Expert Systems with Applications 38 (2011) 12101–12106

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Expert Systems with Applications

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High speed detection of potato and clod using an acoustic based intelligent system

Adel Hosainpour a,⇑, Mohammad H. Komarizade b, Asghar Mahmoudi c, Mahrokh G. Shayesteh d

a Department of Agricultural Machinery, College of Agriculture, University of Ilam, Ilam, Iranb Department of Agricultural Machinery, College of Agriculture, University of Urmia, Urmia, Iranc Department of Agricultural Machinery, College of Agriculture, University of Tabriz, Tabriz, Irand Department of Electrical Engineering, Faculty of Engineering, University of Urmia, Urmia, Iran

a r t i c l e i n f o a b s t r a c t

Keywords:AcousticsClodDiscriminatingNeural networksPotatoSeparation

0957-4174/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.eswa.2011.02.164

⇑ Corresponding author. Tel.: +98 4116671294, moE-mail addresses: a_hosainpour12@yahoo.com,

Hosainpour), m.komarizade@urmia.ac.ir (M.H. Komazu.ac.ir (A. Mahmoudi), m.shayesteh@urmia.ac.ir (M.G

Discriminating between potato tubers and clods is the first step in developing an automatic separationsystem on potato harvesters. In this study, an acoustic-based intelligent system was developed for highspeed discriminating between potato tubers and soil clods. About 500 kg mixture of potato tubers andclods were loaded on a belt conveyer and were impacted against a steel plate at four different velocities.The resulting acoustic signals were recorded, processed and potential features were extracted from theanalysis of sound signals in both time and frequency domains. A multilayer perceptron neural networkwith a back propagation algorithm was used for pattern recognition. Altogether, 17 potential discriminat-ing features were selected and fed as input vectors to the artificial neural network models. Optimal net-work was selected based on mean square error, correct detection rate and correlation coefficient. At thebelt velocity of 1 m s�1, detection accuracy of the presented system was about 97.3% and 97.6% for pota-toes and clods, respectively. Increasing the belt velocity resulted in the reduction of detection accuracyand increase in the number of miss classified samples. By using this system, it is expected that a potatoharvester may operate at a capacity of 20 ton hr�1 with the accuracy of about 97%.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Potato is one of the most prominent crops in the world with anestimated annual production of approximately 325 million ton(FAO, 2008). This enormous amount of potato needs fully auto-mated harvesters. For a long time, the agro-industry has attemptedto automate potato harvesting operations. Although the tasks ofdigging, picking up potatoes and removing fine clods are per-formed mechanically, removing clods and probable stones are usu-ally done manually. The clods and stones must be removed beforeentry into the potato harvester container; because they occupy alarge space of the container and with their abrasive nature maymechanically damage potato tubers. Accompanying mixture of po-tato also reduces the air circulation and prevents proper tempera-ture and humidity control (Main, 1971). Discriminating betweenpotato tubers and clods is the first step in developing an automaticseparation system on potato harvesters. Developing an automatedsystem for separating clods from potato tubers is more challenging,because potato tubers have a wide diversity in shape, size and col-or. Moreover, the shape, size, and moisture content of clods are not

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bile: +98 9143002974.a.hosainpour@urmia.ac.ir (A.rizade), A_mahmoudi@tabri-. Shayesteh).

predictable in harvesting condition. Partially and completely mud-dy potatoes add another obstacle in developing automated separa-tor systems.

Separating potatoes from its impurities has been investigated inmany research studies. Most researchers have examined mechan-ical methods to solve the problem (Brantley, Hamann, & Whitfield,1975; Feller, Morgolin, Zacharin, & Pasternak, 1985; Gan-Mor,Zacharin, Galili, Feller, & Margolin, 1986; Shyam, Singh, & Singh,1990). Although some of these methods are being used in some po-tato harvesters, they cause surface damages and bruises on potatotubers (McGechan, 1980). Moreover, large space occupancy andhigh energy consumption are other main drawbacks of these sys-tems. Story and Raghavan (1973) used infrared reflectance to sort-ing potatoes from stones and soil clods. In recent years, somemachine vision based systems have been developed for discrimi-nating between potato tubers and clods (Al-Mallahi, Kataoka, &Okamoto, 2008; Marchant, Onyango, & Street, 1988; Morrow, Som-mer, Heinemann, & Tao, 1990). Operational problems are mostlikely to be faced, by using this system under field conditions(i.e. rain, moisture, dust, vibration). High price of the camerasand other auxiliary devices as well as inefficiency in detecting clodand muddy potatoes are other disadvantages of this method.

Recently, non destructive acoustical experiments have beenincreasingly implemented in detection and classification of agri-cultural products (Diezma-Iglesias, Ruiz-Altisent, & Barreiro,

12102 A. Hosainpour et al. / Expert Systems with Applications 38 (2011) 12101–12106

2004; Mahmoudi, 2006; Pearson, 2001; Pearson, Cetin, Tewfik, &Haff, 2007). A good correlation between fruit firmness and reso-nant frequency has been reported in some investigations (Garsia-Ramos, Ortiz-Canavate, Ruiz-Altisent, Diez, & Chavez, 2003; Jivanu-wong, 1998; Sugiyama, Katsurai, Hong, Koyama, & Mikuriya, 1998;Younce & Davis, 1995). Quite recently, Elbatawi (2008) used anacoustic impact method to detect hollow heart of potato tubers.Artificial neural networks (ANN) offer much faster and more flexi-ble approach in classification fields. Also, in noisy obscure pattern,ANN models are more efficient than statistical pattern classifiers(Kavdir & Guyer, 2008).

The main goal of this study is to investigate the feasibility ofusing impact sound signals for rapid detection of clods in potatoharvesters. An artificial neural network was used as a decisionmaking unit for this purpose. Experiment was performed in off-lineand on-line stages. In off-line experiments, potential features wereextracted from analysis of emitted sound signals, whereas in on-line stage, extracted features were used in real-time detection ofclods and potato tubers. Moreover, the effect of sample velocityon detection accuracy was investigated.

2. Materials and methods

About 500 kg of a mixture of potato tubers, clods and stoneswere collected just from the entrance of a potato harvester con-tainer in the 2008 harvesting season. Four varieties of potato tu-bers namely Marfona, Agria, Kosima and Granola, commonlygrown in Iran were used in this study. All samples of potatoes,clods and stones were weighed and pieces smaller than 40 g wereneglected. Table 1 shows some physical characteristics of the usedsamples. During weighing, mixture was manually sorted into twodiscrete groups; potato and non-potato. Since the number ofstones was much lower than the number of clods (Table 1), hereaf-ter in this paper the non-potato category is simply called ‘‘clods’’. Itis worth mentioning that hard enough clods were selected as sam-ples and other flimsy clods were crumbled and removed beforereaching the potato container.

2.1. Experimental apparatus

A test rig was built to feed samples, drop them onto an impactplate, acquire sound signal upon impact, process the signal dataand discriminate between potato tubers and clods. The schematicdiagram of the experimental apparatus is shown in Fig. 1. The sys-tem consisted of a feeding platform, an impact plate, an acousticunit and a PC based data acquisition system. A 5 m long and0.35 m wide belt conveyor was designed and fabricated as feedingplatform. The belt conveyor was driven by a 1 kW hp electric mo-tor. The velocity of the conveyer belt could be changed by using avariable frequency drive (Inverter IG5). Some special cups weremounted on the belt for singulating and better control of the sam-ple movement. The impact plate was a polished block of stainlesssteel approximately 22.5 � 30 � 1.5 cm. To study the effect ofvelocity on detection accuracy, four levels of belt conveyer velocity

Table 1Numbers and physical characteristic of used samples.

Samples Variety Number N Mean weight (g) Stda (g)

Potato Marfona 378 186 82Agria 443 158 73Kosima 446 150 88Granola 625 115 50

Clod 1483 123 72Stone 38 108 40

a Standard deviation.

at 1, 1.5, 2 and 2.5 m s�1 were examined throughout the study. Theimpact plate position was adjusted by trial and error method for allvelocities (Table 2).

A low cost Panasonic Electret capsule microphone (VM-034CYmodel), sensitive to frequencies up to 100 kHz, was used for cap-turing impact sound signals. The microphone was installed insidean isolated acoustic chamber to eliminate environmental noise ef-fects. To prevent the chamber from acoustic reflections, it wasfilled with glass wool. For further environmental noise elimination,the data acquisition system was triggered by a piezoelectric sensormounted on the impact plate. By adding this electronic circuit(main board and microcontroller) only impact emission sound sig-nals from samples were recorded and the environmental noises didnot interfere with the actual desired signals. Microphone outputwas send to a PC based data acquisition system, where it was dig-itized using a sound card (Intel� 82801 BA/BAM AC‘97Audio con-troller) at a sampling frequency of 44.1 kHz, with 16 bitresolution. A personal computer (Pentium IV) was used for acquir-ing, saving and processing of data as well as controlling the system.

A schematic diagram describing the overall detection system isshown in Fig. 2. A parallel hardware and software architecture isused to perform the described duty. In each stage of off-line exper-iment, about 45 samples of potato or clod were manually loaded inthe cups on the belt. By switching the belt conveyor power on,samples were made to fall onto the impact plate separately andone bye one. The emitted sound signals were acquired by themicrophone, digitized by the sound card and saved by using Mat-lab data acquisition toolbox (MathWorks, 2007). Similar proce-dures were repeated for all belt velocities. Since the maximumfrequency of the sound card was 44.1 kHz and data acquisitioncontinued for 11.61 ms after triggering, upon receiving a triggersignal the computer acquired 512 data points from every samplein the time-domain. Matlab software was used for data collectionand management.

2.2. Signal processing and feature extraction

For extracting potential features, recorded sound signals wereprocessed and analyzed in both time and frequency-domains. Typ-ical emitted sound signals from potatoes and clods are shown inFig. 3. Although the maximum peak values of clod sound signalswere slightly larger than potato ones, the preliminary attemptsto use only time-domain features were not successful. However,in order not to lose any useful transient feature, all 512 data pointamplitude (Amp) values were considered as features. A 1024-pointfast Fourier transform (FFT) was computed from each sound signal,using a Mathwork Windows.

The magnitude, power spectral density (PSD) and peak fre-quency (the frequency corresponding to the peak value of FFT infrequency spectrum) of each spectrum was computed and a lowpass filter was applied to remove the jagged spikes in the spec-trum. In addition to PSD and peak frequency, the 25 normalizedFFT magnitudes before peak frequency and 25 points afterwardswere saved as another frequency-domain feature. Normalizedspectrum was calculated by dividing the magnitude of FFT at eachfrequency by maximum value of spectrum (Fig. 4). After processingand analysis of emitted sound signals in both time and frequencydomain, following features were considered as potential discrimi-nating features: 512 amplitude (Amp) values in time domain; thepeak frequency (PF); 50 normalized FFT (NFFT) magnitudes cen-tered about the peak frequency and the entire 512 PSD magnitudesin frequency domain. A total of 1075 features were obtained foreach sample. For real time systems, this dimension of the inputvector is large, but the components of the vectors are highly corre-lated. Principle Component Analysis (PCA) was used to reduce thedimension of the input vectors to a maximum of 20 features. Three

Fig. 1. (a) Schematics of experimental apparatus. (b) Position of impact plate respect to feeding platform.

Table 2Position of impact plate at different velocity of belt conveyer.

Belt velocityV m s�1

Horizontal distanceX cm

Vertical distanceY cm

Angle a(�)

1 25 9 301.5 27 8 302 30 7 352.5 34 5 35

A. Hosainpour et al. / Expert Systems with Applications 38 (2011) 12101–12106 12103

sets of features were utilized for PCA purposes: Amp, PSD andNFFT. After normalization of data, PCA analysis was performedon data using Matlab software. Table 3 shows the relation between

number of principle components and percentage of eliminatedcomponent variance.

2.3. Back propagation neural network

The multilayer perceptron (MLP) is one of the most widelyimplemented neural network topologies that are being used inthe classification applications. MLPs are normally trained withthe back propagation algorithm (BPA). The back propagation rulepropagates the errors through the network and allows adaptationof the hidden processing elements (PEs). The MLP is trained witherror correction learning, which means that the desired responsefor the system must be known a priori. After adequate training,

Data noitisiuqca

Feature extraction

Signal processing

Detection system

Interpretation system

Output

Fig. 2. The block diagram of the detection system.

Potato

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0 5 10 15 20

Time (ms)

Am

plitu

de (

V)

Clod

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 5 10 15 20

Time (ms)

Am

plitu

de (

V)

Fig. 3. Typical digitized impact sound signals from potato and clod.

0

0.2

0.4

0.6

0.8

1

1.2

0 840 1680 2520 3360 4200Frequency (Hz)

Nor

mal

ized

FFT

mag

nitu

de

PotatoClod

Fig. 4. Normalized frequency spectra obtained by FFT for potato and clod.

Table 3Relation among eliminated components variance and number of selected principlecomponents.

Percentage of eliminated components variance (%) Number of selectedfeatures

Ampa PSDb NFFTc

2 9 1 10.5 28 4 30.1 98 34 11

a Amplitude.b Power spectral density.c Normalized FFT magnitudes centered about the peak frequency.

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the network weights are adapted and employed for cross valida-tion in order to determine overall performance of the ANN model.Gradient descent with momentum (GDM) learning rule is animprovement to the straight GD rule in the sense that a momen-tum term is used to speed up learning and stabilizing convergence(Haykin, 1999). Therefore, the GDM method of learning was usedthroughout this study.

In order to minimize ANN training time, only one hidden layerwas considered in the network. The number of neuron in hiddenlayer was determined using an exhaustive search from 1 to 40nodes. The neural network with 29 nodes in hidden layer hadthe least standard deviation error as well as high stability. In devel-oping ANN models, the linear function at the input layer and thenon-linear hyperbolic tangent function at both hidden and outputlayer were used as transfer functions. Learning rate was 0.7throughout the momentum learning rule. As an additional guardagainst over-fitting, the data sets were divided into three randomlyselected data sets; 70% of data were used for training, 15% for test-ing and the remaining 15% were used for cross validation. Neuro-Solutions 5.0 software was used for designing and testing of ANNmodels (NeuroSolutions, 2005).

3. Results and discussion

To find the best combinations of potential features and optimalANN configuration, 31 different combinations of principle compo-nent features were selected and tested by neural network. Thesefeatures were fed to the ANN models and their performances weredetermined by evaluation of the mean square error (MSE), correctdetection rate (CDR) and correlation coefficient (r). Table 4 showsthe summery of off-line experiment using the belt velocity of1 m s�1. In summary, the best combination was nine amplitudes,4 PSD, 3 NFFT and one peak frequency feature. Table 5 shows theperformance of optimal ANN configuration. The final structure ofnetwork was 17 input nodes, 29 hidden nodes and 2 output nodes.This relatively low number of input vector was used to guardagainst the possibility of over-fitting the neural network. The peakfrequency feature had more potential to detect clods and potato tu-bers (Table 6). However, there are overlaps in some cases whichare mainly due to existence of muddy potatoes. High standarddeviation of clods peak frequency values (Table 6) is due to diver-sity of moisture content and texture of clods.

After extracting potential features in off-line stage, on-lineexperiment was performed to evaluate real-time detection accu-racy. In this stage, about 1000 samples of potatoes and clods wererandomly selected and off-line procedures were repeated for them.

Table 4Performance of off-line experiment for subsets of features.

Features Number of features Correct detection rate (%) Cross validation MSE

Potato Clod

Ampa 28 90.2 84.2 0.068Amp 9 87.4 73.6 0.163PSDb 4 89.3 91.4 0.032NFFTc 3 91.8 80.1 0.070PFd 1 88.6 92.5 0.018Amp + PSD 9 + 4 92.2 91.2 0.019Amp + NFFT 9 + 3 92.7 86.8 0.067PSD + NFFT 4 + 3 94.3 92.2 0.019Amp + PSD + NFFT 9 + 4 + 3 95.4 93.3 0.016Amp + PSD + PF 9 + 4 + 1 93.4 94.5 0.013Amp + NFFT + PF 4 + 3 + 1 95.2 95.4 0.013Amp + PSD + NFFT + PF 9 + 4 + 3 + 1 97.3 97.6 0.011

a Amplitude.b Power spectral density.c Normalized FFT magnitudes centered about the peak frequency.d Peak frequency.

Table 5Performance of optimal ANN model.

Performance Potato Clod

MSE 0.0113 0.0105CDRa (%) 97.3% 97.6%r 0.989 0.992

a Correct detection rate.

Table 6Distribution of peak frequency values.

Samples Peak frequency (Hz)

Maximum Minimum Mean Stda

Potato 1455 1180 1260 75Clod 3055 1429 1911 205

a Standard deviation.

Table 7Detection accuracy of on-line system at different belt velocity.

Belt velocity (m s�1) Correct detection Cross validationMSE

Potato Clod

Ratio % Ratio %

1 176/181

97.2 79/81

97.5 0.011

1.5 174/182

95.6 81/84

96.4 0.018

2 162/178

91 77/82

93.9 0.045

2.5 157/184

85.2 71/81

87.6 0.165

0

0.05

0.1

0.15

0.2

0.25

0 150 300 450 600 750 900Number of epochs

MSE

Training MSE

Cross validation MSE

Fig. 5. Learning curve with GDM algorithm for 1000 epochs.

A. Hosainpour et al. / Expert Systems with Applications 38 (2011) 12101–12106 12105

In this stage, data acquisition, signal processing and samples detec-tion were performed at the same time. Table 7 shows the perfor-mance of on-line system at different belt conveyer velocities.Increasing the belt velocity resulted in the reduction of detectionaccuracy and increase in the number of miss classified samples.Low detection accuracy in high velocity was caused by high soundsignal intensity. Although the distance between feeder and impactplate had been adjusted before, saturated and weak sound signalswere likely at the high velocity of belt conveyor. Using only onemicrophone does not seem to be sufficient in high speedseparation.

Fig. 5 shows the neural network training procedure. It illus-trates that in the first 100 epochs of the training and cross valida-tion, MSE reaches to an acceptable magnitude. Therefore, fordecreasing the training time we can consider only 100 epochs.The number of used features in relation to the size and diversityof the training sets indicated that, over-fitting of the neural net-work was not possible.

The signal acquisition and computational procedures tookabout 25 ms for a sample at throughput rate of 40 samples per sec-ond. However there were some mechanical disturbances that lim-ited the detection capacity under 15 samples per second. A potatoharvester by using this system can perform at the field capacity of7.2 ton hr�1.

4. Conclusion

In the present study, an acoustic-based intelligent system wasused for discriminating between potato tubers and clods. Mainadvantages of this system are high accuracy, large capacity andpracticality as well as requiring small space and low cost of equip-ment and computation. In order to maintain detection accuracy,belt velocities greater than 1.5 m s�1 are not recommended forthe conveyor. By devising four potato stream lines on the belt atthe velocity of 1 m s�1, detection capacity of 20 ton hr�1 with theaccuracy of 97% is possible. A rejection system is needed to studythe real separation accuracy. So, currently the design and develop-ment of a high speed rejecter is being conducted by the authors.

12106 A. Hosainpour et al. / Expert Systems with Applications 38 (2011) 12101–12106

Moreover, the effect of partially muddy potatoes on detectionaccuracy is being investigated too. Installing the mentioned systemon a potato harvester is needed to expand the application of thismethod.

References

Al-Mallahi, A., Kataoka, T., & Okamoto, H. (2008). Discrimination between potatotubers and clods by detecting the significant wavebands. Biosystems Engineering,100, 329–337.

Brantley, S. A., Hamann, D. D., & Whitfield, J. K. (1975). A multiple belt adjustableVee size grader for sweet potatoes and cucumbers. Transactions of the ASAE, 18,350–362.

Diezma-Iglesias, B., Ruiz-Altisent, M., & Barreiro, P. (2004). Detection of internalquality in seedless watermelon by acoustic impulse response. BiosystemsEngineering, 88, 221–230.

Elbatawi, I. E. (2008). An acoustic impact method to detect hollow heart of potatotubers. Biosystems Engineering, 100, 206–213.

Food and agriculture Organization of the United Nations. (2008). <http://www.fao.org/corp/statistics/en>.

Feller, R., Morgolin, E., Zacharin, A., & Pasternak, H. (1985). Development of a clodseparator for potato packing houses. Transactions of the ASAE, 28, 1019–1023.

Gan-Mor, S., Zacharin, A., Galili, N., Feller, R., & Margolin, E. (1986). Absorbing stoneimpact to enable separation from potatoes. Transactions of the ASAE, 29,1526–1529.

Garsia-Ramos, F. J., Ortiz-Canavate, J., Ruiz-Altisent, M., Diez, J., & Chavez, M. (2003).Development and implementation of an on-line impact sensor for firmnesssensing of fruits. Journal of Food Engineering, 58, 53–57.

Haykin, S. (1999). Neural networks: A comprehensive foundation. Prentice-Hall.Jivanuwong, S. ⁄⁄⁄⁄(1998). Nondestructive detection of hollow heart in potatoes using

ultrasonics. Master of Science thesis. Submitted to the faculty of VirginiaPolytechnic Institute and Stat University, Blacksburg, Virginia, pp. 76.

Kavdir, I., & Guyer, D. E. (2008). Evaluation of different pattern recognitiontechniques for apple sorting. Biosystems Engineering, 99, 211–219.

Mahmoudi, A. (2006). Development of a suitable algorithm using Artificial NeuralNetworks for sorting of pistachio nuts with closed shells using impact acoustics, PhDthesis, Department of Agricultural Machinery, University of Tehran, Karaj: Iran,p. 144.

Main, J. C. (1971). Potatoes in the UK, Developments in production and marketing.Span, 14, 87–90.

Marchant, J.A., Onyango, C.M., & Street, M.J. (1988). High speed sorting of potatoesusing computer vision. ASAE Paper No. 88–3540.

MathWorks, MATLAB User’s Guide, the Math Works, Inc., (2007).McGechan, M. B. (1980). An investigation into the damage sustained by different

varieties of potatoes during riddling to remove soil. Journal of AgriculturalEngineering Research, 25, 345–353.

Morrow, C.T., Sommer, J.H., Heinemann, P.H., & Tao, Y. (1990). Automated machinevision inspection of potatoes. ASAE Paper No. 90–3531.

NeuroSolutions for Excel, NeuroDimension, Inc., (2005).Pearson, T. C. (2001). Detection of pistachio nuts with closed shells using impact

acoustics. Applied Engineering in Agriculture, 17, 249–253.Pearson, T. C., Cetin, A. E., Tewfik, A. H., & Haff, R. P. (2007). Feasibility of impact-

acoustic emissions for detection of damaged wheat kernels. Digital SignalProcessing, 17, 617–633.

Shyam, M., Singh, V., & Singh, R. (1990). Design and development of potato grader.AMA, 21, 40–49.

Story, A. G., & Raghavan, G. S. V. (1973). Sorting potatoes from stones and soil clodsby infrared reflectance. Transactions of the ASAE, 16, 304–309.

Sugiyama, J., Katsurai, T., Hong, J., Koyama, H., & Mikuriya, K. (1998). Melon ripenessmonitoring by a portable firmness tester. Transactions of the ASAE, 41, 121–127.

Younce, F. L., & Davis, F. D. C. (1995). A dynamic sensor for cherry firmness.Transactions of the ASAE, 38, 1467–1476.