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Computers and Electronics in Agriculture31 (2001) 1729

Measuring image analysis attributes andmodelling fuzzy consumer aspects for tomato

quality grading

Gerhard Jahns a, *, Henrik Mller Nielsen b , Wolfgang Paul aa Institute of Biosystems Engineering , Bundesallee 50 , D -38116 Braunschweig , Germany

b Department of Agricultural Sciences , Agro ej 10 , DK -2630 Taastrup , Denmark

Abstract

Quality grading is done by the consumer in a fuzzy way according to his senses sight,touch and smell. Visual appearance is the main source of information and can be brokendown by image analysis to attributes like size, colour, shape, defects and abnormalities.Moreover, these attributes are often correlated with nutritional or other sensual qualityparameters. Starting with such basic quality attributes, with the example of tomatoes areasoning is proposed, mapping various fuzzy consumer aspects to overall quality classes.The objective is to achieve an automatic rating of fruit quality, modelling consumer aspectsand producer needs. Such a mapping of fuzzy image analysis attributes to an overall visualquality reduces destroying tests. The reasoning can easily be rearranged and optimisedaccording to varying consumers expectations. 2001 Elsevier Science B.V. All rightsreserved.

Keywords : Image processing; Fuzzy reasoning; Tomato quality

www.elsevier.com /locate /compag

1. Introduction

Ofcial quality denitions for fruit or vegetables are hardly more than a roughrating on size and colour. Where the USDA grade standard for tomatoes (USDA,1991) species six maturity stages based on the dominating colour of the tomato,the EU standard as described by (AID, 1992) effectively has two stages: green and

* Corresponding author. Tel.: + 49-531-596466; fax: + 49-531-596369.E -mail address : [email protected] (G. Jahns).

0168-1699 /01/$ - see front matter 2001 Elsevier Science B.V. All rights reserved.PI I : S0168-1699(00)00171-X

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G . Jahns et al . / Computers and Electronics in Agriculture 31 (2001) 1729 18

Table 1Quality items as seen by the consumer

Size, colour, defects, maturitySightFirmness, grip, maturityTouchJuiciness, sweetness, sourness, aromaSmell /tasteHope for vitamins, nutrients, valuable elementsNutrientsFear of plant protection agents, nitrate, natural toxinsToxins

red (a Dutch auction colour scale covers 12 colour classes). No objective measuresof shape are given, whereas size grading is done by measuring the largest diameter:3035, 3540, 4047, 4757, 5767, 6782, 82102 and 102 mm and more. Thesize tolerance for each interval is 2 mm. Tomatoes are divided into three classes(class Extra, class I, and class II) based upon the overall state of the tomato(rmness, number and extend of defects or blemishes), but most of these statevariables are not specied in an objective manner. With respect to uniformity:tomatoes from the classes Extra and I should have the same colour and maturity.

So far with ofcial quality denitions. Clearly, the consumer is looking for more.The produce should be appealing by sensual check (sight, touch, organoleptic, etc.),nutritional values should be satised and security factors (residues of fertiliser orplant protection agents as well as natural toxins) must be met. Thus Table 1 wouldbe the ideal denition from the consumers point of view. One has to admit, thatTable 1 contains several fuzzy denitions, items set up irrespective whether thequality characteristics are measurable or not. So the ideal consumer quality has tobe translated into practice. The quality items in Table 1 have to be translated intoeasily measurable factors.

Table 2 is a breakdown of Table 1 relating physical quality parameters to whatinterests the consumer or the grower. Measurements according to Table 2 would bea full scale quality assessment, denitely not manageable in practical production.But with these measurements, correlation between the above parameters can beachieved. Table 3 is a possible reduction of Table 2, relating some of these qualityparameters to image analysis. It is shown later on, that attributes of appearancecorrelate also with quality parameters like rmness, sugar /acid ratio or vitamin C.

Table 2Physical parameters related to the needs of consumers and producers

Quality aspectsPropertiesSensorics

Area, spectral analysis, texture, Weight, maturity, shape, position,Image analysiscurvature greenback, surface defects

Penetrometer Elasticity, hardness skin, maturityE-Modulus, ruptureTaste intensity, aromaHuman team Taste limit

Sugars, vitamin C, BRIX, TOC,Photometrics, Nutritionals, sugar /acid ratio specic ionstest strips NO 3 , K + , Ca 2 +

pH-value, conductivityElectronics Acidity, total salts

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Table 3Quality parameters related to image analysis

Image analysis attributesConsumer aspects

Weight, AreaSpectral analysisMaturity, elasticity, sugar /acid ratio, greenbackTextureSurface defectsCurvatureShape

Finally, a fuzzy rating is recommended as a basis to process such a basicquality parameter set. Here a reduction to image analysis attributes is proposed,but other automatically measurable parameters are easily included. The mainobjective is to reduce quality assessment to few characteristics, which can bemeasured by non contact and non destroying tests, preferably by image process-ing. Only a few and random destroying extra checks are necessary then for

counter checking.

2. Image analysis and quality attributes

A standard colour camera, frame grabber and PC is used for image analysisto measure visible quality parameters. Illumination is done in a half sphere inorder to avoid shadows. Further descriptions may be found in Nielsen and Paul(1995). Here only the results of practical quality assessments are summarised.

2 .1. Size and size distribution

Size is taken here as (1) the largest diameter the major axis and (2) thearea of a tomato viewed from above. The minor axis and perimeter measure areneeded later for computing the shape indices in the following section. The areais computed from the 2D image of a tomato and so an estimate of the tomatosweight /volume is achieved (Fig. 1).

The highest degree of correlation was found between area measured by imageanalysis and weight measured using a scale:

W = 0.0021 A 1.3614 , (R 2 = 0.9955) (1)

where A is the area (mm 2 ) and W is the weight (g) (Eq. (1)). Using the aboverelationship, the mean absolute error of weighing a tomato by determining the

area using image analysis would be 2.06% (approximately 1.8 g for 90 gtomato). Thus, size and size distribution or weight and weight distribution canbe recorded automatically with a high degree of accuracy by image analysis.

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2 .2 . Colour and colour uniformity

The colour of a tomato is an accepted measure for its maturity stage (Sarkar andWolfe, 1985a,b; Choi et al., 1995). The procedure for estimating the maturity stageof a tomato by measuring its colour using image analysis is done by placing thetomato on a small plate having approximately the same luminance as thetomatoes in the illumination chamber and an RGB image is acquired. Usingcolour calibration data, the RGB image, I RGB (i , j ), is converted to a standardised

CIE /XYZ image (Nielsen and Paul, 1995, 1996).The result of computing is either a normalised red /green index or better the

dominating wavelength as maturity index. For some 60 Pannovy tomatoes theresult of the colour discrimination is compared to human grading, as shown in Fig.2. Overlapping is found at maturity stages 68, thus the human grouping of thetomatoes is not perfect.

2 .3 . Shape and cur ature

The global shape measures compactness and eccentricity is estimated from themeasured parameters area, perimeter, major and minor axis as shown in Fig. 3.

This provides a simple method of judging the overall shape of tomatoes.In order to locate and determine the extent of local shape errors, an analysis of the tomato boundary curvature is an efcient solution. The boundary curvature iscomputed from the smoothed version of the contour data (Figs. 4 and 5).

This method clearly detects and locates concavities and convexities, also a notsellable curvature.

Fig. 1. Relationship between area measured by image analysis (blossom end view) and weight measuredusing a scale for tomatoes of different size and maturity.

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Fig. 2. Dominating wavelength as maturity index.

2 .4 . Spots and scars

The colour distribution of a tomato is easily evaluated using, e.g. the dominatingwavelength. The histogram of the dominating wavelength of the pixels of adiscoloured tomato (Figs. 6 and 7) shows a clear threshold value between theyellow spots and the red colours, thus providing a way of detecting colourabnormalities. The histogram method is also easily capable of detecting greenbacktomatoes or tomatoes with bottom-end rot. Either the total spread of the distribu-

Fig. 3. Example of shape sorting based on the calculated compactness and eccentricity values: amisshapen ( C = 0.9725, E = 0.8683) and a perfectly shaped ( C = 0.99807, E = 0.9929) tomato.

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Fig. 4. Tomato with several local shape errors located by analysing the boundary curvature (Fig. 3).

tion or the attribute more than one peak can be taken as a measure for colourabnormalities. Furthermore, scars or injuries can be detected with texture analysis(differences in reection within neighbouring pixels) or black /white discrimination,see Fig. 8. Surface injuries have as a result a discontinuity in reection.

2 .5 . Correlation between optical quality attributes and inner alues

Within different stages of maturity or different groups of constituents, values of quality parameters sometimes develop in parallel. It is interesting to look atcorrelation between these measurements. The aim is to reduce tedious measure-ments, because when the fruits of one variety are coming from the same greenhouse

Fig. 5. The boundary curvature calculated for the tomato shown in Fig. 4.

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Fig. 6. Unevenly coloured tomato (yellowish spots). The overlay shows the areas found by analysing thehistogram of dominating wavelengths.

with more or less the same nutrition and climate, quality assessment should bereduced to non contact, non destructive tests.

The clearest correlation is between colour and rmness. Both measures have todo with maturity. No doubt, maturity can easily and with good result be measuredwith colour assessment (Fig. 9). Only a weak correlation exists between Brix andmaturity (Fig. 10). The same can be stated about maturity and vitamin C (Fig. 11).Although there is a tendency, the correlation is not very good.

Fig. 7. Histogram (number of pixels) of the unevenly coloured tomato depicted in Fig. 6 over thedominating wavelength of each pixel.

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Fig. 8. Reection discontinuities in the red band of an over mature tomato with a scar.

3. Fuzzy fusion: quality attributes

For the overall objective of fuzzy mapping, a model for processing the visualfuzzy parameters into some classes of an output parameter total visual quality isproposed.

3 .1. Basic quality attributes

Consumers as well as producers need some basic quality standard, which isgenerally accepted and is measurable by devices reasonable in price. Producers can

assess the results of their management decisions, consumers are looking for betterquality. As a result from the above discussions attributes and classes listed in Fig.12 and Table 4, for quality assessment are proposed. According to mans impres-

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Fig. 9. Correlation between maturity and rmness.

sion, the proposed parameters are not crisp numbers, but distributions around agiven value. Here fuzzy triangular distributions are chosen with peak values of one.The membership functions decrease to zero at the peak memberships of theforgoing or following classes, as can be seen in the attached windows of Fig. 10 or

Fig. 11. Because the following fuzzy reasoning was done with a professionalsoftware system, F UZZY C ONTROL M ANAGER (FCM) www.transfertech.de, varia-tions like other membership functions (i.e. Gauss, etc.) could easily be applied.

3 .2 . Fuzzy rating of consumer aspects

Parameters for contour and shape as well as colour and colour uniformity are thebasic parameters to be processed. These four parameters belong to fuzzy classesaccording to consumers judgements (Fig. 13). Contour and shape are processed byfuzzy reasoning to an output called geometry, colour and colour distribution to an

Fig. 10. Correlation between maturity and BRIX value

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Fig. 11. Correlation between maturity and vitamin C.

output impression. Both give the overall appearance, which had to be checked forcracks and faults. The attribute cracks is more or less a knockout criterion. Whenno discontinuities in colour or reection can be detected, a further grading to sizegives an overall visual quality as result.

The processing is done with classical fuzzy AND operators: IF (X1 AND X2)THEN Y1. Also operators like GAMMA (compensatory AND) may be applied.But these operators need a careful design. To ease plausibility control according tohuman reasoning the whole system has been broken down to rule bases with twoinputs only. With such an approach the rule base is easily surveyed.

Fig. 14 gives an example of mapping the contour variables and shape variablesto appearance. The best appearance is with even contour and round shape. The

Fig. 12. Elements for automatic assessment of visual quality.

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Fig. 14. Mapping of geometry on appearance.

Fig. 15. Mapping of colour and shape on quality.

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So contact or destructive measurements can be reduced to a minimum. The fuzzyimpression of consumers modelled by fuzzy logic can be ne tuned if enough dataare available. The proposed principle of modelling consumer quality can be adaptedand applied to other products, too.

Acknowledgements

This work was funded by the EU MACQU project contract No. AIR3-CT93-1603. The funding is gratefully acknowledged.

References

AID, 1992. Quality standard for vegetables. Auswertungs- und Informationsdienst fur Ernahrung,Landwirtschaft und Forsten (AID) e.V. 53179 Bonn, Germany.

Choi, K., Lee, G., Han, Y.J., Bunn, J.M., 1995. Tomato maturity evaluation using colour imageanalysis. Trans. Am. Soc. Agric. Eng. 38 (1), 171176.

Nielsen, H.M., Paul, W., 1995. Quality measurements of tomatoes by image analysis. Acta Hort. 421, 2.Nielsen, H.M., Paul, W., 1996. Tomato quality: correlations between image analysis and destructive

tests. Paper AGENG 96, Madrid, 2326, September.Sarkar, N., Wolfe, R.R., 1985a. Feature extraction techniques for sorting tomatoes by computer vision.

Trans. Am. Soc. Agric. Eng. 28 (3), 970979.Sarkar, N., Wolfe, R.R., 1985b. Computer vision based system for quality separation of fresh market

tomatoes. Trans. Am. Soc. Agric. Eng. 28 (5), 17141718.USDA, 1991. United States standards for grades of fresh tomatoes. USDA Agricultural Marketing

Service, Washington, DC.

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