Investigation, Optimization of Energy Consumption and Yield Modeling of Two Paddy Cultivars with Genetic-Artificial Bee Colony Algorithm

Articles in Press

Document Type : Research Article

Authors

• S. Sharifi ¹
• N. Hafezi ²
• M. H. Aghkhani ³

¹ MSc Graduated Student in Agricultural Mechanization, Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

² PhD Graduated Student in Agricultural Mechanization, Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

³ Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran

https://doi.org/10.22067/jam.2022.77064.1108

Abstract

Introduction
Efficient use of energy in paddy production can lower greenhouse gas emissions, safeguard agricultural ecosystems, and promote the growth of sustainable agriculture. Meanwhile, intelligent agriculture has come to the aid of farmers and policy-makers by harnessing cutting-edge technologies, which will lead to sustainable welfare and the comfort of human society in the present and the future. Therefore, this study aimed to analyze energy consumption and production, as well as model and optimize the yield of two paddy cultivars using Artificial Bee Colony (ABC) and Genetic Algorithms (GA).
Materials and Methods
Extensive research data was collected by thoroughly examining documentary and library resources, as well as conducting face-to-face questionnaires with 120 paddy farmers and farm owners in Rezvanshahr city, located in the province of Guilan, Iran, during the 2019-2020 production year. The farms consisted of 80 high-grading and 40 high-yielding paddies. The independent variables were machinery, diesel and gasoline fuels, electricity, seed, compost and straw, biocides, fertilizers, and labor. The dependent variable was paddy yield per hectare [of the farm area]. In the first step, energy consumption and production were calculated by multiplying the variables by their corresponding coefficients. In the second step, all the variables that maximize paddy yield were entered into MATLAB software. An artificial bee colony (ABC) algorithm with a novel and straightforward elitism structure was utilized to enhance the fitness function of the genetic algorithm (GA). The Sphere, Repmat, and Unifrnd functions were employed to determine the objective function, define the position of the bee colony, and quantify the position of the bee colony, respectively. In each generation, 900 new solutions were created, and the algorithm iterated 200 times. For the genetic algorithm, the population was defined as a double vector with a size of 100.
Results and Discussion
The findings revealed that the Hashemi (high-grading) paddy cultivar had an average energy consumption and production of 55.973 and 30.742 GJ·ha^-1, respectively. The Jamshidi (high-yielding) paddy cultivar had an average energy consumption of 54.796 GJ·ha^-1 and double the energy production of the Hashemi at 62.522 GJ·ha^-1. In both cultivars, agricultural machinery consumed the highest amount of energy, while straw consumed the lowest amount. The average energy consumption of tractors in the Hashemi and Jamshidi cultivars was 25.111 and 25.865 GJ·ha^-1, respectively, accounting for 44.862% and 47.202% of the total average consumed energy. This undoubtedly demonstrates the significant effect of this input and reflects the operators' skill and experiential knowledge. The evaluation indexes, including R², RMSE, MAPE, and EF, as well as statistical comparisons such as mean, STD, and distribution, consistently demonstrated that the ABC algorithm provided the essential conditions for the fitness function. The results of the bee-genetic algorithm optimization revealed that the majority of the consumed resources could be effectively managed on the farm to closely match optimal conditions. Through this optimization, energy consumption in the Hashemi and Jamshidi cultivars was reduced by 53.96% and 39.41%, respectively.
Conclusion
Given its impressive performance and potential for minimizing energy consumption, the ABC-GA algorithm offers an opportunity for policymakers in energy resource management and rice industry managers to develop innovative strategies for significantly reducing energy usage in rice production. This approach could lead to more sustainable and efficient practices in the agricultural sector.

Keywords

• Artificial Bee Colony Algorithm
• Genetic Algorithm
• Optimization
• Paddy

Main Subjects

• Modeling

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Anissa, R. D., Mahmudy, W., & Widodo, A. W. (2019). Optimisation of Rice Fertiliser Composition Using Genetic Algorithms. Knowledge Engineering and Data Science, 2(2), 72-81. https://doi.org/10.17977/um018v2i22019p72-81
2. Baradaran Motie, J., Aghkhani, M. H., Rohani, A., & Lakzian, A. (2019). Modeling the Effective Parameters on Accuracy of Soil Electrical Conductivity Measurement Systems Using RBF Neural Network. Journal of Agricultural Machinery, 9(1), 139-154. (in Persian with English abstract). https://doi.org/10.22067/jam.v1i1.10075
3. Baruah, K. K., Gogoi, B., & Gogoi, P. (2010). Plant Physiological and Soil Characteristics Associated with Methane and Nitrous Oxide Emission from Rice Paddy. Physiology and Molecular Biology of Plants International. A Journal of Functional Plant Biology, 16, 79-91. https://doi.org/10.1007/s12298-010-0010-1
4. Behrang, M. A., Assareh, E., Assari, M. R., & Ghanbarzadeh, A. (2011). Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 33(19), 1747-1759. https://doi.org/10.1080/15567036.2010.493920
5. Bolandnazar, E., Rohani, A., & Taki, M. (2019). Energy Consumption Forecasting in Agriculture by Artificial Intelligence and Mathematical Models. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(13), 1618-1632 https://doi.org/10.1080/15567036.2019.1604872
6. Camci, E., Kripalani, D. R., Ma, L., Kayacan, E., & Khanesar, M. A. (2018). An Aerial Robot for Rice Farm Quality Inspection with Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm. Swarm and Evolutionary Computation, 41, 1-8. https://doi.org/10.1016/j.swevo.2017.10.003
7. Chauhan, N. S., Mohapatra, P. K. J., & Pandey, K. P. (2006). Improving Energy Productivity in Paddy Production through Benchmarking: An Application of Data Envelopment Analysis. Energy Conversion and Management, 47, 1063-1085. https://doi.org/10.1016/j.enconman.2005.07.004
8. Chen, X., Mao, Y., Ma, X., & Qi, L. (2020). A Tactile Method for Rice Plant Recognition Based on Machine Learning. Sensors, 20)18), https://doi.org/10.3390/s20185135
9. Cochran, W. G. (1977). Sampling Techniques. John Wiley & Sons, New York.
10. Cubero, S., Aleixos, N., Albert, F., Torregrosa, A., Ortiz, C., García-Navarrete, O., & Blasco, J. (2014). Optimised Computer Vision System for Automatic Pre-grading of Citrus Fruit in the Field Using A Mobile Platform. Precision agriculture 15, 80-94. https://doi.org/10.1007/s11119-013-9324-7
11. Elsoragaby, S., Yahya, A., Mahadi, M. R., Nawi, N. M., Mairghany, M., Elhassan, S. M. M., & Kheiralla, A. F. (2020). Applying Multi-Objective Genetic Algorithm (MOGA) to Optimize the Energy Inputs and Greenhouse Gas Emissions (GHG) in Wetland Rice Production. Energy Reports, 6, 2988-2998. https://doi.org/10.1016/j.egyr.2020.10.010
12. Fallah-Shamsi, S. A., Pirdashti, H., Ebadi, A., Esfahani, M., & Raeini, M. (2017). Response of Root Characteristics and Dry Matter Partitioning in 40 Improved and Local rice (Oryza sativa ) Genotypes Under Deficit Irrigation. Journal of Crop Breeding, 9(22), 63-72. (In Persian). https://doi.org/10.29252/jcb.9.22.63
13. Geleta, D. K., & Manshahia, M. S. (2021). Artificial Bee Colony-Based Optimization of Hybrid Wind and Solar Renewable Energy System. Research Anthology on Clean Energy Management and Solutions. 819-842. https://doi.org/10.4018/978-1-5225-9420-8.CH017
14. Gogoi, N., Baruah, K. K., & Gupta, P. K. (2008). Selection of Rice Genotypes for Lower Methane Emission. Agronomy for Sustainable Development, 28, 181-186. https://doi.org/10.1051/agro:2008005
15. Gracia, C., Diezma-Iglesias, B., Barreiro, P., Gardini, S. T., & Aditya, S. K. (2013). A Hybrid Genetic Algorithm for Route Optimization in the Bale Collecting Problem. Spanish Journal of Agricultural Research, 11, 603-614. https://doi.org/10.5424/sjar/2013113-3635
16. Gummert, M., Cabardo, C., Quilloy, R., Aung, Y. L., Thant, A. M., Kyaw, M. A., Labios, R., Htwe, N. M., & Singleton, G. R. (2020). Assessment of Post-Harvest Losses and Carbon Footprint in Intensive Lowland Rice Production in Myanmar. Scientific Reports, 10(1), 1-13. https://doi.org/10.1038/s41598-020-76639-5
17. Hosseinzadeh-Bandbafha, H., Safarzadeh, D., Ahmadi, E., & Nabavi-Pelesaraei, A. (2018). Optimization of Energy Consumption of Dairy Farms Using Data Envelopment Analysis - A case study: Qazvin city of Iran. Journal of the Saudi Society of Agricultural Sciences, 17, 217-228. https://doi.org/10.1016/j.jssas.2016.04.006
18. Imani, A., Hosseinpour, S., Keyhani, A., & Azimzadeh, M. (2020). Modeling and Optimization of Oligonucleotide-Based Nanobiosensor Using Artificial Neural Network and Genetic Algorithm Based Procedure. Iranian Journal of Biosystems Engineering, 51(1), 171-181. (In Persian with English abstract). https://doi.org/10.22059/ijbse.2019.290631.665231
19. Jiang, Y., Jan van Groenigen, K., Huang S., Bruce A. H., van Kessel C. H., Shuijin, Zhang, J., Wu, L., Yan, X., Wang, L., Chen, J., Hang, X., Zhang, Y., Horwath, W. R., Ye, R., Linquist, B. A., Song, Z., Zheng, C., Deng, A., & Zhang, W. (2017). Higher Yields and Lower Methane Emissions with New Rice Cultivars. Global Change Biology, 23(11), 4728-4738. https://doi.org/10.1111/gcb.13737
20. Karaboga, D., & Akay, (2009). A Comparative Study of Artificial Bee Colony Algorithm. Applied Mathematics and Computation, 214, 108-132. https://doi.org/10.1016/j.amc.2009.03.090
21. Kazemi, H., Kamkar, B., Lakzaei, S., Badsar, M., & Shahbyki, M. (2015). Energy Flow Analysis for Rice Production in Different Geographical Regions of Iran. Energy, 84, 390-396. https://doi.org/10.1016/j.energy.2015.03.005
22. Kitani, O., Jungbluth, T., Peart, R. M., & Ramdani, A. (1999). CIGR handbook of agricultural engineering. Energy and biomass engineering, 5, 330.
23. Le Dinh, L., Vo Ngoc, D., & Vasant, P. (2013). Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem. The Scientific World Journal. https://doi.org/10.1155/2013/159040
24. Loganthurai, P., Rajasekaran, V., & Gnanambal, K. (2016). Evolutionary Algorithm Based Optimum Scheduling of Processing Units in Rice Industry to Reduce Peak Demand. Energy, 107, 419-430. https://doi.org/10.1016/j.energy.2016.04.027
25. Mousavi-Avval, S. H., Rafiee, S., & Mohammadi, A. (2011). Optimization of Energy Consumption and Input Costs for Apple Production in Iran Using Data Envelopment Analysis. Energy, 36(2), 909-916. https://doi.org/10.1016/j.energy.2010.12.020
26. Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S., & Taromi, K. (2014). Applying Data Envelopment Analysis Approach to Improve Energy Efficiency and Reduce Greenhouse Gas Emission of Rice Production. Engineering in Agriculture, Environment and Food, 7(4), 155-162. https://doi.org/10.1016/j.energy.2010.12.020
27. Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., & Chau, K. W. (2017). Energy Consumption Enhancement and Environmental Life Cycle Assessment in Paddy Production Using Optimization Techniques. Journal of Cleaner Production, 16(2), 586-571. https://doi.org/10.1016/j.jclepro.2017.06.071
28. Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., & Chau, K. W. (2019). Assessment of Optimized Pattern in Milling Factories of Rice Production Based on Energy, Environmental and Economic Objectives. Energy, 169, 1273-1259. https://doi.org/10.1016/j.energy.2018.12.106
29. Nassiri, S. M., & Singh, S. (2009). Study on Energy Use Efficiency for Paddy Crop Using Data Envelopment Analysis (DEA) Technique. Applied Energy, 86(7), 1320-1325. https://doi.org/10.1016/j.apenergy.2008.10.007
30. Ozkan, B., Akcaoz, H., & Fert, C. (2004). Energy input–output analysis in Turkish agriculture. Renewable energy, 29(1), 39-51. https://doi.org/10.1016/S0960-1481(03)00135-6
31. Pishgar-Komleh, S. H., Sefeedpari, P., & Rafiee, S. (2011). Energy and Economic Analysis of Rice Production under Different Farm Levels in Guilan Province of Iran. Energy, 36(10), 5824-5831. https://doi.org/10.1016/j.energy.2011.08.044
32. Pour-Bagher, R., Rohani, A., Rahmati, M. H., & Abbaspour-Fard, M. H. (2018). Modeling and Optimization of Drying Process of Paddy in Infrared and Warm Air Fluidized Bed Dryer. Agricultural Engineering International: CIGR Journal, 20(3), 162-171.
33. Riahi, V., & Javan, F. (2016). The Role of Climate Comfort in the Diversification of Livelihood in Rural Areas. Geography, Civil Engineering and Urban Management Studies, 2(4), 35-47. (in Persian with English Abstract).
34. Rohani, A., Abbaspour-Fard, M. H., & Abdolahpour, S. (2011). Prediction of Tractor Repair and Maintenance Costs Using Artificial Neural Network. Expert Systems with Applications, 38, 8999-9007. https://doi.org/10.1016/j.eswa.2011.01.118
35. Salehi, M., Ebrahimi, R., Maleki, A., & Mobtaker, H. G. (2014). An assessment of energy modeling and input costs for greenhouse button mushroom production in Iran. Journal of Cleaner Production, 64, 377-383. https://doi.org/10.1016/j.jclepro.2013.09.005
36. Sarai, H. (1993). An Introduction to Research Sampling. The Organization for Researching and Composing University Textbooks in the Humanities (SAMT), Tehran.
37. Shabri, A., Samsudin, R., & Hezzam, E. A. (2021). Short-term co₂ emissions forecasting using multi-variable grey model and artificial bee colony (abc) algorithm approach. PP 856-598 in F. Saeed F. Mohammed A. Al-Nahari eds. Innovative Systems for Intelligent Health Informatics. IRICT 2021. Lecture Notes on Data Engineering and Communications Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_54
38. Sharifi, S., Aghkhani, M. H., & Rohani, A. (2021). Investigation of Brown Rice Losses in the Drying Process. Journal of Agricultural Machinery. 14(2) (In Persian with English Abstract). https://doi.org/10.22067/jam.2021.69208.1026
39. Sharma, D. K., & Jana, R. K. (2009). Fuzzy Goal Programming Based Genetic Algorithm Approach to Nutrient Management for Rice Crop Planning. International Journal of Production Economics, 1(1), 12. https://doi.org/10.1016/j.ijpe.2009.05.009
40. Shukla, A. K. (2020). Multi-Population Adaptive Genetic Algorithm for Selection of Microarray Biomarkers. Neural Computing and Applications, 32(15), 11897-11918. https://doi.org/10.1007/s00521-019-04671-2
41. Taheri-Rad, A., Khojastehpour, M., Rohani, A., & Khoramdel, S. (2017). Assessing the Energy Consumption Efficiency of Different Long Grain Rice Varieties in Golestan Province. Cereal Research, 7(1), 51-66. (in Persian with English abstract). https://doi.org/10.22124/c.2017.2428
42. Taki, M., & Farhadi, R. (2021). Application of Artificial Neural Network Models (MLP and RBF) and Support Vector Machine (SVM) to Estimate the Shadow in Flat-Plate Solar Collectors in Iran. Iranian Journal of Biosystems Engineering, 52(2), 197-209. (in Persian with English abstract). https://doi.org/10.22059/ijbse.2021.310127.665339
43. Van-Hung, N., Sander, B. O., Quilty, J., Balingbing, C., Castalone, A. G., Romasanta, R., Alberto, M. C. R., Sandro, J. M., Jamieson, C., & Gummert, M. (2019). An Assessment of Irrigated Rice Production Energy Efficiency and Environmental Footprint with In-field and Off-field Rice Straw Management Practices. Scientific Reports, 9(1), 1-12. https://doi.org/10.1038/s41598-019-53072-x
44. Wan, S., Chang, S. H., Peng, C. T., & Chen, Y. K. (2017). A Novel Study of Artificial Bee Colony With Clustering Technique on Paddy Rice Image Classification. Arabian Journal of Geosciences, 10(9), 215. https://doi.org/10.1007/s12517-017-2992-2
45. Wang, Y., Wu, P., Mei, F., Ling, Y., Qiao, Y., Liu, C., Leghari, S. H., Guan, X., & Wang, T. (2021). Does Continuous Straw Returning Keep China Farmland Soil Organic Carbon Continued Increase? A Meta-analysis. Journal of Environmental Management, 288, https://doi.org/10.1016/j.jenvman.2021.112391
46. Yang, X. S. (2013). Optimization and metaheuristic algorithms in engineering. PP 1-23 in A. H. Gandomi S. Talatahari and A. H. Alavi eds. Metaheursitics in Water, Geotechnical and Transport Engineering. Elsevier. https://doi.org/10.1016/B978-0-12-398296-4.00001-5
47. Yarpiz. (2021). Bees Algorithm (BeA) in MATLAB: MATLAB Central File Exchange.
48. Yodkhum, S., Sampattagul, S., & Gheewala, S. H. (2018). Energy and Environmental Impact Analysis of Rice Cultivation and Straw Management in Northern Thailand. Environmental Science and Pollution Research, 25(18), 17654-17664. https://doi.org/10.1007/s11356-018-1961-y
49. Zeng, S., Wu, T., Hu, L., Tang, L., & Liu, S. (2019). Crop Row Detection by ABC Clustering Algorithm in Paddy. International Agricultural Engineering Journal, 28(1), 304-309.