A Frame Work for the Development of a Suitable Method to Find Shoot Length at Maturity of Mustard Plant Using Soft Computing Model
The production of a plant can be measured in terms of
seeds. The generation of seeds plays a critical role in our social and
daily life. The fruit production which generates seeds, depends on the
various parameters of the plant, such as shoot length, leaf number,
root length, root number, etc When the plant is growing, some leaves
may be lost and some new leaves may appear. It is very difficult to
use the number of leaves of the tree to calculate the growth of the
plant.. It is also cumbersome to measure the number of roots and
length of growth of root in several time instances continuously after
certain initial period of time, because roots grow deeper and deeper
under ground in course of time. On the contrary, the shoot length of
the tree grows in course of time which can be measured in different
time instances. So the growth of the plant can be measured using the
data of shoot length which are measured at different time instances
after plantation. The environmental parameters like temperature, rain
fall, humidity and pollution are also play some role in production of
yield. The soil, crop and distance management are taken care to
produce maximum amount of yields of plant. The data of the growth
of shoot length of some mustard plant at the initial stage (7,14,21 &
28 days after plantation) is available from the statistical survey by a
group of scientists under the supervision of Prof. Dilip De. In this
paper, initial shoot length of Ken( one type of mustard plant) has
been used as an initial data. The statistical models, the methods of
fuzzy logic and neural network have been tested on this mustard
plant and based on error analysis (calculation of average error) that
model with minimum error has been selected and can be used for the
assessment of shoot length at maturity. Finally, all these methods
have been tested with other type of mustard plants and the particular
soft computing model with the minimum error of all types has been
selected for calculating the predicted data of growth of shoot length.
The shoot length at the stage of maturity of all types of mustard
plants has been calculated using the statistical method on the
predicted data of shoot length.
[1] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series part I", Fuzzy Sets and Systems 54(1993) 1 - 9.
[2] J. Sullivan and William H. Woodall, "A Comparison of Fuzzy
Forecasting and Markov Modelling", Fuzzy Sets and Systems 64(1994)
279 - 293.
[3] Q. Song and B. S. Chissom, "Fuzzy Time Series and its Models", Fuzzy
Sets and Systems 54(1993) 269-277.
[4] H. Bintley, "Time Series analysis with REVEAL", Fuzzy Sets and
Systems 23(1987) 97-118.
[5] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series - part II", Fuzzy Sets and Systems 62(1994) 1-8.
[6] G. A. Tagliarini, J. F. Christ, E. W. Page, "Optimization using Neural
Networks, IEEE Transactions on Computers. vol. 40. no 12. December
ÔÇÿ91 1347-1358.
[7] T. K. Bhattacharya and T. K. Basu, "Medium range forecasting of power
system load using modified Kalman filter and Walsch transform",
Electric Power and Energy Systems", vol. 15, no 2, 109 -115, 1993.
[8] F. G. Donaldson and M. Kamstra, "Forecast combining with Neural
Networks", Journal of Forecasting 15(1996) 49-61.
[9] J. V. Hansen and Ray D. Nelson, "Neural Networks and Traditional
Time Series Methods: A Synergistic combination in state Economic
Forecasts", IEEE Transactions on Neural Networks vol. 8, no 4, July
1997.
[10] S. F. Brown, A. J. Branford, W. Moran, "On the use of Artificial Neural
Networks for the analysis of Survival Data", IEEE Transactions on
Neural Networks, vol. 8, no 5, Sept. 1997.
[11] M. Sugeno and K. Tanaka, "Successive Identification of a Fuzzy Model
and its applications to prediction of a complex system", Fuzzy Sets and
Systems 42(1991) 315-334.
[12] L. Zuoyong, Chen Zhenpei, Li Jitao, "A model of Weather Forecast by
Fuzzy Grade Statistics", Fuzzy Sets and Systems 26(1998) 275-281.
[13] L. feng and Xu Xia Guang, "A Forecasting Model of Fuzzy Self
Regression", Fuzzy Sets and Systems 58(1993) 239-242.
[14] M. Ishikawa, T. Moriyama, "Prediction of Time Series by a Structural
Learning of Neural Networks", Fuzzy Sets and Systems 82(1996) 167-
176.
[15] D.H.Goldberg, "Genetic Algorithms in Search, Optimization and
Machine Learning", Addison Wesley Inc, 1999.
[16] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Some
Issues in building a Fuzzy Neural Network based Framework for
forecasting Engineering Manpower", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 213-227,
October -November 1999.
[17] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Rule
Base of a Fuzzy Expert Selection System", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 98-104, October
-November 1999.
[18] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "A
Fuzzy Time Series based Framework in the Forecasting Engineering
Manpower in comparison to Markov Modeling", Proceedings of
Seminar on Information Technology, The Institution of
Engineers(India), Computer Engineering Division, West Bengal State
Center, Calcutta, pp. 39-45, March 2000.
[19] G. P. Bansal, A. Jain, A. K. Tiwari and P. K. Chanda, "Optimization in
the operation of Process Plant through Genetic Programming", IETE
Journal of Research, vol 46, no 4, July-August 2000, pp 251-260.
[20] K. K. Shukla, Neuro-genetic prediction of Software Development
Effort", Information and Software Technology 42(2000), pp 701-713.
[21] S. Bandyopadhaya and U. Maulik, "An Improved Evolutionary
Algorithm as Function Optimizer", IETE Journal of Research, vol 46, no
1 and 2, pp 47-56, 2000.
[22] B. Banerjee, A. Konar and S. Mukhopadhayay, " A Neuro-GA approach
for the Navigational Planning of a Mobile Robot", Proceedings of
International Conference on Communication, Computers and
Devices(ICCD-2000), Department of Electronics and Electrical
Engineering, Indian Institute of Technology, Kharagpur, December
2000, pp 625-628.
[23] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee,
"Forecasting using Time Series Model Direct Method in comparison to
Indirect Method", Proceedings of International Conference on
Communication, Computers and Devices(ICCD-2000), Department of
Electronics and Electrical Engineering, Indian Institute of Technology,
Kharagpur, December 2000, pp 655-658.
[24] R.A. Aliev and R. R. Aliev, "Soft Computing and its applications",
World Scientific, 2002.
[25] G.W.Snedecor and W.G.Cochran, "Statistical Methods", eight edition,
East Press, 1994.
[26] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof Dilip Dey, Prof. S.
K. Mukherjee, Bayesian Learning versus Neural Learning : towards
prediction of Pod Yield", Proceedings of National Seminar on Recent
advances on Information Technology(RAIT-2007), Department of
Computer Science and Engineering, Indian School of Mines University,
Dhanbad, India , pp 298-313, February 2007.
[27] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof. S. K. Mukherjee,
"Shoot Length Growth Prediction of Paddy Plant using Neural Fuzzy
Model", Proceedings of National Conference on Cutting Edge
Technologies in Power Conversion & Industrial Drives PCID,
Department of Electrical and Electronics Engineering , Bannari Amman
Institute of Technology, Sathyaamangalam, Tamilnadu, India , pp 266-
270, February 2007.
[1] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series part I", Fuzzy Sets and Systems 54(1993) 1 - 9.
[2] J. Sullivan and William H. Woodall, "A Comparison of Fuzzy
Forecasting and Markov Modelling", Fuzzy Sets and Systems 64(1994)
279 - 293.
[3] Q. Song and B. S. Chissom, "Fuzzy Time Series and its Models", Fuzzy
Sets and Systems 54(1993) 269-277.
[4] H. Bintley, "Time Series analysis with REVEAL", Fuzzy Sets and
Systems 23(1987) 97-118.
[5] Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time
series - part II", Fuzzy Sets and Systems 62(1994) 1-8.
[6] G. A. Tagliarini, J. F. Christ, E. W. Page, "Optimization using Neural
Networks, IEEE Transactions on Computers. vol. 40. no 12. December
ÔÇÿ91 1347-1358.
[7] T. K. Bhattacharya and T. K. Basu, "Medium range forecasting of power
system load using modified Kalman filter and Walsch transform",
Electric Power and Energy Systems", vol. 15, no 2, 109 -115, 1993.
[8] F. G. Donaldson and M. Kamstra, "Forecast combining with Neural
Networks", Journal of Forecasting 15(1996) 49-61.
[9] J. V. Hansen and Ray D. Nelson, "Neural Networks and Traditional
Time Series Methods: A Synergistic combination in state Economic
Forecasts", IEEE Transactions on Neural Networks vol. 8, no 4, July
1997.
[10] S. F. Brown, A. J. Branford, W. Moran, "On the use of Artificial Neural
Networks for the analysis of Survival Data", IEEE Transactions on
Neural Networks, vol. 8, no 5, Sept. 1997.
[11] M. Sugeno and K. Tanaka, "Successive Identification of a Fuzzy Model
and its applications to prediction of a complex system", Fuzzy Sets and
Systems 42(1991) 315-334.
[12] L. Zuoyong, Chen Zhenpei, Li Jitao, "A model of Weather Forecast by
Fuzzy Grade Statistics", Fuzzy Sets and Systems 26(1998) 275-281.
[13] L. feng and Xu Xia Guang, "A Forecasting Model of Fuzzy Self
Regression", Fuzzy Sets and Systems 58(1993) 239-242.
[14] M. Ishikawa, T. Moriyama, "Prediction of Time Series by a Structural
Learning of Neural Networks", Fuzzy Sets and Systems 82(1996) 167-
176.
[15] D.H.Goldberg, "Genetic Algorithms in Search, Optimization and
Machine Learning", Addison Wesley Inc, 1999.
[16] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Some
Issues in building a Fuzzy Neural Network based Framework for
forecasting Engineering Manpower", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 213-227,
October -November 1999.
[17] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "Rule
Base of a Fuzzy Expert Selection System", Proceedings of 34th Annual
Convention of Computer Society of India, Mumbai, pp. 98-104, October
-November 1999.
[18] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee, "A
Fuzzy Time Series based Framework in the Forecasting Engineering
Manpower in comparison to Markov Modeling", Proceedings of
Seminar on Information Technology, The Institution of
Engineers(India), Computer Engineering Division, West Bengal State
Center, Calcutta, pp. 39-45, March 2000.
[19] G. P. Bansal, A. Jain, A. K. Tiwari and P. K. Chanda, "Optimization in
the operation of Process Plant through Genetic Programming", IETE
Journal of Research, vol 46, no 4, July-August 2000, pp 251-260.
[20] K. K. Shukla, Neuro-genetic prediction of Software Development
Effort", Information and Software Technology 42(2000), pp 701-713.
[21] S. Bandyopadhaya and U. Maulik, "An Improved Evolutionary
Algorithm as Function Optimizer", IETE Journal of Research, vol 46, no
1 and 2, pp 47-56, 2000.
[22] B. Banerjee, A. Konar and S. Mukhopadhayay, " A Neuro-GA approach
for the Navigational Planning of a Mobile Robot", Proceedings of
International Conference on Communication, Computers and
Devices(ICCD-2000), Department of Electronics and Electrical
Engineering, Indian Institute of Technology, Kharagpur, December
2000, pp 625-628.
[23] J. Paul Choudhury, Dr. Bijan Sarkar and Prof. S. K. Mukherjee,
"Forecasting using Time Series Model Direct Method in comparison to
Indirect Method", Proceedings of International Conference on
Communication, Computers and Devices(ICCD-2000), Department of
Electronics and Electrical Engineering, Indian Institute of Technology,
Kharagpur, December 2000, pp 655-658.
[24] R.A. Aliev and R. R. Aliev, "Soft Computing and its applications",
World Scientific, 2002.
[25] G.W.Snedecor and W.G.Cochran, "Statistical Methods", eight edition,
East Press, 1994.
[26] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof Dilip Dey, Prof. S.
K. Mukherjee, Bayesian Learning versus Neural Learning : towards
prediction of Pod Yield", Proceedings of National Seminar on Recent
advances on Information Technology(RAIT-2007), Department of
Computer Science and Engineering, Indian School of Mines University,
Dhanbad, India , pp 298-313, February 2007.
[27] Dr. J. Paul Choudhury, Satyendra Nath Mandal, Prof. S. K. Mukherjee,
"Shoot Length Growth Prediction of Paddy Plant using Neural Fuzzy
Model", Proceedings of National Conference on Cutting Edge
Technologies in Power Conversion & Industrial Drives PCID,
Department of Electrical and Electronics Engineering , Bannari Amman
Institute of Technology, Sathyaamangalam, Tamilnadu, India , pp 266-
270, February 2007.
@article{"International Journal of Information, Control and Computer Sciences:49480", author = "Satyendra Nath Mandal and J. Pal Choudhury and Dilip De and S. R. Bhadra Chaudhuri", title = "A Frame Work for the Development of a Suitable Method to Find Shoot Length at Maturity of Mustard Plant Using Soft Computing Model", abstract = "The production of a plant can be measured in terms of
seeds. The generation of seeds plays a critical role in our social and
daily life. The fruit production which generates seeds, depends on the
various parameters of the plant, such as shoot length, leaf number,
root length, root number, etc When the plant is growing, some leaves
may be lost and some new leaves may appear. It is very difficult to
use the number of leaves of the tree to calculate the growth of the
plant.. It is also cumbersome to measure the number of roots and
length of growth of root in several time instances continuously after
certain initial period of time, because roots grow deeper and deeper
under ground in course of time. On the contrary, the shoot length of
the tree grows in course of time which can be measured in different
time instances. So the growth of the plant can be measured using the
data of shoot length which are measured at different time instances
after plantation. The environmental parameters like temperature, rain
fall, humidity and pollution are also play some role in production of
yield. The soil, crop and distance management are taken care to
produce maximum amount of yields of plant. The data of the growth
of shoot length of some mustard plant at the initial stage (7,14,21 &
28 days after plantation) is available from the statistical survey by a
group of scientists under the supervision of Prof. Dilip De. In this
paper, initial shoot length of Ken( one type of mustard plant) has
been used as an initial data. The statistical models, the methods of
fuzzy logic and neural network have been tested on this mustard
plant and based on error analysis (calculation of average error) that
model with minimum error has been selected and can be used for the
assessment of shoot length at maturity. Finally, all these methods
have been tested with other type of mustard plants and the particular
soft computing model with the minimum error of all types has been
selected for calculating the predicted data of growth of shoot length.
The shoot length at the stage of maturity of all types of mustard
plants has been calculated using the statistical method on the
predicted data of shoot length.", keywords = "Fuzzy time series, neural network, forecasting error,average error.", volume = "2", number = "1", pages = "5-8", }
