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Model for Predicting and Classifying Durian Fruit Based on Maturity and Ripeness Using Neural Network
MODEL FOR PREDICTING AND CLASSIFYING DURIAN FRUIT BASED ON MATURITY AND RIPENESS USING NEURAL NETWORK
Amin Rejo
1),
Suroso 1) ,I Wayan Budiastra 1), Hadi K.Punvadaria Slamet Susanto l), Yu) Y. Nazaruddin 3)
1>,
Dept. oJAgricultural Enginering. Bogor Agricultural University (lPB). Indonesia ]) Dept. oJAgronomy. Bogor Agricultural University (IPB).Indonesia 3) Dept. oJ Enginering Physics. Bandung Institute o.fTechnology (ITB). Indonesia
1)
Abstract: This study was aimed to develop the model to predict the maturity, ripeness and defects of durian based on its physical and chemical characteristics by using the neural network. The density and acoustic characteristics measurement was fed into the model as the inputs. which provided the levels of maturity and ripeness as the output. Data training were tested to models of neural network with various nodes in the hidden layer, i.e., 4, 6, 8, and 10 nodes. The results recommended the use of 6 nodes in the hidden layer that would provide the highest accuration of 100 % in classifying the durian based on its maturity and ripeness. Copyright © 2001IFAC. Keywords: neural network, durian, maturity, ripeness, zero moment power
1. INTRODUCTION
Durian fruit come from the plant of Durio zibethinus,Murr. is a specific tropical crop that widely known, and nicknamed as " King oJTropical Fruit". Indonesia, so far, did not get much profit from its durian exported. Subadrabandhu et al., ( 1991) reported that Thailand exported durian more than 11,000 tons to various countries in Asia, America, and Europe every year. The export volume was much greater than the average e"'POrt of indonesia which was 53,767 kgs in 1990 and decreased to 13,794 kgs in 1998. Although the total durian production of Indonesia was quite high, but the qual ity was still inconsistent. This was due to
321
various factors, among of them was the post harvest handling. One of the greater problem commonly faced by the con summers was to select the fruits based on their maturity, ripeness and defects without peeling the skin. Until now, the determination of the fruit contents and defects have been done by ex1raction method or destructive method where by this way. the durian have to be opened up. Ultrasonic wave can penetrate through high density materials where the acoustic characters, namely the sound velocity and the absorption coefficient are able to show the quality contents and the defect level inside the fruits (Budiastra et al., 1999). Other teclmiques
such as the near infrared reflectance (NIR) have been proved to penetrate only 5 mm in depth from a fruit surface (Ikeda et al., 1992), while X-ray can penetrate through the fruit but results in a high cost. Quality evaluation of fruits and vegetables using ultrasonic has been reported by some researchers. Cheng and Haugh (1994) detected the hollow heart of potatoes, Mizrach et al. (1989, and 1997) applied the method to determine the maturity of mango fruit, and Budiastra et al. (1999) investigated the utilisation of the ultrasonic system for internal quality eval uation of durian. Hruyanto (2001), found out that the zero moment power, Mo can differentiate the ilTUnature durians to the mature ones, by their reapective value 5.877 and 2.505. The relationship, so far, has been done by using a linear mathematical model. In reality, it is a complex system because of the continuous change of durian physico-chemical properties. A neural network model that can relate the acoustic character to the physico-chemicals of durian fruit may solve the problem. Neural r:etwork has been long developed (RC';;enbIatt, 1957), but the application in agriculture :;(arted only in 1980s. Studies was carried out by Susanto et al. (2000) applying the neural network in sorting mangoes based on the concentrations of sucrose and malic acid in the frui t measured by near infrared reflectance (NJR). The objectives of this study were to develop and to validify a neural network model in predicting and to classifying the durian fruits based on their maturity and ripeness.
2.
EXPERIMENTAL
2.1. Material
Material used in this study was durian cultivar A ceupan from the citizenry plantation in Rancamaya, Bogor with the following characteristics : tapering shape. 2512 g in average weight., 16.5 cm in di(jlileter, 20.2 cm in length, brownish-light green skin. and ye llowish-white pulp. TIle Indonesian nati onal standard (SNI, 1997) stated that the durian was a short-age type which would fully- matured in 120-135 days after the peak blossom. In this study, the fruits were classified into the following groups: inuualure (1 17 days after peak blossom), fullyma ture (120 days after peak blossom), ripe (stored fo r 2 days after harvest), and overripe (stored for 4 days after harvest).
2.2. Procedures
The experimental apparatus for measuring tlle transmission of ultrasonic wave in durian fruits was set up according to Budiastra et al. (1999), as described in Fig. 1. A transducer of 50 kHz was used for measuring the characteristics of durian fruit. The durian fruits, were placed between the transmitter and the receiver transducers which had been applied by the silicon grease to enhance the coupling effect. The pulse from the ultrasonic tester ToU! was sent to the transmitter transducer T After propagating inside the durian fruit. the ultrasonic wave was detected by the receiver transducer R and then was sent to the ultrasonic ':ester Rin. The signal from the ultrasonic tester Rout was observed by the analog oscilloscope and digitized in the digital oscilloscope. The signal stored in the digital oscilloscope was then transferred to the PC computer through the interface PC Lab Card for further processing and analysis. After the acoustic measurement, the density was detennined, and tllen each durian fruit was opened up and subjected to the physico-chemical analysis covering tlle water content., total soluble solid, total sugar. firmness and total acid. The neural network model used the multi layer perceptron with two inputs, density and the acoustic characteristic, i.e. , Mo, the zero moment power (Fig. 2). Four outputs were observed, namely, inunature (IM), fully-mature (FM). ripe (R). andoverripe (OR). Data used for training the model was 66 pairs, and for the validation was 34. The simulation was carried out in various number of nodes in the hidden layer: 4,6, 8. and 10. and using 1000 and 5000 iterations. ANALOG OSCILLOSCOPE
Tout r - - - - - - - , ULTRASONIC TESTER PCLAB.
CARD
PERSONAL COMPUTER
Fig. 1. Blok diagram of the ultrilsonic apPilllll us (Bud iastra et al., 1999)
322
Mo, zero moment power, had a similar pattern to the density. Mo decreased as the maturity and ripeness increased. The Mo value was down from 0.77 for the immature fruit to 0.10 for tile overripe. The durian also lost its firmness from 3.84 N at the immature state to 0.49 N at the fully-immature 0.25 N at the ripe, and to 0.17 N at the overripe state. In contrary, the water content, and total sugar increased along with the increasing maturity and ripeness. The water content moved up from 62.5% to 84.70/0, and the total sugar from 3.1 % 10 16.0 % when the immature fruil changed to fully-mature, ripe and overripe.
Bias
Table 1. The correlation coefficient between the density and acoustic properties the physicochemical characteristics of durian fruit Physico-chemical Density Density Density Density
Hidden Layer Fig. 2. Neural network structure to predict the maturity and ripeness of durian
3.
RESULTS AND DISCUSSION
Zero Moment Power Zero Moment Power Zero Moment Power Zero Moment Power
characteristics r, n=IOO Water content -0.922 -0. 932 Total sugar Total acid -0.392 Total soluble -0.972 solid Water content -0.781 Total sugar -0. 720 Total acid -0.865 Total soluble -0.702 solid
3. J. Relationship ofAcoustic and Physico--Chemical characteristics
3.2. Neural Network Training Fig. 3 presented the relationship of the fruit physicochemical and acoustic natures resulted from the experiments. The density of durian decreased with the increasing maturity and ripeness, as from 970 kg/m3 at the immature state to 918 kg/cm) at the fully immature, 887 kg/cm3 at the ripe, and 850 kg/cm) at the overripe state.
The neural network training was carried out using 66 training data pairs and various nodes 4, 6, 8 and 10 in the hidden layer. One thousand and five thous and iterations were applied.
0.004
100r-~====~====~====~~
0.0037
w en ex:
:1: 0.0034
0.1 J...._ _ _ _ _ _....::::a::==-_.....---.J
0.0031
Maturity and ripe ne as
--+- Density --'-Mo -e--Firmness -a-Waler content
+-----------~~--------
0.0028
_ _ Total Sugar -<>--Total acid ___ Total Soluble Solid
•
•
•
•
4
6
8
10
Nodes
1-+-1000 IteratlonS-+-5QO()lte~ra~~~ns I
Fig. 3. The average of Mo and physico-chemical characteristics of durian fruit classified by its maturity and ripeness.
323
Fig. 4. RMSE by various nodes at 1000 and 5000 iterations
The results indicated that the Root Mean Square Error (RMSE) of 5000 iterations at all nodes better than of 1000 iterations. The RMSE value for predicting the maturity and ripeness ranged from 0 .00383 to 0.00362 for 1000 iterations, and from 0.00287 to 0.00286 for 5000 iterations. The results suggested that the training model using 4, 6, 8 and 10 nodes in the hidden layer with 1000 and 5000 iterations was sufficient
3.3. Validation Validation was carried out by inputting weights obtained from training and 34 data pairs. Table 2. Validation values for classifiying durian based on its maturity and ripeness.
1000 iterations Classifi nodes 8 cation 4 6 10 [M lOO 100 lOO lOO 90 lOO 90 90 FM lOO 100 lOO lOO R 87.5 87.5 87.5 87.5 OR
5000 iterations 4 lOO 90 100 lOO
nodes 6 lOO lOO lOO lOO
8 lOO lOO lOO lOO
REFERENCES Budiastra, I W., A. Trisnobudi, and H.K Purwadaria. 1999. Ultrasonic System for automation of internal quality evaluation of durian. Proceedings of the 14 fh World Congress IFAC, International Federation of Automatic Control, Vol K. Beijing, P.R. China, 5-9 July 1999. Cheng, Y. and e.G. Haugh. 1994. Detecting hollow heart in potatoes using ultrasonic excitation. Transactions of ASAE, 37(1): 217-222. Galili N., A. Mizrach, and G. Rosenhouse . 1993. Ultrasonic testing of whole fruit for nondestructive quality evaluation. ASAE paper No. 93-6062 St. Joseph, Michigan : ASAE. Haryanto, B., H.KPurwadaria, I W. Budiastra, and A. Trisnobudi. 200 L Determination of durian fruit maturity by ultrasonic properties.
Journal of Knowledge and Agricultural of Technology 21 (1): 21-25. Hutabarat, L., S. 1990. Quality of durian (Durio Zibethinus) Var Otong and Sitokong based
10 100 lOO 100 100
The results presented in Table 2 indicated that the accuracy reached 100 % in c1assifiying the immature . and overripe fruits at aU nodes 4, 6, 8, and 10, and both iterations 1000 and 5000. The classification of the fully mature durian gave 10 % error at 4, 6, and 8 nodes using 1000 iterations, and 4 nodes using 5000 iterations. The 1000 iterations only reached 87.5 % accuracy to classify the overripe ones at all nodes, however, the situation was improved to no error when using 5000 iterations.
4. CONCLUSIONS 1. Density, finnness and ultrasound transmissibility (Mo) decreased with the increasing of maturity, ripeness and defects of durian. In the contrary, the total soluble solid, water content, total sugar, and total acid increased along with the increasing maturity and ripeness. 2. The neural network model predicted the maturity and ripeness of durian accurately. It provided 100 % accuracy in predicting the maturity and ripeness at 5000 iterations. 3. II is recommended that a neural network model be developed to classify the defect fruits from the healthy ones.
on harvest time and storage duration. Thesis, of Agronomy, Bogor Departement Agricultural University (IPB), Bogor. Indonesia. Ikeda. Y., I W. Budiastra and T. Nishizu. 1992. On predicting concentration of individual sugar and malic acid of trne fruits by near-infrared reflectance spectrofotometry. Proceedings
Advances on Agricultural Engineering and Technology Vo!. 2. 80gor, Indonesia 12-15 October 1992. Mizrach. A., N. Galili, and G. Rosenhouse. 1989. Detennination of fruit and vegetable eXCitatIOn. properties by ultrasonic Transactions ofASAE 32(6): 2053-2058. Mizrach A., U, Flitsmon, and Y. Fuchs. 1997. An ultrasonic non-destructive method for measuring maturity of mango fruit. Transactions ofASAE, 40(4): 1107-1111. Subadrabandhu, S., lMP. Schneeman and E. W.M. Verheij. 1991. Durio zibethinus Murr. In E. W.M Verheij and RE. Coronel (ed) . Edibel fruit and nuts. Plant Resource of
South East Asia (prosea) : (2) Susanto, Suroso, I W. Budiastra, and H.K.Purwadaria 2000. Classification of mango by artificial neural network based on near infrared difuse reflectance.
Proceedings 2 nd IFAClCIGR International Workshop on Bio-Robotics, Information Technology and In tellegent Control for Bioproduclion Systems. Sakai, Osaka, Japan, 25-26 November 2000.
324
Amin Rejo
1),
Suroso 1) ,I Wayan Budiastra 1), Hadi K.Punvadaria Slamet Susanto l), Yu) Y. Nazaruddin 3)
1>,
Dept. oJAgricultural Enginering. Bogor Agricultural University (lPB). Indonesia ]) Dept. oJAgronomy. Bogor Agricultural University (IPB).Indonesia 3) Dept. oJ Enginering Physics. Bandung Institute o.fTechnology (ITB). Indonesia
1)
Abstract: This study was aimed to develop the model to predict the maturity, ripeness and defects of durian based on its physical and chemical characteristics by using the neural network. The density and acoustic characteristics measurement was fed into the model as the inputs. which provided the levels of maturity and ripeness as the output. Data training were tested to models of neural network with various nodes in the hidden layer, i.e., 4, 6, 8, and 10 nodes. The results recommended the use of 6 nodes in the hidden layer that would provide the highest accuration of 100 % in classifying the durian based on its maturity and ripeness. Copyright © 2001IFAC. Keywords: neural network, durian, maturity, ripeness, zero moment power
1. INTRODUCTION
Durian fruit come from the plant of Durio zibethinus,Murr. is a specific tropical crop that widely known, and nicknamed as " King oJTropical Fruit". Indonesia, so far, did not get much profit from its durian exported. Subadrabandhu et al., ( 1991) reported that Thailand exported durian more than 11,000 tons to various countries in Asia, America, and Europe every year. The export volume was much greater than the average e"'POrt of indonesia which was 53,767 kgs in 1990 and decreased to 13,794 kgs in 1998. Although the total durian production of Indonesia was quite high, but the qual ity was still inconsistent. This was due to
321
various factors, among of them was the post harvest handling. One of the greater problem commonly faced by the con summers was to select the fruits based on their maturity, ripeness and defects without peeling the skin. Until now, the determination of the fruit contents and defects have been done by ex1raction method or destructive method where by this way. the durian have to be opened up. Ultrasonic wave can penetrate through high density materials where the acoustic characters, namely the sound velocity and the absorption coefficient are able to show the quality contents and the defect level inside the fruits (Budiastra et al., 1999). Other teclmiques
such as the near infrared reflectance (NIR) have been proved to penetrate only 5 mm in depth from a fruit surface (Ikeda et al., 1992), while X-ray can penetrate through the fruit but results in a high cost. Quality evaluation of fruits and vegetables using ultrasonic has been reported by some researchers. Cheng and Haugh (1994) detected the hollow heart of potatoes, Mizrach et al. (1989, and 1997) applied the method to determine the maturity of mango fruit, and Budiastra et al. (1999) investigated the utilisation of the ultrasonic system for internal quality eval uation of durian. Hruyanto (2001), found out that the zero moment power, Mo can differentiate the ilTUnature durians to the mature ones, by their reapective value 5.877 and 2.505. The relationship, so far, has been done by using a linear mathematical model. In reality, it is a complex system because of the continuous change of durian physico-chemical properties. A neural network model that can relate the acoustic character to the physico-chemicals of durian fruit may solve the problem. Neural r:etwork has been long developed (RC';;enbIatt, 1957), but the application in agriculture :;(arted only in 1980s. Studies was carried out by Susanto et al. (2000) applying the neural network in sorting mangoes based on the concentrations of sucrose and malic acid in the frui t measured by near infrared reflectance (NJR). The objectives of this study were to develop and to validify a neural network model in predicting and to classifying the durian fruits based on their maturity and ripeness.
2.
EXPERIMENTAL
2.1. Material
Material used in this study was durian cultivar A ceupan from the citizenry plantation in Rancamaya, Bogor with the following characteristics : tapering shape. 2512 g in average weight., 16.5 cm in di(jlileter, 20.2 cm in length, brownish-light green skin. and ye llowish-white pulp. TIle Indonesian nati onal standard (SNI, 1997) stated that the durian was a short-age type which would fully- matured in 120-135 days after the peak blossom. In this study, the fruits were classified into the following groups: inuualure (1 17 days after peak blossom), fullyma ture (120 days after peak blossom), ripe (stored fo r 2 days after harvest), and overripe (stored for 4 days after harvest).
2.2. Procedures
The experimental apparatus for measuring tlle transmission of ultrasonic wave in durian fruits was set up according to Budiastra et al. (1999), as described in Fig. 1. A transducer of 50 kHz was used for measuring the characteristics of durian fruit. The durian fruits, were placed between the transmitter and the receiver transducers which had been applied by the silicon grease to enhance the coupling effect. The pulse from the ultrasonic tester ToU! was sent to the transmitter transducer T After propagating inside the durian fruit. the ultrasonic wave was detected by the receiver transducer R and then was sent to the ultrasonic ':ester Rin. The signal from the ultrasonic tester Rout was observed by the analog oscilloscope and digitized in the digital oscilloscope. The signal stored in the digital oscilloscope was then transferred to the PC computer through the interface PC Lab Card for further processing and analysis. After the acoustic measurement, the density was detennined, and tllen each durian fruit was opened up and subjected to the physico-chemical analysis covering tlle water content., total soluble solid, total sugar. firmness and total acid. The neural network model used the multi layer perceptron with two inputs, density and the acoustic characteristic, i.e. , Mo, the zero moment power (Fig. 2). Four outputs were observed, namely, inunature (IM), fully-mature (FM). ripe (R). andoverripe (OR). Data used for training the model was 66 pairs, and for the validation was 34. The simulation was carried out in various number of nodes in the hidden layer: 4,6, 8. and 10. and using 1000 and 5000 iterations. ANALOG OSCILLOSCOPE
Tout r - - - - - - - , ULTRASONIC TESTER PCLAB.
CARD
PERSONAL COMPUTER
Fig. 1. Blok diagram of the ultrilsonic apPilllll us (Bud iastra et al., 1999)
322
Mo, zero moment power, had a similar pattern to the density. Mo decreased as the maturity and ripeness increased. The Mo value was down from 0.77 for the immature fruit to 0.10 for tile overripe. The durian also lost its firmness from 3.84 N at the immature state to 0.49 N at the fully-immature 0.25 N at the ripe, and to 0.17 N at the overripe state. In contrary, the water content, and total sugar increased along with the increasing maturity and ripeness. The water content moved up from 62.5% to 84.70/0, and the total sugar from 3.1 % 10 16.0 % when the immature fruil changed to fully-mature, ripe and overripe.
Bias
Table 1. The correlation coefficient between the density and acoustic properties the physicochemical characteristics of durian fruit Physico-chemical Density Density Density Density
Hidden Layer Fig. 2. Neural network structure to predict the maturity and ripeness of durian
3.
RESULTS AND DISCUSSION
Zero Moment Power Zero Moment Power Zero Moment Power Zero Moment Power
characteristics r, n=IOO Water content -0.922 -0. 932 Total sugar Total acid -0.392 Total soluble -0.972 solid Water content -0.781 Total sugar -0. 720 Total acid -0.865 Total soluble -0.702 solid
3. J. Relationship ofAcoustic and Physico--Chemical characteristics
3.2. Neural Network Training Fig. 3 presented the relationship of the fruit physicochemical and acoustic natures resulted from the experiments. The density of durian decreased with the increasing maturity and ripeness, as from 970 kg/m3 at the immature state to 918 kg/cm) at the fully immature, 887 kg/cm3 at the ripe, and 850 kg/cm) at the overripe state.
The neural network training was carried out using 66 training data pairs and various nodes 4, 6, 8 and 10 in the hidden layer. One thousand and five thous and iterations were applied.
0.004
100r-~====~====~====~~
0.0037
w en ex:
:1: 0.0034
0.1 J...._ _ _ _ _ _....::::a::==-_.....---.J
0.0031
Maturity and ripe ne as
--+- Density --'-Mo -e--Firmness -a-Waler content
+-----------~~--------
0.0028
_ _ Total Sugar -<>--Total acid ___ Total Soluble Solid
•
•
•
•
4
6
8
10
Nodes
1-+-1000 IteratlonS-+-5QO()lte~ra~~~ns I
Fig. 3. The average of Mo and physico-chemical characteristics of durian fruit classified by its maturity and ripeness.
323
Fig. 4. RMSE by various nodes at 1000 and 5000 iterations
The results indicated that the Root Mean Square Error (RMSE) of 5000 iterations at all nodes better than of 1000 iterations. The RMSE value for predicting the maturity and ripeness ranged from 0 .00383 to 0.00362 for 1000 iterations, and from 0.00287 to 0.00286 for 5000 iterations. The results suggested that the training model using 4, 6, 8 and 10 nodes in the hidden layer with 1000 and 5000 iterations was sufficient
3.3. Validation Validation was carried out by inputting weights obtained from training and 34 data pairs. Table 2. Validation values for classifiying durian based on its maturity and ripeness.
1000 iterations Classifi nodes 8 cation 4 6 10 [M lOO 100 lOO lOO 90 lOO 90 90 FM lOO 100 lOO lOO R 87.5 87.5 87.5 87.5 OR
5000 iterations 4 lOO 90 100 lOO
nodes 6 lOO lOO lOO lOO
8 lOO lOO lOO lOO
REFERENCES Budiastra, I W., A. Trisnobudi, and H.K Purwadaria. 1999. Ultrasonic System for automation of internal quality evaluation of durian. Proceedings of the 14 fh World Congress IFAC, International Federation of Automatic Control, Vol K. Beijing, P.R. China, 5-9 July 1999. Cheng, Y. and e.G. Haugh. 1994. Detecting hollow heart in potatoes using ultrasonic excitation. Transactions of ASAE, 37(1): 217-222. Galili N., A. Mizrach, and G. Rosenhouse . 1993. Ultrasonic testing of whole fruit for nondestructive quality evaluation. ASAE paper No. 93-6062 St. Joseph, Michigan : ASAE. Haryanto, B., H.KPurwadaria, I W. Budiastra, and A. Trisnobudi. 200 L Determination of durian fruit maturity by ultrasonic properties.
Journal of Knowledge and Agricultural of Technology 21 (1): 21-25. Hutabarat, L., S. 1990. Quality of durian (Durio Zibethinus) Var Otong and Sitokong based
10 100 lOO 100 100
The results presented in Table 2 indicated that the accuracy reached 100 % in c1assifiying the immature . and overripe fruits at aU nodes 4, 6, 8, and 10, and both iterations 1000 and 5000. The classification of the fully mature durian gave 10 % error at 4, 6, and 8 nodes using 1000 iterations, and 4 nodes using 5000 iterations. The 1000 iterations only reached 87.5 % accuracy to classify the overripe ones at all nodes, however, the situation was improved to no error when using 5000 iterations.
4. CONCLUSIONS 1. Density, finnness and ultrasound transmissibility (Mo) decreased with the increasing of maturity, ripeness and defects of durian. In the contrary, the total soluble solid, water content, total sugar, and total acid increased along with the increasing maturity and ripeness. 2. The neural network model predicted the maturity and ripeness of durian accurately. It provided 100 % accuracy in predicting the maturity and ripeness at 5000 iterations. 3. II is recommended that a neural network model be developed to classify the defect fruits from the healthy ones.
on harvest time and storage duration. Thesis, of Agronomy, Bogor Departement Agricultural University (IPB), Bogor. Indonesia. Ikeda. Y., I W. Budiastra and T. Nishizu. 1992. On predicting concentration of individual sugar and malic acid of trne fruits by near-infrared reflectance spectrofotometry. Proceedings
Advances on Agricultural Engineering and Technology Vo!. 2. 80gor, Indonesia 12-15 October 1992. Mizrach. A., N. Galili, and G. Rosenhouse. 1989. Detennination of fruit and vegetable eXCitatIOn. properties by ultrasonic Transactions ofASAE 32(6): 2053-2058. Mizrach A., U, Flitsmon, and Y. Fuchs. 1997. An ultrasonic non-destructive method for measuring maturity of mango fruit. Transactions ofASAE, 40(4): 1107-1111. Subadrabandhu, S., lMP. Schneeman and E. W.M. Verheij. 1991. Durio zibethinus Murr. In E. W.M Verheij and RE. Coronel (ed) . Edibel fruit and nuts. Plant Resource of
South East Asia (prosea) : (2) Susanto, Suroso, I W. Budiastra, and H.K.Purwadaria 2000. Classification of mango by artificial neural network based on near infrared difuse reflectance.
Proceedings 2 nd IFAClCIGR International Workshop on Bio-Robotics, Information Technology and In tellegent Control for Bioproduclion Systems. Sakai, Osaka, Japan, 25-26 November 2000.
324