Abstract
Precision agriculture can be cost effective for date palm groves because the tree positions are known and fixed, the groves are mostly structured and many of agricultural operations are applied manually. Therefore, the new technology tools of precision agriculture are not essential. This study was done to improve date palm yield using maps of the variation in tree properties. Data on five Mozafati tree properties, such as sex, age, yield, visual appearance and fruit length were measured and recorded for each tree in five groves near the city of Bam in Iran. Tree positions were defined and the above properties were mapped. It was difficult to judge patterns in the variation because of tree to tree variability. Therefore, the Mamdani fuzzy inference system (MFIS) was used to classify the productive trees based on yield, fruit length and visual appearance, and to produce a tree total quality map (TTQM) for each grove. Based on the TTQM the trees were graded as excellent, good, medium, poor and very poor. The trees were also graded by the date growers who were experts. The evaluation for all groves by MFIS showed 87% general agreement with the results from the human expert indicating that it is a feasible method for classifying date palms based on quality. The results indicted that there are young and old trees in these groves that need different treatments, such as the amount of fertilizer required. Some date growers have low incomes, therefore, the overall TTQM is suitable for them because they can identify which trees have the greatest needs and apply limited resources to only those. This case study illustrates the use of computer and mathematical software to enhance date palm production and reduce the costs.
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References
Abraham, A. (2005). Rule-based expert systems. In P. H. Sydenham & R. Thorn (Eds.), Handbook of measuring system design (pp. 909–919). Stillwater, OK, USA: Oklahoma State University.
Al-Suhaibani, S. A., Babier, A. S., Kilgour, J., & Blackmore, B. S. (1993). Field tests of the KSU date palm machine. Journal of Agricultural Engineering Research, 51, 179–190.
Ambuel, J. R., Colvin, T. S., & Karlen, D. L. (1994). A fuzzy logic production simulator for prescription farming. Transactions of the ASAE, 37, 1999–2009.
Anon. (2003). Iranian management and planning organization. Economical, social and cultural report of Kerman province. Kerman, Iran. http://www.mporg.ir/english/index.htm. Accessed 6 Aug 2008.
Anon. (2006). Iranian ministry of agricultural statistics. http://www.agri-jahad.ir.
Blackmore, B. S. (2005). Developing the principles of precision farming. The center for precision farming. The royal veterinary and agricultural university, Denmark. http://www.cpf.kvl.dk.
Center, B., & Verma, B. (1998). Fuzzy logic for biological and agricultural systems. Artificial Intelligence Review, 12, 213–225.
Emmott, A., Hall, J., & Mathews, R. (1997). The potential of precision farming applied to plantation agriculture. In J. V. Stafford (Ed.), Precision agriculture ‘97 (pp. 289–296). Oxford: BIOS Scientific Publishers.
Herrera, F., & Lozano, M. (2003). Fuzzy adaptive genetic algorithm: Design, taxonomy, and future directions. Soft Computing, 7, 545–562.
Heske, T., & Heske, J. N. (1996). Fuzzy logic for real world design. San Diego, CA: Anna Books.
Jang, J., & Sun, C. (1995). Neuro-Fuzzy modeling and control. Proceedings of the Institute of Electrical and Electronics Engineers, 83, 378–406.
Jang, J. S. R., Sun, C., & Mizutani, E. (1997). Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice-Hall International, Inc.
Kargaran, S. (2000). Introduction of precision farming for date palm orchard. Unpublished B.Sc. Thesis, Shahid Bahonar University of Kerman, Kerman, Iran.
Mamdani, E., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. In International Journal of Man-Machine Studies, 7, 1–13.
MathWorks. (2004). Fuzzy logic toolbox user’s guide, for the use of Matlab. The Math Works Inc. http://www.mathworks.com/.
Mazloumzadeh, M., & Shamsi, M. (2007). Evaluation of alternative date harvesting methods in Iran. Acta Horticulturae, (International Society of Horticultural Sciences), 736, 463–469.
Mazloumzadeh, S. M., Shamsi, M., & Nezamabadi-pour, H. (2008). Evaluation of general-purpose lifters for the date harvest industry based on a fuzzy inference system. Computers and Electronics in Agriculture, 60, 60–66. doi:10.1016/j.compag.2007.06.005.
Mehrabi, H. (2004). Optimization of economical resource in agricultural sector at Kerman Province. Kerman, Iran: Ministry of Management and planning, project report.
Nauck, D., & Kruse, R. (1995). NEFCLASS—A Neuro-Fuzzy approach for the classification of data. In K. M. George., J. H. Carroll., E. d. Deaton., D. Oppenheim., & J. Hightower (Eds.), Proceedings of the 1995 ACM symposium on applied computing (pp. 461–465). New York: ACM Press.
Roychowdhury, S., & Pedrycz, W. (2001). A survey of defuzzification strategies. International Journal of Intelligent Systems, 16, 679–695.
Shamsi, M. (1998). Design and development of a date harvesting machine. Unpublished Ph.D. Thesis, Silsoe College, Cranfield University, UK.
Shanon, K., Brumett, J., Ellis, C., & Hoette, G. (2001). Can a $300 GPS receiver be used for yield mapping? Columbia: Missouri Precision Agriculture Center (MPAC). University of Missouri.
Simonton, W. (1993). Bayesian and fuzzy logic classification for plant structure analysis. Applied Engineering in Agriculture, 12, 89–97.
Sørensen, C. G., Fountas, S., Blackmore, S., & Pedersen, H. H. (2002). Information sources and decision making on precision farming. In P. C. Robert (Ed.), Proceedings of the 6th international conference on precision agriculture and other precision resources management (pp. 1683–1695). Minneapolis, MN, USA: American Society of Agronomy.
Surfer v8. (2002). User’s guide. Colorado: Golden Software, Inc.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control. Institute of Electrical and Electronics Engineers Transactions on Systems, Man and Cybernetics, 15, 116–132.
Timmermann, T., Gehards, R., & Kuhbauch, W. (2003). The economic impact of site-specific weed control. Precision Agriculture, 4, 249–260.
Verma, B. (1995). Application of fuzzy logic in post harvest quality decisions. In B. Hashem (Ed.), Proceedings of the national seminar on post harvest technology of fruits (pp. 207–220). Bangalore, India: University of Agricultural Sciences.
Yang, C. C., Prasher, S. O., Landry, J. A., Perret, J., & Ramaswamy, H. S. (2005). Recognition of weeds with image processing and their use with fuzzy logic for precision farming. Canadian Agricultural Engineering, 42, 195–200.
Yoshinari, Y., Pedrycz, W., & Hirota, K. (1996). Construction of fuzzy models through clustering techniques. Fuzzy Sets and Systems, 78, 1–4.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Zaid, A. (2002). Date palm cultivation. Rome: FAO Publication, No. 156.
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This research is supported in part by the Fuzzy Systems and Applications Center of Excellence, Shahid Bahonar University of Kerman, Kerman, Iran.
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Mazloumzadeh, S.M., Shamsi, M. & Nezamabadi-pour, H. Fuzzy logic to classify date palm trees based on some physical properties related to precision agriculture. Precision Agric 11, 258–273 (2010). https://doi.org/10.1007/s11119-009-9132-2
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DOI: https://doi.org/10.1007/s11119-009-9132-2