Computers and Electronics in Agriculture 109 (2014) 302–310

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Computers and Electronics in Agriculture

journal homepage: www.elsevier .com/locate /compag

Neural image analysis for maturity classification of sewage sludgecomposted with maize straw

http://dx.doi.org/10.1016/j.compag.2014.08.0140168-1699/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +48 61 848 70 77; fax: +48 61 848 71 57.E-mail address: sebastian.kujawa@up.poznan.pl (S. Kujawa).

Sebastian Kujawa a,⇑, Krzysztof Nowakowski a, Robert Jacek Tomczak b, Jacek Dach a, Piotr Boniecki a,Jerzy Weres a, Wojciech Mueller a, Barbara Raba a, Tomasz Piechota c, Pablo César Rodríguez Carmona a

a Institute of Biosystems Engineering, Poznan University of Life Sciences, ul. Wojska Polskiego 50, 60-637 Poznan, Polandb Faculty of Informatics and Visual Communication, Higher School of Humanities and Journalism, ul. gen. Tadeusza Kutrzeby 10, 61-719 Poznan, Polandc Department of Agronomy, Poznan University of Life Sciences, ul. Dojazd 11, 60-632 Poznan, Poland

a r t i c l e i n f o

Article history:Received 8 March 2014Received in revised form 11 August 2014Accepted 14 August 2014

Keywords:Image analysisNeural networksCompost maturitySewage sludgeMaize straw

a b s t r a c t

This study uses the methods of computer image analysis and neural modelling for the construction ofclassification models to identify the stage of early maturity in composted material based on sewagesludge and maize straw. The research material was produced in strictly controlled laboratory conditionswith a six-chamber bioreactor. Samples of the material were subjected to image acquisition in visiblelight (VIS), ultraviolet light from the UV-A range and mixed light (MIX, VIS + UV-A). The acquired imageswere subjected to broad analysis. As a result the values of 46 parameters providing information about thecolour and texture were obtained. The colour was analysed for the RGB, HSV and greyscale model. Thetexture analysis determined the grey level co-occurrence matrixes (GLCM). The parameters acquiredfrom the image were the basis of train, validation and test sets which were used for the constructionof neural classification models. The models were based on the MLP (Multilayer Perceptron) topology.The process of their construction went on in the iterative manner, where the potentially insignificantinput parameters were eliminated by means of sensitivity analysis. Finally 21 such models weregenerated. The classification error for the best model in the MIX light was 1.56%. On the other hand,the models with the best accuracy in the UV-A and VIS light showed the error, which was 1.83% and2.87% greater than the best model for the MIX light, respectively.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction drying) as energetic material in waste incineration plants or

In recent years in Poland there has been a considerable increasein the production of communal sewage sludge (Malinska andZabochnicka-Swiatek, 2013). It largely results from improvedeffectiveness of sewage treatment, which is the effect of theintroduction of new technologies in response to the increasingrequirements concerning the quality of treated sewage dischargedto the environment. Apart from that, in poorly urbanised areas thelength of the sewerage system is growing dynamically. Thisharmonises with investments in new sewage treatment plants.The sludge produced as a result of wastewater treatment is apotentially harmful material, which must be handled somehow.From 2016 the storage of sewage sludge in Poland, so far the basichandling method, will be prohibited due to the adaptation of thePolish law to the EU standards. In the new legal situation there willbe two basic methods of sewage sludge recycling, i.e. using it (after

composting. In view of the fact that the construction of a wasteincineration plant is beyond the financial capacity of not only ruralcommunes but also counties, the only reasonable alternativerelated with the handling of communal sewage sludge locatedoutside big cities is the construction of small composting plants.Sewage sludge subjected to the composting process may be usedfor soil fertilisation (Kosobucki et al., 2000; Waszkielis et al.,2013). Taking into account the possibility of the future applicationin agriculture it is assumed that sewage sludge with reduced con-tent of heavy metals is adequate to the composting process. Itmeans that the content of heavy metals in the final product shouldnot exceed the limit values for farmland fertilisation. This sludgeusually comes from small and medium-sized sewage treatmentplants located in the areas which are not strongly industrialised.On the other hand, the considerable increase in temperature whichaccompanies the composting process leads to the pasteurisationand destruction of pathogenic organisms in sewage sludge (Haug,1980; Wolna-Maruwka et al., 2012).

Composting of sewage sludge is possible only if when mixedwith the structure creative material, i.e. various types of residues

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from the agricultural or wood production, such as sawdust orstraw. Under the project entitled ‘‘The use of maize straw as asubstrate for methane fermentation and structural material incomposting process’’, financed by the Polish Ministry of Science(2010–13) were studied the possibilities of wider use of maizestraw due to the increase of the prices of typical substrates inrecent years (Dach et al., 2014). It has been stated that under Polishconditions the most suitable is maize straw, which is a residue ofthe maize tillage for grain. Relatively high humidity of this materialin the fresh state (Kaliyan and Morey, 2009; Womac et al., 2005)causes that the possibilities of its rational use for heating purposesare strictly limited. That is why the maize straw has considerablyless of the potential buyers, and thus clearly lower price than strawfrom the cereals ears or even rape production. The use of maizestraw as addition to the composted sewage sludge allows toimprove its structure as it provides an adequate porosity muchlonger than the typical cereal straw. Due to improvement of theC:N ratio by introducing a large number of readily available organiccarbon it is also visible a significant reduction of ammonia emis-sions from composted sewage sludge (Boniecki et al., 2012a).

In a rational composting process it is important to identify themoment when the material reaches the stage of early maturityas soon as possible. At that stage the decomposition processesbecome clearly inhibited. The material which reaches this stagemay be stored in high heaps up to 4 m. This has influence on savingthe area of the compost platform, which is relatively expensive. Itis a problem to precisely identify that stage. This can be done byphysiochemical analyses concerning the intensity and compositionof gases emitted from the composted biomass, checking the C:Nratio or by checking the content of humus (Piotrowska-Cypliket al., 2009, 2013; Dach et al., 2008; Dach, 2010a,b). However, inpractice this cannot be done by the staff of a composting plant.Therefore, it was necessary to develop a quick and effectivemethod of verification of the degree of maturity of the compostedsewage sludge in order to support the decision to move the com-posted sewage sludge to the maturing facility.

The issue of using artificial neural networks to investigate theprocesses of biomaterial composting is not fully appreciated. In fact,in recent years there have been few studies where these tools wereused for such investigations. Artificial neural networks were used forthe prediction of ammonia emission from the composted biomass(Boniecki et al., 2012a), the prediction of heat loss (Boniecki et al.,2013) and the classification of the degree of maturity on the basisof selected physical and microbiological parameters (Gao et al.,2007). They were also used for the optimisation of the compostingprocess based on the achievement of appropriate courses of thecurves of variance in pH, temperature and CO2 concentration (Díazet al., 2012). The application of the methods of computer image anal-ysis in such studies is even more limited. Except for few studies(Boniecki et al., 2012b; Kujawa et al., 2013) the literature does notprovide any publications about the application of these methodsto investigate the composting processes. However, the methods ofcomputer image analysis proved to be an adequate and credible toolsupporting the classification and assessment of the state of differentbiomaterials (Delwiche et al., 2013; Majumdar and Jayas, 1999,2000a,b,c,d; Manickavasagan et al., 2008; Rodríguez-Pulido et al.,2012; Szczypinski and Zapotoczny, 2012). They are often used incombination with neural modelling. The combination of both tools,which is also called the neural image analysis, is applied for theassessment of damage to cereal grains (Nowakowski et al., 2009,2011) and fruit (Guyer and Yang, 2000). Apart from that it is alsoused for the identification of cereal grain varieties (Chen et al.,2010; Nowakowski et al., 2012; Pourreza et al., 2012; Zapotoczny,2011). In view of the broad classification potential offered by theneural image analysis the authors decided to use it for research onthe determination of compost maturity. The aim of the study was

to develop neural classification models to identify the stage of earlymaturity in the composted sewage sludge and maize straw mixtureon the basis of the information included in images of the compostedmaterial acquired in different light variants.

2. Materials and methods

2.1. Research material acquisition

The research material was acquired in 2012 by means of asix-chamber bioreactor (Wolna-Maruwka and Dach, 2009;Wolna-Maruwka et al., 2012) from the Ecotechnology Laboratory,Poznan University of Life Sciences, Poland. The composting mate-rial consisted of sewage sludge mixed in different proportions withthe structure-forming material of maize straw, both in an ensiledand non-ensiled form (Table 1). The sludge used in the researchcame from the sewage treatment plant in Szamotuły near Poznan(Greater Poland Voivodeship). It had reduced content of heavymetals. The straw came from farms also located near this city.

The experiments were made in strictly controlled laboratoryconditions. During and at the end of each process samples of thematerial were collected for photographs and physic-chemicalanalyses. The following parameters were identified for the samplescollected: dry mass, pH, conductivity, density, ash and organicmatter, ammonium nitrogen, total nitrogen and organic carbon.During the composting processes the temperature of the materialwas registered, the concentrations of oxygen and carbon dioxidein the air escaping from the bioreactor chambers were determinedand the emissions of ammonia, hydrogen sulphide and methanewere also analysed. In order to classify the composted materialas the one which achieved the stage of early maturity the authorsused the opinion of experts who had been conducting research onbiological waste processing for a long time and the results of phys-iochemical analyses. The experts developed and used the followingcriteria to classify the composted material as the one whichachieved the stage of early maturity:

� the material should be dark-coloured and smell like garden soilor duff; putrefactive or specific and offensive odour resultingfrom intensified ammonia or hydrogen sulphide emission isunacceptable,� the material should undergo the process of hygienisation, i.e.

during the process its temperature should be maintained at alevel of at least 55 �C for at least 1 day or reach at least 70 �Cfor at least 1 h (this parameter was mentioned in EC Regulation1774/2002),� the temperature of the acquired material should not exceed

30 �C,� the material should be relatively stable; the content of oxygen

in the air escaping from the bioreactor chambers should begreater than 18%, whereas the content of carbon dioxide shouldnot exceed 2.9%,� the content of dry substance in the product obtained as a result

of composting in the bioreactor should be higher than 25%,� at the end of the process the pH of the material should range

from 7 to 9.

Totally 8 experiments were conducted. The period of the exper-iments 1–3 was 36 days, while the period of the experiments 4–6was 29 days, and the period of the experiments 7–9 was 39 days.Fig. 1 shows variations in the temperature of the compostedmaterial. Fig. 2 shows variations in the concentration of oxygenin the air escaping from the bioreactor chambers. The characteris-tics of both parameters were found to be satisfactory. In 7 out of 8experiments the compost met all the criteria of early maturity.

Table 1The arrangement of composting experiments.

Experiment number Sewage sludge percentage (%) Straw percentage (%) Straw type Duration (days)

1 30 70 Non-ensiled 362 45 55 Non-ensiled 363 60 40 Non-ensiled 364 30 70 Ensiled 295 40 60 Ensiled 296 50 50 Ensiled 297 45 55 Ensiled 398 55 45 Ensiled 39

Fig. 1. The temperature of the composted material (dots – values measured, lines – interpolation of the results, dashed line – upper limit for the material which achieved thestage of early maturity).

Fig. 2. The content of oxygen in the air escaping from the bioreactor chambers (dots – values measured, lines – interpolation of the results, dashed line – lower limit for thematerial which achieved the stage of early maturity).

304 S. Kujawa et al. / Computers and Electronics in Agriculture 109 (2014) 302–310

Only in the third experiment the composted material did not reachthis stage because the content of dry substance in the final productwas too low (22%).

2.2. Image acquisition

Three photo chambers with different types of light: visible light(VIS), ultraviolet light from the UV-A range and mixed light (MIX,VIS + UV-A) were the basic element of the station for the acquisition

of images of the composted material samples (Kujawa et al., 2012).The images were acquired for the material samples obtained in fourvarious days of the composting experiments. Due to the possibleinterference of the composting processes it was not desirable toobtain the samples more often. For the experiments 1–3 the imageswere acquired in 1, 10, 20 and 36 day, while for the experiments 4–6 in 1, 6, 14 and 29 day, and for the experiments 7–8 in 1, 15, 24 and39 day. The VIS was standard type of light, the images registeredusing this type of light corresponded to images viewed by human

S. Kujawa et al. / Computers and Electronics in Agriculture 109 (2014) 302–310 305

eye (light reflected from the material) in standard light conditions,for example in sunlight. In a case of usage of the UV-A light it wasnot important to register the light reflected from the material, butit was important to register potential luminescence in the VISrange, induced by the UV-A light. Such luminescence may deliverinformation, which are not noticed under the VIS light. The MIXlight contained the features of VIS and UV-A light.

The following fluorescent lamps were used as the sources oflight: Sylvania Luxline Plus F15 W/865 for VIS and Philips TL-D15 W BLB for UV-A. The photographs were taken with a Nikondigital single-lens reflex camera (10.1 Mpix DX-format image sen-sor) with a fixed-focus lens 35 mm. The ISO sensitivity was set at100 and the aperture value was f/5.6. The exposure time and whitebalance for the VIS and MIX light were determined according to theprinciples of photography for visible light. Appropriate functions ofthe camera and a grey card reflecting 18% of visible light were usedfor this purpose. As far as UV-A light is concerned, the applicationof these rules was not justified. Therefore, these parameters wereassumed by experiment. Eventually, three exposure times wereadopted for this type of light. Table 2 shows information aboutthe adopted variants of image acquisition. Fig. 3 shows examplephotographs of the composted material samples acquired inindividual image acquisition variants.

2.3. Image analysis

Twelve photos were obtained for each image acquisition variantin each of the composting experiments. Each of the acquiredphotos was divided into 16 images. Thus, 192 images of the mate-rial samples were obtained for each image acquisition variant ineach of the experiments (Kujawa et al., 2013). Each of the imageswith a resolution of 968 � 648 pixels showed an area of about98 � 65 mm of evenly distributed portion of the compostedmaterial.

The initial analysis of the obtained photographs revealed that itwould be difficult and time-consuming to precisely determine theshapes of individual components of the composted biomaterial andits usefulness would be doubtful in view of the goal of the research.Therefore the shapes which can be seen in the images were notanalysed. However, each image was broadly analysed for its colourand texture. As a result 46 parameter values were obtained. Inorder to obtain some of them it was necessary to make the follow-ing conversions:

� image conversion from a 24-bit RGB colour space model to an 8-bit greyscale, where the pixel brightness was determined as theweighted sum of R, G and B components:

GS ¼ 0:2989Rþ 0:5870Gþ 0:1140B; ð1Þ

� image conversion from an RGB model to an HSV model,� binarisation of the image in 8-bit greyscale using 4 binarisation

thresholds (on a scale of 0 to 1): 0.05, 0.1, 0.15 and 0.2,

Table 2Image acquisition variants.

Acquisition variant Type of light Exposure tim

VIS VISa 1/25UVA1s UV-Ab 1UVA5s UV-Ab 5UVA10s UV-Ab 10MIX MIXc 5

a 4 Sylvania Luxline Plus F15 W/865 fluorescent lamps.b 4 Philips TL-D 15 W BLB fluorescent lamps.c 2 Sylvania Luxline Plus F15 W/865 and 2 Philips TL-D 15 W BLB fluorescent lamps.d Preset camera mode for fluorescent light (colour temperature 4200 K).

� reducing the original resolution of the image in 8-bit greyscaleto 4 modified resolutions: 768 � 512, 384 � 256, 192 � 128 and96 � 64 pixels.

In the process of texture analysis the grey level co-occurrencematrixes (GLCM) were determined for the images in the originalresolution and the four modified resolutions (Haralick et al.,1973; Haralick and Shapiro, 1992; Pourreza et al., 2012; Kujawaet al., 2013). The following parameters were taken into accountwhen the GLCMs were determined: 8 classes of pixel brightness,4 directions of neighbourhood search, i.e. 0�, 45�, 90� and 135�(symmetrically) and the neighbourhood in the form of one pixel.

The following colour-related parameters were determined foreach of the acquired images (Kujawa et al., 2013):

� WH_PER1, WH_PER2, WH_PER3, WH_PER4 – the percentage(PER) of white (WH) in the image subjected to binarisation withfour thresholds adopted (0.05, 0.1, 0.15 and 0.2, respectively),� MEAN_R, MEDIAN_R, STD_R – the mean value, median and

standard deviation in the intensity of R component for theoriginal image,� MEAN_G, MEDIAN_G, STD_G – the mean value, median and

standard deviation in the intensity of G component for theoriginal image,� MEAN_B, MEDIAN_B, STD_B – the mean value, median and

standard deviation in the intensity of B component for theoriginal image,� MEAN_GS, MEDIA_GS, STD_GS – the mean value, median and

standard deviation in the grey intensity for the image convertedto greyscale (GS),� MEAN_H, MEDIA_H, STD_H – the mean value, median and

standard deviation in the H component for the image convertedinto the HSV model,� MEAN_S, MEDIA_S, STD_S – the mean value, median and

standard deviation in the S component for the image convertedinto the HSV model,� MEAN_V, MEDIA_V, STD_V – the mean value, median and

standard deviation in the V component for the image convertedinto the HSV model.

On the other hand, the following parameters were determinedfor the texture:

� GS_ENT – the entropy (ENT) of the image converted togreyscale,� GS_CON, GS512_CON, GS256_CON, GS128_CON, GS64_CON –

the intensity contrast (CON) between pixels and their neigh-bourhood for the greyscale image in the original resolutionand 4 modified resolutions, averaged for the selected directions,� GS_COR, GS512_COR, GS256_COR, GS128_COR, GS64_COR – the

correlation (COR) between pixels and their neighbourhood forthe greyscale image in the original resolution and 4 modifiedresolutions, averaged for the selected directions,

e (s) White balance

Manual settings (using grey card)Manual settings (preset moded)Manual settings (preset moded)Manual settings (preset moded)Manual settings (using grey card, only for visible light)

Fig. 3. Example images of samples of the composted material with 55% of sewage sludge and 45% of maize straw acquired in individual image acquisition variants.

306 S. Kujawa et al. / Computers and Electronics in Agriculture 109 (2014) 302–310

� GS_ENE, GS512_ENE, GS256_ENE, GS128_ENE, GS64_ENE –energy (ENE) for the greyscale image in the original resolutionand 4 modified resolutions, averaged for the selected directions,� GS_HOM, GS512_HOM, GS256_HOM, GS128_HOM, GS64_HOM

– homogeneity (HOM) for the greyscale image in the originalresolution and 4 modified resolutions, averaged for the selecteddirections.

The following formulas were used to identify the textureparameters (Haralick et al., 1973; Haralick and Shapiro, 1992;Gao et al., 2013; Kujawa et al., 2013):

Entropy ¼ �X256

k¼1

ðnk � log2nkÞ ð2Þ

Contrast ¼X8

i¼1

X8

j¼1

ði� jÞ2pði; jÞ ð3Þ

Correlation ¼X8

i¼1

X8

j¼1

ði� liÞðj� ljÞpði; jÞrirj

; ð4Þ

Energy ¼X8

i¼1

X8

j¼1

pði; jÞ2; ð5Þ

Homogeneity ¼X8

i¼1

X8

j¼1

pði; jÞ1þ ði� jÞ ; ð6Þ

where:

li ¼X8

i¼1

X8

j¼1

i � pði; jÞ;

lj ¼X8

i¼1

X8

j¼1

J � pði; jÞ;

ri ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX8

i¼1

X8

j¼1ði� liÞ

2 � pði; jÞr

;

ri ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiX8

i¼1

X8

j¼1ðj� ljÞ

2 � pði; jÞr

;

nk – number of pixels of intensity k, i – row number of GLCM, j –column number of GLCM, p(i,j) – elements of GLCM divided bythe sum of its elements.

In order to automate the process of image segmentation andextraction of selected parameter values from the obtained imagesoriginal Compost Image Analysis software was used. It wasdeveloped in the MATLAB environment with the Image ProcessingToolbox.

2.4. Neural models development

For each of the five image acquisition variants one datasetcontaining the train, validation and test sets was prepared in orderto build neural classification models. In each dataset the inputinformation consisted of the following parameters: the values of46 parameters acquired from the image, the percentage of sewagesludge in the mixture (SLUD_PER variable) and the qualitativeinformation whether or not the applied straw had been ensiled(STR_TYPE variable). The output information was the expectedresponse from the neural network whether the material achievedthe stage of early maturity (IS_YCOMP variable). Each datasetincluded 1536 cases, with 864 cases of the material that did notreach the early maturity stage and 672 cases of the material whichreached that stage. Each single case was related to one singleimage of the composted material. The train set consisting of 768cases and the validation and test sets, either of which consistedof 384 cases, were distinguished from each dataset (proportions2:1:1). The train, validation and test sets were completely indepen-dent from each other.

The neural models were constructed in the Statistica 10environment with the tool for the construction of artificial neuralnetworks, which is part of the Data Miner module. The MLP (Mul-tilayer Perceptron) network topology with one hidden layer wasapplied. The networks were assumed originally to have 49 neuronsin the input layer, 50 neurons in the hidden layer and 2 neurons inthe output layer (Fig. 4). It can be observed that there is an offsetbetween the number of neurons in the input layer and the numberof input parameters. It results from the fact that the qualitativeSTR_TYPE variable employs two input neurons. The hyperbolic tan-gent was adopted as the activating function for the hidden layerand the softmax was adopted for the neurons in the output layer.The networks were trained with the conjugate gradients (CG) algo-rithm. Cross entropy was used as the error function. The modelswere constructed iteratively, with the elimination of insignificantinput parameters by means of the tool of sensitivity analysis. Thismeans that the inputs which had been proved by the sensitivity

Table 3The developed classification models.

Modela Classification error of test set(%)

Number ofepochsb

MLP 49-50-2 VIS 4.69 271MLP 45-50-2 VIS 5.99 202MLP 37-50-2 VIS 4.43 219MLP 35-50-2 VIS 5.47 143MLP 32-50-2 VIS 4.95 282MLP 31-50-2 VIS 4.43 316MLP 30-50-2 VIS 5.21 279MLP 49-50-2 UVA1s 8.59 159MLP 18-50-2 UVA1s 8.07 132MLP 17-50-2 UVA1s 8.07 137MLP 16-50-2 UVA1s 7.81 145MLP 15-50-2 UVA1s 8.07 135MLP 14-50-2 UVA1s 8.33 214MLP 49-50-2 UVA5s 4.69 116MLP 20-50-2 UVA5s 4.95 182MLP 49-50-2

UVA10s3.91 211

MLP 30-50-2UVA10s

3.39 204

MLP 49-50-2 MIX 1.82 165MLP 36-50-2 MIX 1.56 167MLP 35-50-2 MIX 1.56 147

S. Kujawa et al. / Computers and Electronics in Agriculture 109 (2014) 302–310 307

analysis of the parent network to be potentially insignificant wereeliminated from the consecutive neural network. The process ofsensitivity analysis was based on dividing errors of the networkexcluding individual input parameters by the error of the networkincluding all inputs. In view of a relatively large number of inputdata the elimination threshold for the error quotient of 1.05 wasassumed. The iterative construction of new offspring networkscontinued until none of the input variables gave the error quotientunder the assumed threshold value. The quality and classificationerror were determined for individual sets in each network. Theerror provided information about the percentage of wrongresponses of the network in reference to all the responses in aparticular set. The classification error values for the test sets wereparticularly important, because these sets were not used in thetraining processes. Next, the optimal models for individual lightvariants (VIS, UV-A and MIX) were selected from the networks,allowing for the lowest value of the classification error for the testset. If two or more models had the same value of the ratio, thesimpler one was selected, i.e. the one with a smaller number ofinputs. The optimal models were subjected to further analyses asa result of which ROC (Receiver Operative Characteristics) curvesand the significance of input variables were determined.

MLP 34-50-2 MIX 2.08 141

a Model names consist of: topology (MLP), structure (number of neurons in inputlayer, hidden layer and output layer, respectively), and image acquisition variant(VIS, UVA1s, UVA5s, UVA10s or MIX).

b Models were trained using the CG algorithm.

3. Results

3.1. The developed models

The research resulted in the construction of a total number of21 classification models for determination of the achievement ofthe early maturity stage in the composted material (Table 3). Thefollowing coding was used for model names:

� first symbol provides information about the topology of themodel (MLP),� second symbol provides information about the structure of the

model (consists of number of neurons in the consecutivelayers),� third symbol provides information about image acquisition

variant which was used to develop the model.

Seven models were constructed for the VIS image acquisitionvariant. Their classification error for the test set ranged from4.43% to 5.99%. The initial model was MLP 49-50-2 VIS. The inputvariables eliminated from the six consecutive offspring networksas a result of the sensitivity analysis of the parent networks wereas follows:

Fig. 4. The initial structure of classification models.

1. for MLP 45-50-2 VIS: GS256_COR, WH_PER3, GS64_CORand GS_STD,

2. for MLP 37-50-2 VIS: GS128_ENE, GS256_CON, V_MEAN,GS256_ENE, GS_MEDIAN, GS_MEAN, G_STD and G_MEAN,

3. for MLP 35-50-2 VIS: B_STD and GS_HOM,4. for MLP 32-50-2 VIS: G_MEDIAN, S_MEDIAN and H_MEAN,5. for MLP 31-50-2 VIS: GS_ENE,6. for MLP 30-50-2 VIS: GS_COR.

Six models were constructed for the UVA1s variant. Theirclassification error ranged from 7.81% to 8.59%. The initial modelwas MLP 49-50-2 UVA1s. The following input variables wereeliminated from the five offspring networks for this variant:

1. for MLP 18-50-2 UVA1s: B_STD, V_STD, WH_PER1,WH_PER3, WH_PER4, GS64_ENE, GS64_CON, GS64_HOM,GS512_ENE, GS128_ENE, GS_ENE, GS256_ENE, GS128_CON,GS128_HOM, WH_PER2, GS256_HOM, GS256_CON,GS512_HOM, GS512_CON, GS_HOM, GS_CON, S_MEDIAN,G_MEDIAN, G_MEAN, GS_MEAN, GS_MEDIAN, B_MEAN,V_MEAN, GS64_COR, B_MEDIAN and V_MEDIAN,

2. for MLP 17-50-2 UVA1s: GS_STD,3. for MLP 16-50-2 UVA1s: GS512_COR,4. for MLP 15-50-2 UVA1s: G_STD,5. for MLP 14-50-2 UVA1s: H_MEDIAN.

Two models were obtained for the UVA5s variant. They resultedin the classification errors of 4.69% and 4.95%. The initial modelwas MLP 49-50-2 UVA5s. The following variables were eliminatedfrom the only offspring network, i.e. from MLP 20-50-2 UVA5s:GS256_HOM, GS_STD, B_MEAN, V_MEAN, S_MEDIAN, GS_MEDIAN,WH_PER2, GS512_COR, B_STD, V_STD, S_MEAN, GS256_COR,GS_MEAN, WH_PER3, G_MEDIAN, S_STD, G_MEAN, GS128_HOM,WH_PER4, H_MEDIAN, GS64_HOM, GS64_ENE, GS512_ENE,GS256_ENE, GS_ENE, GS128_ENE, H_MEAN, GS128_COR andR_MEDIAN.

Fig. 5. The ROC curves for the optimal models (the test set).

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Two models were constructed for the UVA10s variant. Theirclassification errors were 3.91% and 3.39%. The initial model wasMLP 49-50-2 UVA10s. The following variables were eliminatedfrom the only offspring network, i.e. from MLP 30-50-2 UVA10s:GS_COR, GS512_ENE, GS128_HOM, H_STD, GS_ENE, GS256_ENE,GS128_ENE, V_MEDIAN, B_MEDIAN, WH_PER3, B_MEAN, V_MEAN,G_MEAN, GS_MEAN, G_MEDIAN, WH_PER4, GS_MEDIAN,GS512_COR and GS256_COR.

Four classification models were generated for the MIX imageacquisition variant. They were burdened with the classificationerror ranging from 1.56% to 2.08% for the test set. The initial modelwas MLP 49-50-2 MIX. The following variables were eliminatedfrom the three consecutive offspring networks:

1. for MLP 36-50-2 MIX: GS512_COR, GS_MEAN, R_MEAN,GS_COR, GS_STD, G_STD, B_MEDIAN, GS512_ENE, GS_HOM,H_MEDIAN, GS_ENT, B_MEAN and GS256_ENE,

2. for MLP 35-50-2 MIX: GS_ENE,3. for MLP 34-50-2 MIX: GS512_HOM.

The best models in individual image acquisition variantsachieved the classification error values ranging from 1.56% to7.81% in the test set. The MLP 16-50-2 UVA1s achieved the highestvalue of this ratio. It was generated on the basis of images of thecomposted material obtained in UV-A light and the exposure timeof 1 s. Interestingly, the MLP 49-50-2 UVA5s and MLP 30-50-2UVA10s models, which were related with same source of lightbut with the exposure time extended to 5 s and 10 s, respectively,achieved the classification error values that were lower by 3.12%and 4.42%, respectively. This fact leads to the presumption thatthe exposure time of 1 s was too short for the UV-A light and didnot give a possibility to fully enhance the significant details ofthe composted material. The results obtained for the best modelin the VIS variant were 3.38% better than the results in the UVA1svariant and 0.26% better than the results in the UVA5s variant. Onthe other hand, in reference to the results in the UVA10s variant,the results in the VIS variant were worse by 1.04%. The model inthe MIX image acquisition variant definitely achieved the bestclassification results.

3.2. The selection and analysis of optimal models

In view of the assumed criterion the optimal models in the VIS,UV-A and MIX light variants were as follows: MLP 31-50-2 VIS,MLP 30-50-2 UVA10s and MLP 35-50-2 MIX. Their classificationerror for the test set achieved the values of 4.43%, 3.39% and1.56%, respectively. Table 4 shows the classification statistics ofthese models. Furthermore, Fig. 5 shows the ROC curves whichpresent the correlations between sensitivity and specificity ofthese models. As can be seen from the curves, the models perfectlydetect the objects in a particular class and, in practice, are free fromcontamination of one class with the objects from the other class.The area under the ROC curves for the VIS, UV-A and MIX models

Table 4The classification statistics of the optimal models (the test set).

Optimal model for VIS: MLP 31-50-2 VIS Optimal model for

IS_YCOMP-0a IS_YCOMP-1b IS_YCOMP-0a

Total 216 168 216Correct 207 160 212Incorrect 9 8 4Correct (%) 95.83 95.24 98.15Incorrect (%) 4.17 4.76 1.85

a Cases classified as material which did not achieve the stage of early maturity.b Cases classified as material which achieved the stage of early maturity.

was 0.993, 0.994 and 0.997, respectively. Both the values of theclassification ratios and the shapes of the ROC curves for theselected models were found to be satisfactory. Although it is clearthat the best was the model for the MIX light, the second was themodel for the UV-A light, and the worst was the model for the VISlight. It can be considered that the luminescence of the compostedmaterial exposed under the UV-A light gave more useful informa-tion than standard images acquired under the VIS light. On theother hand, the images acquired in the MIX light combine standardinformation with the information about the luminescence, and itimproves the correctness of the classification.

The number of input variables used in the optimal models forthe VIS, UV-A and MIX was 30, 29 and 34, respectively. Thedetailed information about the input variables that was used inthe optimal models was presented in Table 5. Eighteen input vari-ables were common to the optimal models, 7 of the variables wererelated with the colour (WH_PER1, WH_PER2, R_MEDIAN, R_STD,S_MEAN, S_STD, V_STD), 9 were related with the texture (GS_CON,GS512_CON, GS512_HOM, GS256_HOM, GS128_CON, GS128_COR,GS64_CON, GS64_ENE and GS64_HOM), and 2 were related withthe physical parameters (SLUD_PER and STR_TYPE). In the optimalmodels for the types of light containing VIS the group of 4 otherinput variables was also common, including WH_PER4, H_STDand V_MEDIAN variables related with the colour, and GS128_HOMvariable related with the texture. While, in the optimal models forthe types of light containing UV-A the group of 5 other input vari-ables was also common, including B_STD, H_MEAN and S_MEDIANvariables related with the colour, and GS256_CON and GS64_CORvariables related with the texture. It can be considered that thefirst group of 18 common input variables is independent fromthe type of light, the second group of 4 variables depends on theVIS light, and third group of 5 variables depends on the UV-A light.

UV-A: MLP 30-50-2 UVA10s Optimal model for MIX: MLP 35-50-2 MIX

IS_YCOMP-1b IS_YCOMP-0a IS_YCOMP-1b

168 216 168159 214 1649 2 494.64 99.07 97.625.36 0.93 2.38

Table 5Input variables included in the optimal models.

Optimal model Input variablesa

MLP 31-50-2 VIS GS64_HOM, STR_TYPE, WH_PER2, GS128_HOM, S_MEAN, GS256_HOM, R_MEDIAN, SLUD_PER, GS128_COR, V_MEDIAN, R_MEAN, WH_PER4,GS_ENT, B_MEAN, GS64_ENE, B_MEDIAN, H_STD, GS512_CON, R_STD, V_STD, GS64_CON, GS_CON, WH_PER1, GS128_CON, H_MEDIAN, S_STD,GS512_COR, GS512_ENE, GS512_HOM, GS_COR

MLP 30-50-2UVA10s

STR_TYPE, WH_PER2, GS_ENT, S_MEDIAN, GS_CON, GS512_CON, GS64_HOM, WH_PER1, GS_HOM, GS64_ENE, GS256_CON, GS512_HOM,GS64_COR, R_MEAN, R_STD, H_MEAN, GS128_COR, GS256_HOM, GS64_CON, GS128_CON, R_MEDIAN, SLUD_PER, H_MEDIAN, S_MEAN, G_STD,S_STD, V_STD, B_STD, GS_STD

MLP 35-50-2 MIX STR_TYPE, H_MEAN, H_STD, GS64_ENE, GS128_HOM, GS256_HOM, SLUD_PER, WH_PER2, GS128_CON, GS64_HOM, S_MEAN, S_MEDIAN,V_MEDIAN, V_MEAN, GS128_ENE, WH_PER4, R_STD, GS128_COR, GS64_CON, V_STD, B_STD, GS_CON, R_MEDIAN, GS512_CON, GS_MEDIAN,GS256_COR, G_MEAN, S_STD, G_MEDIAN, WH_PER1, GS256_CON, WH_PER3, GS64_COR, GS512_HOM

a Variables ordered from the most significant one – according to the decreasing error quotient for the validation set.

S. Kujawa et al. / Computers and Electronics in Agriculture 109 (2014) 302–310 309

The five most important image parameters that affected thecorrect identification of early maturity in the composted material,using the model for the VIS variant, were: GS64_HOM, WH_PER2,GS128_HOM, S_MEAN and GS256_HOM. They were characterizedby the following error quotient values: 19.26, 17.45, 15.94, 11.41and 7.55, while the error quotient for the least important parame-ter was 1.05. The most important image parameters for the modelfor the UV-A variant were: WH_PER2, GS_ENT, S_MEDIAN, GS_CONand GS512_CON. The error quotients for these variables wererespectively: 23.86, 21.97, 5.69, 4.66 and 4.23, while the error quo-tient for the least important variable was 1.06. Regarding to themodel for the MIX variant, the following image parameters provedto be the most important: H_MEAN, H_STD, GS64_ENE,GS128_HOM and GS256_HOM. They were characterized by thefollowing error quotients: 35.25, 23.41, 17.43, 11.89 and 7.88,while the error quotient for the least important parameter was1.02. Therefore, it can be seen that among the most importantimage parameters that affected the correct identification of earlymaturity in the composted material are those related to the colourand texture. Although it has been noticed that in the MLP 35-50-2MIX, the best of all developed models, two parameters from theHSV colour model (the mean and standard deviation of the Hcomponent) played crucial role among all the image parameters.Their summative error quotient was 58.66.

Fig. 6 shows the graphs of the summative error quotient for thethree categories of input variables, i.e. the variables concerning thephysical parameters of the composted material and those relatedwith the colour and texture contained in its image. Thus, it ispossible to conclude that variables from all the groups have signif-icant influence on the correctness of classification. The removal ofany group would have significant influence on an increase in the

Fig. 6. The summative error quotient for the assumed categories of input variables.

network error. As far as the models for VIS and UV-A light areconcerned, the variables related with the texture have the greatestinfluence. On the other hand, as far as the MIX light model isconcerned, the parameters related with the colour are the mostsignificant.

The optimal classification models, obtained due to computingresearch and natural experiments, allow the evaluation of theproper look of the composted mixture of sewage sludge and maizestraw after reaching the stage of early maturity. This compost has abrown colour typical for materials with high content of humus andwithout ammonia or hydrogen sulfide odors characteristic for freshsewage sludge. The proper appearance of the compost proves theright hygienisation process of sewage sludge, moreover can havea significant marketing effect in terms of the possibilities ofmanagement of the obtained material as a fertilizer in agriculture.

4. Conclusions

The application of the methods computer image analysis andneural modelling enabled the construction of adequate classifica-tion models to identify the stage of early maturity in compostedmaterial on the basis of sewage sludge and maize strawproportions. The mixed light (MIX) which was applied in the imageacquisition process to construct neural models improved theirclassification properties in comparison with the models generatedon the basis of the images which had been generated with VIS orUV-A light only. The classification error for the best model in theMIX light was 1.56%. On the other hand, the most precise modelsfor the UV-A or VIS light resulted in the error which was greaterthan in the best MIX light model by 1.83% and 2.87%, respectively.The constructed models used the sets of input variables concerningthe colour, texture and physical parameters of the compostedmaterial. As far as the summative values are concerned, the param-eters acquired from the image had definitely the greatest influenceon the correctness of classification. However, the omission of phys-ical parameters, namely the information about the percentage ofsewage sludge and the type of maize straw used in the compostedmixture would significantly deteriorate the classification capacityof the generated models. It can be assumed that implementationof the achieved results to the real business practice will allow inthe right time to take an economically reasonable decision aboutmoving of the composted mixture of sewage sludge and maizestraw onto maturing facility, and on the other hand will increasethe chance for wider commercialization of the compost producedfrom these substrates.

Acknowledgments

The study was financed from the Polish budget funds forscience in 2010–2013 as a research Project entitled ‘‘Evaluation

310 S. Kujawa et al. / Computers and Electronics in Agriculture 109 (2014) 302–310

of compost quality using computer image analysis and neuralmodelling’’ (Contract No. N N313 273939).

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