DEVELOPMENT OF A COMPUTER PROGRAM USING VISUAL BASIC FOR PREDICTING PERFORMANCE PARAMETERS OF TILLAGE IMPLEMENTS

Al-Hamed, Saad; Wahby, Mohammed; Aboukarima, Abdulwahed; Ahmed, Khaled

doi:10.21608/mjae.2014.99147

DEVELOPMENT OF A COMPUTER PROGRAM USING VISUAL BASIC FOR PREDICTING PERFORMANCE PARAMETERS OF TILLAGE IMPLEMENTS

Document Type : Original Article

Authors

¹ Professor, Dept. of Agric. Eng., Coll.of Food and Agric. Sci., King Saud University, Saudi Arabia.

² Senior Res., Agric. Eng. Res. Inst., Agric. Res. Centre, Egypt

³ Assistant Professor at Shaqra University, Saudi Arabia.

⁴ Researcher, Agric. Eng. Res. Inst., Agric. Res. Centre,

⁵ Assi. Prof. at Dept. of Agric. Engi., College of Food and Agric. Sci., King Saud Univ., Saudi Arabia.

10.21608/mjae.2014.99147

Abstract

This research effort is looking at alternatives to equation-based modeling; the ability to make predictions without creating equations to describe variable relations satisfies the research goals. A program for predicting performance of tillage implements in visual basic based on trained artificial neural network was developed. The program was designed to predict the required draft and energy of chisel, moldboard and disk plows. Field efficiency for each plow was assumed. The results of the program application are found almost identical with actual performance data of the selected plows. The fuel consumption could be determined through rated PTO power of a tractor. Hence, the developed program can be used to aid decision – makers in selecting the optimum tractor–machine complement size during operation under specific conditions of the soil and both plowing speed and depth. The specifications of each plow were considered as inputs in the program. The benefit of the program is to assist graduate and undergraduate students in agricultural engineering field to estimate data performance of tillage implements.

References

Aboukarima, A. M. (2007). Draft models of chisel plow based on simulation using artificial neural networks. Misr J. Ag. Eng., 24(1): 42-61.

Aboukarima, A. M. (2013). Evaluation of using a combination of simulated and experimental data to predict draft force of a moldboard plow. Misr J. Ag. Eng., 30 (1): 1 - 28

Aboukarima, A.M. (2004). Artificial neural networks configuration for predicting performance of tillage implements under Egyptian conditions. PhD Thesis, Agric. Eng. Dept., Faculty of Agric., Ain Shams University, Egypt.

Aboukarima, A.M. and A.F. Saad (2006). Assessment of different indices depicting soil texture for predicting chisel plow draft using neural networks. Alexandria Science Exchange J., 27(2): 170-180.

Aboukarima, A.M., M.N. El Awady, A.G. El Kabany and M.H.A. Kabeel (2003). Plows performance under Egyptian conditions depicted by artificial neural networks. Misr J. Agric. Eng., 20 (4): 919-936.

Adamchuk , V. I., A. V. Skotnikov, J. D. Speichinger and M. F. Kocher (2003). Instrumentation system for variable depth tillage. ASAE Paper number 031078, ASAE Annual International Meeting July 27-30, 2003.

Alam, M. M. (1989). Soil reaction forces on agricultural disk implements. PhD Thesis, University of Newcastle, UK.

Alam, M., M.M. Hossain, M.A. Awal (2001). Selection of farm power by using a computer system. AMA, 32(1):65-68.

Al-Hamed, S.A. and M. F. Wahby (2013). Unpublished final report for the project entitled “Modeling of energy consumption during seed bed preparation operation based on soil mechanical properties", No. 09-SPA876-02. National Plan for Science, Technology and Innovation Program, King Saud University, Saudi Arabia.

Al-Hamed, S.A. and A.A. Al-Janobi (2001). A program for predicting tractor performance in Visual C++. Comput. Electron. Agric. 3 (2001), 137–149.

Al-Hamed, S.-A., R.D. Grisso, F.M. Zoz and K.Von Bargen (1994). Tractor performance spreadsheet for radial tires. Comput. Electron. Agric. 10 (1994), 45–62.

Alhassan, J. K. and S. Misra (2011).Using a weightless neural network to forecast stock prices: A case study of Nigerian stock exchange. Scientific Research and Essays, 6 (14):2934-2940.

Alimardani, R., Y. Abbaspour-Gilandeh , A. Khalilian, A. Keyhani and S. H. Sadati (2009). Prediction of draft force and energy of subsoiling operation using ANN model.Journal of Food, Agriculture & Environment Vol.7 (3&4) : 537 – 542.

Al-Jalil, H. F, A. Khdair and W Mukahal (2001). Design and performance of an adjustable three-point hitch dynamometer . Soil & Tillage Research , 62(3):153-156.

Al-Janobi, A. (2000). A Data-acquisition System to Monitor Performance of Fully Mounted Implements. Journal of Agricultural Engineering Research - J AGR ENG RES. 01/2000; 75(2):167-175.

Al-Janobi, A. A., M. F. Wahby and M. A. Al-Belakhy (1998). A laptop computer based data acquisition system to monitor tractor performance. ASAE, paper No: 98-3108.

Al-Janobi, A., S. Al-Hamed and A.M. Aboukarima (2010). An excel spreadsheet to estimate performance parameters for chisel plow-tractor combination based on trained an artificial neural network. Bulletin USAMV Agriculture, 67(2):1-9.

Al-Janobi, A., S. Al-Hamed S. and A. M. Aboukarima (2010). Educational visual C++ program aid to estimate fuel consumption for tillage equipment based on trained artificial neural network. Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Agriculture 2010 Vol. 67 No. 1 pp. 309.

Al-Janobi, A.A. and S.A. Al-Suhaibani (1995). Setup of a calibration rig in a mobile instrumentation package. Alexand. J. Agric. Res., 40: 1-15.

Al-Janobi, A.A. and S.A. Al-Suhaibani (1996). Performance of a three point linkage-implement depth transducer. Misr J. Agric. Eng., 13: 545-557.

Al-Janobi, A.A. and S.A. Al-Suhaibani (1998). Draft of primary tillage implements in sandy loam soil. Applied Engineering in Agriculture, 14(4):343– 348.

Al-Janobi, A.A., A.M. Aboukarima and K.A. Ahmed (2001). Prediction of specific draft of different tillage implements using neural networks. Misr J. Agric. Eng., 18 (3): 669-714.

Al-Janobi, A.A., S.A. Al-Suhaibani, A.A. Bedri and A.S. Babeir (1997). A precision wheel torque and weight transducer for most common agricultural tractors. Agric. Mech. Asia Afr. Latin Am., 28: 13-17.

Al-Suhaibani, S.A. and A.A. Al-Janobi (1996). An instrumentation system for measuring field performance of agricultural tractors. Misr J. Agric. Eng., 13: 516-528.

Al-Suhaibani, S.A., A.A. Al-Janobi and Y.N. Al-Majhadi (2010). Development and Evaluation of Tractors and Tillage Implements Instrumentation System. American J. of Engineering and Applied Sciences 3 (2): 363-371, 2010.

Al-Suhaibani, S.A., A.A. Bedri, A.S. Babeir and J. Kilgour (1994). Mobile instrumentation package for monitoring tractor performance. Agricultural Engineering Research Bulletin No. 40, King Saud University, Riyadh, pp: 26.

Altinisik, A., H. Terzioglu, H. Saglam and K. Altinisik (2012). Analysis of tractor traction force with artificial neural networks in cultivation. 16th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology” TMT 2012, Dubai, UAE, 10-12 September 2012:567-570.

Aluko, O.B. and D.A. Seig (2000). An experimental investigation of the characteristics of and conditions for brittle fracture in two-dimensional soil cutting. Soil & Tillage Research, 57:143-157.

Amara, M., I. Guedioura and M. A. Feddal (2013). Experimental model to estimate draft force for mouldboard ploughs; incorporating effects of plough geometric . Int. J. Adv. Agric. Res.,1:27-36.

Askari, M. and S. Khalifahamzehghasem (2013). Draft Force Inputs for Primary and Secondary Tillage Implements in a Clay Loam Soil. World Applied Sciences Journal 21 (12): 1789-1794.

Bentaher, H., E. Hamza, G. Kantchev, A. Maalej and W. Arnold (2008). Three-point hitch-mechanism instrumentation for tillage power optimization. Biosystems Engineering, 100 (1):24-30.

Brown, D. P. (2005). Artificial Intelligence for Creation of Rapid, Low-Cost Models and Simulations. Defense Acquisition University, Technology and Engineering. Department, Fort Belvoir,VA,22060 http://www.dtic.mil/dtic/tr/fulltext/u2/a484107.pdf.

Carman, K. and A. Taner (2012). PREDICTION OF TIRE TRACTIVE PERFORMANCE BY USING ARTIFICIAL NEURAL NETWORKS. Mathematical and Computational Applications, Vol. 17, No. 3, pp. 182-192.

Catalan, H., P. Linares and V. Mendez (2008). Tractor PT: A traction prediction software for agricultural tractors. computers and electronics in agriculture 6 : 289–295.

Choi, Y. S. ; L.Kyu Seung and W. P. Yeop (2000). Application of a neural network to dynamic draft model. Agricultural and Biosystems Engineering,1(2):67-72.

Dahab, M.H., O.E. Mohamed (2006). A computer model for selection of farm machinery. U. K. J. Agric. Sci. 14(2):167-181.

El Awady, M.N., A.G. El Kabany, M.H.A. Kabeel and A.M. Aboukarima (2004). Predicting unit draft of tillage implements using statistical models and neural networks. The 12^th Conf. of Misr Society Agric. Eng., 15-16 October, Alexandria Univ.: 139-249.

El Awady, M.N., A.G. El Kabany, M.H.A. Kabeel, and A.M. Aboukarima (2003). Relative importance of variables affecting chisel-plow performance using neural networks. The 11^th Conf. of Misr Society Agric. Eng., 15-16 October, Rice Mech. Centre, Meet El Deeba, Kafer El Sheikh Governorate: 395-407.

El Awady, M.N.; A.G. El-Kabany; M.H. Kabeel and A.M. Aboukarima (2002). Using neural networks and linear regression for predicting energy requirements in plowing. The 10th Conf. of Misr Soc. of Ag. Eng.: 115-126 (In Arabic).

El Khadrawy, A.A. (1990). Estimating of used energy in the production of some Egyptian crops. MSc. Theses, Agric. Mech. Dept., Fac. of Agric., Mansoura Univ.: 71-140.

Elashry, E. R., A.M. Bassuni, A. A. Sayed Ahmed and K.A.Ahmed (1994). An instrumentation system for measuring the tractor performance. Misr J. Agric.Eng.11 (4):968-987.

Elbanna, E.B. (1989). Prediction of specific moldboard plow draft. J. Agric. Sci. , Mansoura Univ., 15 (2):253-265.

Elbanna, E.B. (1992). Tillage tools draft: chisel and moldboard plows. Misr J. Ag. Eng., 9 (4): 491-510.

Fishman, G.S. (1973). Concepts and methods in discrete event digital simulation. John Wiley and Sons, New York, pp150.

Formato, A., S. Faugno and G. Paolillo (2005). Numerical simulation of soil-plow moldboard interaction. Biosystems Engineering, 92 (3):309–316.

Gee-Clough, D., M. McAllister, G. Pearson and D.W. Evernden (1978). The empirical prediction of tractor – implement field performance. J. of Terramechanics, 15(2):81-94.

Godwin, J. and M.J. O’Dogherty (2007). Integrated soil tillage force prediction models. J. of Terramechanics, 44 (1):3-14.

Godwin, R.J., M.J. O’Dogherty, C.Saunders and A.T. Balafoutis (2007). A force prediction model for moldboard plows incorporating the effects of soil characteristic properties, plow geometric factors and plowing speed. Biosystems Engineering, 97:117–129.

Graham, W. D., L. D. Gaultney and R. F. Cullum (1990). Tractor Instrumentation for Tillage Research . Applied Engineering in Agriculture. 6(1): 24-28.

Graves, A. R., T. Hess, R. B. Matthews, W. Stephens, and T. Middleton (2002). Crop simulation models as tools in computer laboratory and classroom-based education. J. Nat. Resour. Life Sci. Educ., 31:48-54.

Hassan I. M.,S. I. M. N. Gabir,M. A. A. Omer ,O. M. Abbas ( 2011). A program for predicting performance of agricultural machinery in Visual Basic. Research Journal of Agriculture and Biological Sciences, 7(1): 32-41.

Hassan, A.E. and A.S.Tohmaz (1995). Performance of skidder tires in swamps-comparison between statistical and neural network models. Transaction ASAE, 38 (5): 1545-1551.

Hesse, H. and Keuper, G. (2001). Mass flow-control on hydraulically driven disc spreaders. In: Duhovnik, et al., (2004). Analysis and design parameters for inclined rotors used for manure disposal and broadcast spreaders for solid manure. Transactions of the ASAE, 47(5): 1389 – 1404.

Huijsmans, J. F. M., J. G. L. Hendriks and G. D. Vermeulen (1998). Draft requirement of trailing-foot and shallow injection equipment for applying slurry to grassland. J. agric. Engng Res., 71:347-356.

Hwang HB (2001). Insights into neural network forecasting of time series corresponding to ARIMA (p,q) structures. Omeg,. 29: 273- 289.

In Kawuyo, U. A. (2011). Mathematical modelling of draft characteristics of selected animal-drawn implements on the upland soils of Samaru, Nigeria. PhD Thesis, Department of Agricultural Engineering, Faculty of engineering, Ahmadu Bello University, Zaria:194p.

KarimiInchebron, A., S. R. M. Seyedi and R. T. Koloor (2012). Investigating the effect of soil moisture content and depth on the draft, specific draft and drawbar power of a light tractor. International Research Journal of Applied and Basic Sciences, 3 (11): 2289-2293.

Kawuyo, U. A. (2011). Mathematical modelling of draft characteristics of selected animal-drawn implements on the upland soils of Samaru, Nigeria. PhD Thesis, Department of Agricultural Engineering, Faculty of engineering, Ahmadu Bello University, Zaria:194p.

Kheiralla, A.F., A. Yahya, M. Zohadie and W. Ishak (2004). Modelling of power and energy requirements for tillage implements operating in Serdang sandy clay loam, Malaysia. Soil & Tillage Research, 78:21–34.

Kushwaha, R.L. and Z. X. Zhang (1997). Artificial neural networks modeling of soil-tool interaction.ASAE Paper No. 97-3067, ASAE, St. Joseph, Michigan, USA: 1-11.

Lai, Q., H. Yu, H. Chen and Y. Sun (2010).Prediction of total power of agricultural machinery using artificial neural networks. Proceeding CCIE '10 Proceedings of the 2010 International Conference on Computing, Control and Industrial Engineering - Volume 02:394-396.

Lar, M.B., Z. K. Pour, and G. R. Bamimohammadi (2011). Field efficiency and its use for energy coefficient determination. J. of American Science, 7(8):599-603.

Lendaris, G. (2004). Supervised learning in ANN from introduction to artificial intelligence, New York, April 7.

Leonard, J., and M.A. Kramer (1990). Improvement of the back-propagation algorithm for training neural networks. Comp. Chem. Eng, 14, 337-341.

Lippmann, R.P. (1987). An introduction to computing with neural nets. IEEE Magazine, April:4-22.

Mahfoud, A. and G. Ilham (2013). A force prediction model for the plough introducing its geometrical characteristics and its comparison with Gorjachkin and Gee Clough models. Erudite Journal of Engineering sciences and Technology (EJEST), 1(1):1-7.

Manuwa, S.I. and M.O. Ogunlami (2010). Soil-tool interaction modeling of parameters of soil profile produced by tillage tools. J. Eng. and Applied Sci. , 5:91-95.

Medeiros, M.C. and C.E. Pedreira (2001). What are the effects of forecasting linear time series with neural networks. Eng. Intell. Syst., 4: 237- 424.

Moeenifar, A. , S.R M. Seyedi and D. Kalantari (2013b). Determination of traction force acting on a wide blade using dimensional analysis Method. Intl J Agri Crop Sci., 5 (13):1403-1409.

Moeenifar, A. M., D. Kalantari and S. R. M. Seyedi (2013a). Application of dimensional analysis in determination of traction force acting on a narrow blade. Intl J Agri Crop Sci., 5 (9):1034-1039.

Mohammadi A., R.Alimardani, A. Akbarnia and A.Akram (2012). Modeling of draft force variation in a winged share tillage tool using fuzzy table look-up scheme. December, 2012 Agric Eng Int: CIGR Journal, 14(4):262-268.

Mouazen, A.M and H. Ramon (2002). A numerical-statistical hybrid modeling scheme for evaluation of draft requirements of a subsoiler cutting a sandy loam soil, as affected by moisture content, bulk density and depth. Soil & Tillage Research, 63 (3-4): 155-165.

Muthuri, C.W. (2004). Models. The Green Book: A guide to effective graduate research in African agriculture, environment and rural development. Edited by Patel, B.K., K. Muir-Lerescher, R. Coe and S.D. Hainswort. African Crop Science Society, Kampala, Uganda.

Nadre, R.G. and R.K. Datta (1991). Investigation on forces in three-point linkage of tractor. Journal of Agricultural Engineering, India Society of Agricultural Engineering, Vol. XXVIII, Nos. 1 To 4:254-260.

Nkakini, S. O. and I.E Douglas (2012). Modeling tractive force requirements of wheel tractors for disc harrowing in loamy sand soil. International Journal of Engineering and Technology, 2 (10):1657-1665.

Olatunji, O.M. and R.M. Davies (2009). Effect of weight and draft on the performance of disk plow on sandy-loam soil. Research Journal of Applied Siences, Engineering and Technology, 1(1):22-26.

Oskoui, K. E. and B. D. Witney (1982). The determination of plow draft, part 1: prediction from soil and meteorological data with cone index as the soil strength parameter. J. of Terramechanics, 19: 97–106.

Oskoui, K. E., D. H. Rackham and B. D. Witney (1982). The determination of plow draft: part 2, the measurement and prediction of plow draft for two moldboard shapes in three soil series. J. of Terramechanics, 19: 153–154.

Powell, G. (2000). Selection and matching of tractors and implements. DPI's Agency for Food and Fibre Sciences. File No: FS0305. Date created: September 2000. Revised: N/A. http://www2.dpi.qld.gov.au/fieldcrops/3492.html.

Pranav, P. K., T. Patel, M. Rathore and A. J. Sonowal (2012). Computer Simulation for Haulage Performance of Power Tiller. International Journal of Computer Applications (0975 – 8887) Volume 58– No.5, November 2012:19-25.

Rahman, A., R.L. Kushawaha, S.R. Ashrafizadeh and S. Panigrahi (2011). Prediction of energy requirement of a tillage tool in a soil bin using artificial neural network. ASABE Paper No. 111112. American Society of Agricultural and Biological Engineers, St. Joseph, Michigan, USA.

Rashidi , M., H. F. Lehmali, M. S. Beni, M. Malekshahi and S. T.i Namin (2013). Prediction of disc harrow draft force based on soil moisture content, tillage depth and forward speed. Middle East Journal of Scientific Research, 15(2):260-265.

Rashidi, M., I. Najjarzadeh, B. Jaberinasab, S. M. Emadi and M. Fayyazi (2013). effect of soil moisture content, tillage depth and operation speed on draft force of moldboard plow. Middle-East Journal of Scientific Research, 16 (2): 245-249.

Roul, A.K., H. Raheman, M.S. Pansare and R. Machavaram (2009). Prediction of draft requirement of tillage implements in sandy clay loam soil using an artificial neural network. Biosystems Engineering, 104(4): 476-485.

Sahu, R. K. and H. Raheman (2006). An approach for draft prediction of combination tillage implements in sandy clay loam soil. Soil & Tillage Research 2006; 90(1):145-155.

Sahu, R.K. and H. Raheman (2006). Draft prediction of agricultural implements using reference tillage tools in sandy clay loam soil. Biosystems Engineering, 94 (2):275–284.

Saleh, B. and A. A. Aly (2013). Artificial neural network model for evaluation of the ploughing process performance. International Journal of Control, Automation and Systems, 2 (2):1-11.

Shinde, G. U. and S. R. Kajale (2011). Computer aided engineering analysis and design optimization of rotary tillage tool components. Int J Agric & Biol Eng, Vol. 4 No.3: 1-6.

Shinde, G. U.1, J.M.Potekar, R.V.Shinde and S.R.Kajale (2011). Design analysis of rotary tillage tool components by CAD-tool: rotavator. International Conference on Environmental and Agriculture Engineering IPCBEE vol.15 (2011), IACSIT Press, Singapore:1-6.

Shirgure P. S. and G. S. Rajput (2011). Evaporation modeling with neural networks–A Research review. International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC), 1(2):37-47.

Shrestha, D. S., G. Singh and G. Gebresenbet (2001). Optimizing design parameters of a moldboard plow.J. agric. Engng Res., 78 (4): 377-389.

Smith, L.A. (1993). Energy requirements for selected crop production implements. Soil and Tillage Research, 25:281-299.

Sogaard, H.T. and C.G. Sorensen (1996). A model for optimal selection of machinery sizes within the farm machinery system. Sixth International Conference on Computers in Agriculture, American Society of Agricultural Engineers, St. Joseph, Michigan, USA: 588-596.

Taghavifar, H. , A. Mardani and I. Elahi (2013b). Preliminary researches regarding the use of ANN to predict the wheel-soil interaction. Agronomical Research in Molodivia, Vol. XLVI , No. 1 (153):5-13.

Taghavifar, H. and A. Mardani (2013a). Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin. Neural Computing and Applications, DOI:10.1007/s00521-013-1360-8.

Taghavifar, H. and Mardani (2013b). A. Application of artificial neural networks for the prediction of traction performance parameters. Journal of the Saudi Society of Agricultural Sciences (2013), http://dx.doi.org/10.1016/j.jssas.2013.01.002.

Taghavifar, H., H. Kalbkhani, A. Mardani and H. K. Maslak (2013a)Artificial Neural Network estimation of wheel rolling resistance in clay loam soil. Applied Soft Computing, Volume 13, Issue 8, August 2013, Pages 3544–3551

Tohmaz, A.S. and A. E. Hassan (1995). Application of artificial neural networks to skidder traction performances. J. of Terramechanics, 32 (3): 105-114.

Vesta Services (2000). Vesta Services, Inc. Qnet2000 Shareware, Vesta Services, Inc., 1001 Green Bay Rd, STE 196, Winnetka, IL 60093.

Vishal, B., S. Surendra and P. K. Gupta (2013). Predicting tractor power requirements using decision support system – a tool for farm machinery management. Agricultural Engineering Today, 37(1): 7- 14.

Younis, S. M., E. R. Elashry, A. F. Bahnasy and I. M. Elsybaee (2010). Development a local system for measuring tractors performance. Misr J. Ag. Eng., 27(1): 34 – 53.

Yousif, L. A., M.H. Dahab and H. R. El Ramlawi (2011). Crop-machinery management system for field operations and farm machinery selection. Journal of Agricultural Biotechnology and Sustainable Development, 5(5):84-90.

Zhang GP (2001). An investigation of neural networks for linear time series forecasting. Comput. Operat. Res., 28: 1183-1202.

Zhang, D., Q. Jiang and X. Li (2005). Application of neural networks in financial data mining. International Journal of Computational Intelligence, 1:106-109.

Zhang, Z.X. and R. L. Kushawaha (1999). Application of neural networks to simulate soil-tool interaction and soil behavior. Canadian Agricultural Engineering, 41(2): 119-125.