with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Document Type : Research Article

Author

Department of Biosystem Mechanical Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran

Abstract

Introduction
Corn harvest losses are imposed by several factors, the most important of which is harvesting-time. Since the harvesting-time is coincident with the rainy season, it is necessary to appropriately estimate the corn harvest time to avoid harvesting losses and losing the next cultivation. Accordingly, in the current research, the effect of harvesting-time on corn losses during the month and the day has been into consideration. An expert fuzzy system was designed to predict the best harvest time as it operates based on the losses amounts which are measured in processing and collection units into the combine, and losses due to the humidity percentage.
Materials and Methods
In this paper, corn harvest losses in a John Deere Combine, Model 1165, was studied in a different climatic circumstance in Moghan region. Moreover, a split plot experiment in a completely randomized block design was conducted with three replications. The losses data were collected from the processing and collection units of the combine harvester on the November 5th, 8th and 11th, 2017, in three different daily times of 8-10, 11-13 and 14-16 with three replications. The Mamdani fuzzy inference system with singleton fuzzifire and center average defuzzifire‎ was used to develop a fuzzy expert system.  In the designed expert system, the losses percentage in the processing and collection units and ‎the humidity percentage were considered as system inputs and optimal harvesting time was used as the system output. "Low, Very low, high and very ‎high" and "‏Best, Suitable, Unfit, and Worst" were four groups of linguistic variables for input and output parameters, respectively. These variables follow the triangular and trapezoidal membership functions. The number of 64 fuzzy rules were considered and introduced into the fuzzy system by experts, experienced farmers, and combiners. Furthermore, the same field data (measured data) were applied to evaluate the designed system, so that the predicted value was accounted as the system output.
Results and Discussion
Analysis of variance showed that there was a significant difference between the harvesting dates at the 0.05 probability level and significant difference between the harvesting times of a day at the 0.01 probability level. It can be concluded that the harvest dates and harvest times of a day were very effective in the number of corn losses, but the interaction effects were not significant. The results appeared that the lowest losses were 10.05% on November 8th, 2017, at 14-16 p.m., and the highest losses were 12.88% on November 11th, 2017, at 8-10 a.m.‎ The amount of losses was increased due to the higher air humidity and lower temperature. In the fuzzy simulation model, the suitable harvesting-time can be predicted based on the losses quantities in the processing and collection units and the humidity percentage. The results showed that the predicted values for harvesting-times, by a designed fuzzy system, were completely matched with measured values in this study. The coefficient of determination (R2) was 0.980 between measured and predicted harvesting times. This coefficient demonstrated that the developed fuzzy logic system was suitable for prediction of harvesting time in the studied area.
Conclusion
The experimental observations in the field and data analysis showed that in the corn harvesting in the Moghan region, the humidity level, date, and harvesting-time were the most effective factors in the harvesting losses. In this paper, based on measured data from a small farm and implementation of the expert fuzzy system, the most suitable harvest date was set on November 8th  at 14-16 p.m, at 21-24°C and relative humidity of 44%-53% to have 10.5% losses which has been confirmed by the lowest losses observed in the corn plan (10%). Moreover, the high value of the determination coefficient demonstrates a high correlation between measured and predicted data.

Keywords

Open Access

©2020 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

1. Craessaerts, G., J. De Baerdemaeker, B. Missotten, and W. Saeys. 2009. Fuzzy control of the cleaning process on a combine harvester. Biosystems Engineering 106: 103-111.
2. FAO statistics. 2015. The Food and Agriculture Organization statistics: Corp, Maize, production quantity, Available at: http://www.fao.org/faostat/en/#data/ QC.
3. Humburg, D. 2016. Combine Adjustments to Reduce Harvest Losses. Chapter 37 in Clay, D.E., C.G. Carlson, S.A. Clay, and E. Byamukama eds. Best Management Practices. South Dakota State University.
4. DeToro, A., G. Gunnarsson, G. Lundin, and N. Jonsson. 2012. Cereal harvesting strategies and costs under variable weather conditions. Biosystem Engineering 3: 429-439.
5. Karatalopoulos, S. V. 2000. Understanding Neural Networks and Fuzzy Logic- Basic Concepts and Applications. Prentice Hall, New-Delhi, India.
6. Kordi, S. 2012. Effects of urea foliar application, harvesting time and seed drying methods on agronomical traits and seed quality of corn in Khorramabad conditions. Faculty of agriculture. Ilam University, Ilam, Iran. (In Farsi).
7. Laviolette, M. and J. W. Seaman. 1994. The efficiency of fuzzy representations of uncertainty. IEEE Trans. Fuzzy Systems 2: 4-15.
8. Mostofi Sarkari M. R. 2011. Investigation & determination of corn combine harvester losses to introduce an appropriate methods to reduce losses. Journal of Agricultural Machinery Engineering 1(1): 10-16. (In Farsi).
9. Mostofi Sarkari M. R., M. S Valiahdi, and I. Ranjbar. 2014. Field Evaluation of Cereal Combine Harvesters Processing Losses on JD-955 and JD-1165 Combines Equipped with Grain Loss Monitor. Journal of Agricultural Machinery 4 (2): 335-343. (In Farsi).
10. Nazmi, M.W., G. Chen, and D. Zare. 2010. The effect of different climatic conditions on wheat harvesting strategy and return. Biosystems Engineering 106: 493-502.
11. Omid, M., M. Lashgari, H. Mobli, R. Alimardani, S. Mohtasebi, and R. Hesamifard. 2010. Design of fuzzy logic control system incorporating human expert knowledge for combine harvester. Expert Systems with Applications 37: 7080-7085.
12. Paulsen, M. R., F. D. A. D. C. Pinto, D. G. De Sena, R. S. Zandonadi, S. Ruffato, A. G. Costa, and M. G. C. Danao. 2014. Measurement of combine losses for corn and soybeans in Brazil. Applied Engineering in Agriculture 30 (6): 841-855.
13. Price, T. 1997. Growing dryland maize, Agricultural Extention Office, Northern Territory, Australia.
14. Prochnow, A., H. Risius, T. Hoffmann, and F. M. Chmielewski. 2015. Does climate change affect period, available field time and required capacities for grain harvesting in Brandenburg, Germany? Agricultural and Forest Meteorology 203 (0): 43e53
15. Rahama A. M., M. E. Ali, and M. I. Dawel Beit. 1997. On-farm evaluation of combine harvester losses in the Gezira Scheme in Sudan. Agricultural Mechanization in Asia, Africa and Latin America 28 (2): 23-26.
16. Ross, J. T. 1995. Fuzzy Logic with engineering applications. New York: McGraw Hill Inc.
17. Shauck, T. C., and R. J. Smeda. 2011. Factors Influencing Corn Harvest Losses in Missouri. Crop Management. Online. 10.1094/CM-2011-0926-01-RS. Available at: www.plantmanagementnetwork.org/cm/
18. Shay C., L.V. Ellis, and W. Hires. 1999. Measuring and Reducing Corn Harvesting Losses, Department of Agricultural Engineering, University of Missouri-Columbia.
19. Spengler A., S. Memleben. 2003. Combine harvesting at large scale enterprises in Europe. Proceedings of the international conference on crop harvesting and processing. American Society of Agricultural and Biological Engineers (ASABE). Pub. # 701P1103e. Louisville, Kentucky, USA.
20. Suleiman, A. R. 2015. Current Maize Production, Post-harvest Losses and the Risk of Mycotoxins Contamination in Tanzania. An ASABE Meeting Presentation, 26e29, 2015. Paper No. 152189434. ABSAE, St. Joseph MO, USA.
21. Tolera, A., F. Sundstøl, and A. N. Said. 1998. The effect of stage of maturity on yield and quality of maize grain and stover. Animal Feed Science and Technology 75 (2): 157-168.
22. Wang, L. X. 1997. A Course in Fuzzy Systems and Control, Prentice-Hall.
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