analysis data as neural network input for soybean leaflet discrimination is also reported by Oide and Ninomiya (2000). In this light Aitkenhead et al. (2003) combine image analysis and artificial intelligence methods for weed and crop discrimination. Similar applications are reported by Grannito et al. (2002), Guyer and Yang (2000) and Nakano (1997). The ability of ANNs to associate complicated spectral information with target attributes without any constraints for sample distribution make them ideal for describing the intricate and complex non-linear relationships which exist between canopy-level spectral signatures and various crop conditions (Mather 2000). ANNs forecasting capability was also described by O’Neal et al. (2002) who compared five data coding types for back-propagation neural networks used to predict maize yield from climatic data. They reported neural nets to have much to offer for predicting future characteristics of crops based on weather and environmental data, and to have an accuracy at least as good as polynomial regression. The use of neural networks for apple output prediction in China was described by Yao and Wang (2007). They used a grey neural network and back propagation neural network and report 98.11–98.45  % prediction accuracy. Similar ANN applications in agriculture have been reported by Esmaeili and Tarazkar (2010), Pandey et al. (2010), Sanzogni and Kerr (2001) and Salehi et al. (1998) while the complete overview of ANN in agro-ecological systems are provided by Schultz et al. (2000). The aim of this paper is to improve image analysis based apple yield prediction with the application of the ANN with training data obtained from image analysis, whereby the number of fruits at different times and weighted actual yield for each image per tree would serve as training parameters. We assume that ANN can produce further improvement in comparison with previously applied image analysis based methods. Methodology Experimental Design and Image Analysis During the vegetation period June-September, 120 apple trees (Malus domestica Borkh.) of ‘Golden Delicious’ and 120 samples of ‘Braeburn’ were examined from June to October in the commercial plantations ‘Blanca’ (lat. 46°02’ N, long. 15°12’ E). Four year old apple trees were trained as

Application of Neural Networks and Image Visualization for Early Forecast of Apple Yield Table 1 Common terms in the field of neural networks and their equivalent in statistics (Tu 1996) Neural networks Statistics Input

Independent (predictor) variable

Output

Dependent (outcome) variable, predicted value

Connection weights

Regression coefficients

Bias weight

Intercept parameter

Error

Residuals

Learning, training

Parameter estimation

Training case, pattern

Observation

Table 2 Network architectures used for both analyzed varieties Network parameter ‘Golden Delicious’ ‘Braeburn’ Architecture Test set absolute error Akaike Information criterion

[4-6-1]

[5-14-1]

3.8040

3.1129

− 0.0067

− 0.0104

r2 for test set

0.7198

0.6367

Correlation coefficient r for test set

0.8484

0.7979

Train (learning) set error

5.4897

2.2696

a super spindle and planted at a spacing of 2.8 × 0.7 m. All trees were grafted on the M9 rootstock and the rows were oriented from East-North to West-South. After fruit thinning in June digital RGB images of ‘Golden Delicious’ were captured four times and ‘Braeburn’ five times, respectively (Tables 1 and 2). Each time images were captured using a flesh mode on the sunny side of trees from the distance of 2.0 m at an angle of 90° to the planting row. Concurrently, all fruits were manually counted on each photographed tree.

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Simultaneously with the last capturing date the fruits were picked up and weighted. For counting apple fruits and forecasting the yield fivestep fruit detection RGB algorithm was applied described in details by Stajnko and Čmelik (2005). The objects corresponding to apples were counted and the longest segment of each object was measured separately. The yield was estimated by applying of derived Mitchell’s (1986) equation:



YGolden =



YBreaburn =

N · 0,504 · D 2,9602 106 N · 0,291 · D 2,9602 106

(1)

(2)

in which YGolden and YBraeburn represent the yield per tree (kg), N the number of fruits per tree and D the expected fruit diameter at harvest (mm). A sample of program control panel called ‘GOLDENhectareONE.vi’ is presented in the Fig. 1. ANN Forecast The ANN is an information processing system that has certain performance characteristics in common with biological neural networks. ANNs have been developed as generalizations of mathematical models of human cognition or neural biology (Zupan 1994; Zupan and Gasteiger 1999). An artificial neural network (or simply a neural network) is a biologically inspired computational model which consists of processing elements (called neurons) and connections between them with coefficients (weights) bound to the connections, which constitute the neuronal structure, and training and recall algorithms attached to the structure. Neural net-

Fig. 1 Control panel of fruit detection algorithm

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4 Fig. 2 A processing unit— neuron (Yu 2000)

R. Črtomir et al. x0 x1 ... xn

θj

wj0 wj1 wjn

Σ

j a j

g(aj)

zj

n

aj = ∑ wji xi + θj zj = g(aj) i=1

Figure legend: zj = g(aj) n

aj = ∑wji xi + θj i =1

Where: wji − weights θj − bias xi − input aj − net input g − activation function zj − net output

works are called connectionist models because of the main role of the connections in them. The connection weights are the “memory” of the system (Kasabov 1996). Information processing in ANN occurs at neurons (neuron j in Fig. 2). Signals between the neurons are passed over connection links and each connection link has an associated weight (wj0…wjn), and bias (bias θj acts like a weight on a connection from a unit with a constant activation of 1) which (in a typical neural net) multiplies the signal transmitted. The weight represents the numeric value associated with a connection between units in neural network. This value is dynamically changed during neural network training and determines how much the output of one neuron is fed to the input of another. The weights changes are determined with a training algorithm (a set of rules or equations to train a neural network to solve a specific problem). Each neuron applies an activation function—usually non-linear—to its net input (sum of weighted input signals and the bias; aj) to determine its output signal. The ANN is characterised by its pattern of connections (network architecture), its method of determining weights on the connections (training or learning algorithm) and its activation function g(aj) (Fausset 1994). A typical ANN consists of series of units that are arranged in three layers (input, hidden, output) (see Fig. 2). The input units are where the values of the predictor variables (eg. x1…xn) are presented to the network while the output units (zj) represent the predicted network output. The hidden units (as internal weight structure) allow the ANN to model complex non-linear relationships between predictor variables and the outcome (Tu 1996). Neural networks are not constrained by a predefined mathematical relationship between dependent and independent variables, and have the ability to model any arbitrarily complex relationship (White 1989). According to Tu (1996), developers of ANN prediction models do not require formal

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training in statistical methodology, and models can be developed by users with a minimum of theoretical knowledge. A comparison of neural network and corresponding statistic terms are described in Table 1. The neural network developed for yield prediction was conducted using Allyda Neurointelligence 2.07 commercial software. The data of number of fruits obtained by image analysis at each sampling date (network input) and actual weighted yields (network output or target variable) were used as network training data. With the use of neural network software (such as Alyuda NeuroIntelligence 2.07 used in this particular case, Alyuda Research 864 Terrace Dr., Los Altos, CA 94024, USA) testing of different network architectures and training procedures (algorithms) was executed. The data of image analysis and actual yields (weighted yield per each tree) was used as ANN training data where the yield was designated as output (target) variable. The data set of 120 objects (observed trees) was divided into training set of 82 objects (68 %), validation set and test set of 19 objects (16 %) each of them. The training set was used to build models, which were tested with the validation set. The criterion to select the best model was correlation coefficient r between predicted and measured values for validation set. When the model was selected it was again tested with the test set. The networks architectures used for individual varieties are shown in Table 2. An error value indicates the “quality” of a neural network training and is calculated by subtracting the current output values with the target output values of the neural network. The smaller the network error is, the better the network had been trained. Akaike Information criterion (AIC) is used to compare different networks with different weights (hidden units). With AIC used as fitness criteria during architecture search, simple models are preferred to complex networks if the increased cost of the additional weights (hidden units) in the complex networks do not decrease the network error. A square of correlation coefficient r2 is a statistical measure showing how well the network outputs actual target values. The closer is r2 to 1.0 the better is the network’s performance. It is calculated according to the Equation 3.



r=

n 

i=1 n 

i=1

¯ i − Y¯ ) (Xi − X)(Y

¯ 2 (Xi − X)

n 

i=1

(3)

2 (Yi − Y¯ )

Where X and Y are two variables and n is the number of objects.

Application of Neural Networks and Image Visualization for Early Forecast of Apple Yield

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Results

Yield Forecast by Image Analysis

Number of Fruits Detected by Image Analysis

The correlation coefficients between the yield per tree estimated by image analysis and weighing for the particular sampling date are shown in Tables  5 and 6. There are no data of total weight ‘manually’ before harvest, because the yield was naturally weighted only once (harvest). As seen, in ‘Golden Delicious’ variety the coefficients varied from 0.58 in June to 0.73 at harvest and showing the highest accuracy 1 month before harvest (0.78). On the other hand, one weighed yield per tree was proved to be very accurate by the difference of only 0.21 kg, which is also very promising for the horticultural practice. Also for the variety ‘Braeburn’ (Table 6) the coefficients increased from 0.22 in June to 0.57 in September, but then they have slightly fallen at harvest (0.51). Also the weighed yield per tree (6.77 kg) differed significantly from the forecast, which really required additional improvements with ANN.

The estimated number of apple fruits per tree by the image analysis as well as manually counted fruits is represented in the Table 3 (‘Golden Delicious’) and Table 4 (‘Braeburn’). The number of fruits was slightly decreasing after June’s fruit thinning till the harvest, despite all agro technical measures provided by the owners, mainly due to insufficient water supply. In Table  3 the number of fruits counted by image analysis (I), manually counted fruits, correlation (r) and standard deviation per image (SEE) is shown. As seen in Table 3, the established correlation between the image analysis and manually counted varied for ‘Golden Delicious’ between 0.57 (June) and 0.72 (harvest) respectively, whereby it was the closest in August (0.77). On the other side, for ‘Braeburn’ the lowest correlation was measured in June (0.23) and the highest (0.52) 1 month before harvest (Table 4). The accuracy of fruit detection at different dates was the same as reported by Stajnko et al. (2004) and was proved to be precise enough for predicting the number of fruits prior harvesting.

ANN Yield Forecast The 4-6-1 network architecture was chosen as the best model for Golden delicious data. Figure 3 shows network training and testing procedure and comparison between predicted and actual yield on training set.

Table 3 Total number of ‘Golden Delicious’ apple fruits detected by image analysis and manually counted (n = 120 trees) r SEE/ Diffe- Mean MaImage Date image difference Analy- nually rence (I-M) (I-M) (M) sis (I)

Table 5 Total estimated and weighed yield (kg/tree) of ‘Golden Delicious’ (n = 120 trees) r SEE/ Image Manually Differen- Image Date image differenAnaly- Weighed ce (I-M) ce (I-M) sis (I) (M)

June 21

  5741

13404

− 7513

− 62,61

0.57

16.11

June 21

  889

July 12

12015

13344

− 1239

− 10,33

0.73

32.85

July 12

August 8

14634

13280

1380

   11,50

0.77

34.25

/

− 1013.25

− 0.52

0.58 2.46

1672

/

August 8 2011

/

  − 230.25

− 0.09

0.74 4.29

   108.75

   0.10

0.78 4.45

   279.75

  0.21

0.73 4.79

Septem16260 13254 3006    25,05 0.72 38.98 ber 12 I image analysis based forecasts, M manually counted fruits, SEE standard derivation

Septem- 2182 1902.25 ber 12 SEE standard derivation

Table 4 Total number of ‘Braeburn’ apple fruits detected by image analysis and manually counted (n = 120 trees) r SEE/ Mean DiffeMaImage Date image differenAnaly- nually rence ce (I-M) (I-M) (M) sis (I)

Table 6 Total estimated and weighed yield (kg/tree) of ‘Braeburn’ (n = 120 trees) r SEE/ Image Manually DiffeImage Date image DifferenAnaly- Weighed rence ce (I-M) (I-M) (M) sis (I)

June 6

  8380

13330

− 4950

− 41.25

0.23

18.13

June 6

1315

/

− 1303

− 10.86

0.22

July 12

  7069

13259

− 6190

− 51.58

0.36

21.27

July 12

1219

/

− 1399

− 11.66

0.40

3.41

August 8

15761

13278

2483

20.69

0.34

30.75

August 8

2416

/

− 202

− 1.68

0.35

4.52

September 3

  9499

13219

− 3720

− 31.00

0.52

20.28

September 3

1600

/

− 1018

− 8.48

0.57

3.22

− 812

− 6.77

0.51

3.64

October 10930 13180 − 2250 − 18.75 0.49 23.45 13 I image analysis based forecasts, M manually counted fruits, SEE standard derivation

October 1806 2618 13 SEE standard derivation

2.83

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R. Črtomir et al.

Fig. 3 Network training and testing for ‘Golden Delicious’

The ANN yield forecast for ‘Golden Delicious’ is represented in Table 7, which shows the difference of − 18.85 kg. The comparison between ANN forecast and image analysis forecast is shown in the Tables 8 and 10. It is clearly seen that the ANN improved the forecast significantly, by increasing the correlation coefficients from 0.73 (Table  5) to 0.83 (Table  7) as well decreasing the SEE/image from 4.79 to 2.83 kg. It was shown that the forecasting of a yield with the hybrid model, which consists of ANN and imaging processing, outperforms the analysis performed solely by imaging processing. The 5-14-1 network architecture was chosen as best network for ‘Braeburn’ data. Figure 4 shows network training Table 7 ANN forecast for ‘Golden Delicious’ (n = 120) r SEE (kg/ Total weighed Total foreDifference (W-F) image) (W) (kg) cast (F) (kg) 1902.24 1921.09 − 18.85 0.83 2.83 W weighed yield, F ANN forecasted yield, SEE standard derivation

Table 8 Comparison ‘Golden Delicious’ Date Image analysis forecast (I) (kg)

of the image analysis and ANN forecast for ANN forecast (kg)

Yield weighed (Y) (kg)

Difference (I-Y) (kg)

Difference (I-ANN) (kg)

June 21

  889

/

/

− 1013.0

July 12

1672

/

/

    − 230.30

August 8

2011

/

/

  108.75

September 12

2182

1921.098

1902.25

  279.75 18.8

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procedure and comparison between predicted and actual yield on training set. The ANN improved the forecast of ‘Braeburn’ even more significantly. As seen from Tables 6 and 9, on one side the correlation coefficients increased from 0.51 to 0.78, while on the other side the SEE/image decreased from 3.64 to 2.55 kg/image. Application of the ANN in the hybrid model additionally improved the image analysis based yield forecasts. In comparison to image analysis, a higher correlation coefficient, lower standard deviation and better index foreTable 9 ANN forecast for ‘Braeburn’ variety (n = 120) Total weighed Total foreDifference r (W) (kg) cast (F) (kg) (W-F)

SEE (kg/ image)

2617,91 2623,39 − 5.48 0,78 2,55 W weighed yield, F ANN forecasted yield, SEE standard derivation

Table 10 Comparison of the image ‘Braeburn’ ANN Image Date forecast analysis (kg) forecast (I) (kg)

analysis and ANN forecast for Yield Weighed (Y) (kg)

Difference (I-Y) (kg)

Difference(IANN) (kg)

June 6

1315

/

/

− 1303

/

July 12

1219

/

/

− 1399

/

August 8

2416

/

/

− 202

/

September 3

1600

/

/

− 1018

17.48

October 13

1806

2623.40

2618.00

− 812

    5

Application of Neural Networks and Image Visualization for Early Forecast of Apple Yield

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Fig. 4 Network training and testing for ‘Braeburn’

casts were found for both varieties. In previous research Stajnko et al. (2008) showed that average image analysis based yield forecasts accuracy index was 1.39 for ‘Braeburn’ and 0.93 for ‘Golden Delicious’. The combined approach (see Tables 7 and 9) improved the accuracy index for ‘Golden Delicious’ to 1.010 and for ‘Braeburn’ to 1.002. Although effort for data manipulation and network training may be significant and time consuming, the ANN in combination with image analysis has the potential to improve early apple yield predictions. Despite some disadvantages (“black box” nature, greater computational burden, proneness to over fitting, and the empirical nature of model development) the hybrid model (image analysis-ANN) approach fulfilled most of our expectations. It is able to simplify fruits and speed counting procedures and ANN can improve predictions in comparison to common algorithms. Furthermore, after further improvements the method could provide accurate apple yield prediction at individual orchard level. Future work should be focused on improving the algorithm in order to achieve synchronization between the data flow and the fruit detection procedure for processing measurements in real-time. All improvements could provide the implementation of our algorithm into advanced methods for predicting future yield, which are nowadays used in the practice, but are time consuming and sample a relatively small part of the apple orchards.

Conclusions In this paper an attempt was made to improve image analysis based apple yield forecasts with application of artificial neural networks (ANN). After performing several ANN training procedures using the number of fruits at four or five different dates obtained by image analysis it can be concluded that the ANN was able to improve yield prediction accuracy for both analyzed varieties: ‘Golden Delicious’ and ‘Braeburn’ using the entire data set of fruit numbers and actual yield counted on four or five different dates. To introduce a hybrid model of image visualization and neural network in the practice, additional tests would be needed on the production plantations, in which all important varieties on different growing areas are included. Thus, we would obtain information, with the help of which the neural network is trained and tested. Later on representative samples, sampling system and size would be determined. The basis for all this would be an up-to-date, consistent and professionally organized register of permanent crops with an objective to make a common forecast for the entire country allowing for ± 5  % discrepancy, and the forecast could be communicated to WAPA organization and to the Statistical Office of the Republic of Slovenia. The results and conclusion of this study provide new possible approach for apple yield predictions before harvesting. Acknowledgements  The funding of this research by The Ministry of Agriculture, Forestry and Food of the Republic of Slovenia, project number No. V4-0339-06, and by the Public Research Agency of the Republic of Slovenia is gratefully acknowledged.

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