| تعداد نشریات | 24 |
| تعداد شمارهها | 924 |
| تعداد مقالات | 8,295 |
| تعداد مشاهده مقاله | 16,557,510 |
| تعداد دریافت فایل اصل مقاله | 13,967,337 |
Machine learning approach for sugarcane yield prediction in southwest Iran: A radial basis function neural network | ||
| Iran Agricultural Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 اردیبهشت 1405 اصل مقاله (912.27 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2026.54923.1733 | ||
| نویسندگان | ||
| Alireza Ashtiani Araghi* 1؛ Abbas Rohani* 2؛ Sina Sharifi3 | ||
| 1Department of Agrotechnology, Aburaihan College of Agricultural Technology, University of Tehran, I. R. Iran | ||
| 2Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, I. R. Iran | ||
| 3Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, I. R. Iran | ||
| چکیده | ||
| Crop yield prediction in terms of fresh weight is one of the many applications of machine learning in agriculture. Accurate yield prediction models are crucial as they guide growers in making appropriate decisions on what and when to cultivate under varying circumstances influenced by climatic and crop growth parameters, and market conditions. Production of sugarcane involves activities that heavily rely on accurate and timely cropping and harvest forecasting. This study aims to introduce a machine learning model, that estimates sugarcane yield based on various agronomic and field data collected in southwest Iran. Radial basis function neural network (RBF-NN), a shallow feedforward neural network distinguished for its simple structure, universal approximation, and fast learning speed, was employed to develop a yield prediction model. By utilizing datasets containing nine types of input variables and determining optimal values for network hyperparameters, different algorithms were examined for network training. The RBF-NN trained by Levenberg–Marquardt algorithm, containing 75 neurons in the hidden layer, bandwidth value of 0.9, while using 80% of the total data for training, achieved the highest accuracy with an efficiency of 92%, and least amount of estimation error of 4.77%. Further analysis revealed that crop harvest schedule, electrical conductivity of the soil, and crop variety have the most significant impacts on the estimation accuracy of the model. In terms of performance, RBF-NN may not always demonstrate a significant advantage over similar machine learning models. Yet, faster learning and quick convergence are its remarkable points, particularly when dealing with large datasets. | ||
| کلیدواژهها | ||
| Artificial intelligence؛ Harvest forecasting؛ Prediction model؛ Precision agriculture؛ RBF-NN | ||
| مراجع | ||
|
Aggarwal, C. C. (2018). Neural networks and deep learning: A textbook (1st ed.). Springer.
Akkem, Y., Biswas, S. K., & Varanasi, A. (2023). Smart farming using artificial intelligence: A review. Engineering Applications of Artificial Intelligence, 120, 105899. https://doi.org/10.1016/j.engappai.2023.105899
Amarasingam, N., Salgadoe, A. S. A., Powell, K., Gonzalez, L. F., & Natarajan, S. (2022). A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops. Remote Sensing Applications: Society and Environment, 26, 100712. https://doi.org/10.1016/j.rsase.2022.100712
Bali, N., & Singla, A. (2022). Emerging trends in machine learning to predict crop yield and study its influential factors: A survey. Archives of Computational Methods in Engineering, 29, 95-112. https://doi.org/10.1007/s11831-021-09569-8
Belouz, K., Nourani, A., Zereg, S., & Bencheikh, A. (2022). Prediction of greenhouse tomato yield using artificial neural networks combined with sensitivity analysis. Scientia Horticulturae, 293, 110666. https://doi.org/10.1016/j.scienta.2021.110666
Bocca, F. F., & Rodrigues, L. H. A. (2016). The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Computers and Electronics in Agriculture, 128, 67-76. https://doi.org/10.1016/j.compag.2016.08.015
Bocca, F. F., Rodrigues, L. H. A., & Arraes, N. A. M. (2015). When do I want to know and why? Different demands on sugarcane yield predictions. Agricultural Systems, 135, 48-56. https://doi.org/10.1016/j.agsy.2014.11.008
Cardoso, L. A. S., Farias, P. R. S., & Soares, J. A. C. (2022). Use of unmanned aerial vehicle in sugarcane cultivation in brazil: A review. Sugar Tech, 24(6), 1636-1648. https://doi.org/10.1007/s12355-022-01149-9
Charoen-Ung, P., & Mittrapiyanuruk, P. (2018). Sugarcane yield grade prediction using random forest with forward feature selection and hyper-parameter tuning. In International Conference on Computing and Information Technology (pp. 33-42). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-93692-5_4
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61-69. https://doi.org/10.1016/j.compag.2018.05.012
de França e Silva, N. R., Chaves, M. E. D., Luciano, A. C. D. S., Sanches, I. D. A., de Almeida, C. M., & Adami, M. (2024). Sugarcane yield estimation using satellite remote sensing data in empirical or mechanistic modeling: A systematic review. Remote sensing, 16(5), 863. https://doi.org/10.3390/rs16050863
de Oliveira, M. P. G., Bocca, F. F., & Rodrigues, L. H. A. (2017). From spreadsheets to sugar content modeling: A data mining approach. Computers and Electronics in Agriculture, 132, 14-20. https://doi.org/10.1016/j.compag.2016.11.012
Everingham, Y., Inman-Bamber, N., Thorburn, P., & McNeill, T. (2007). A bayesian modelling approach for long lead sugarcane yield forecasts for the Australian sugar industry. Australian Journal of Agricultural Research, 58(2), 87-94. https://doi.org/10.1071/AR05443
Everingham, Y. L., Smyth, C. W., & Inman-Bamber, N. G. (2009). Ensemble data mining approaches to forecast regional sugarcane crop production. Agricultural and Forest Meteorology, 149(3-4), 689-696. https://doi.org/10.1016/j.agrformet.2008.10.018
Everingham, Y., Sexton, J., Skocaj, D., & Inman-Bamber, G. (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36(2), 27. https://doi.org/10.1007/s13593-016-0364-z
Elavarasan, D., Vincent, D. R., Sharma, V., Zomaya, A. Y., & Srinivasan, K. (2018). Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture, 155, 257-282. https://doi.org/10.1016/j.compag.2018.10.024
FAO. (2024). World Food and Agriculture. Statistical Yearbook 2024. Rome, Italy.
Faris, H., Aljarah, I., & Mirjalili, S. (2017). Evolving radial basis function networks using moth–flame optimizer. In Samui, P., Sekhar, S., and Balas, V.E. (Eds.), Handbook of neural computation (pp. 537-550). Academic Press. https://doi.org/10.1016/B978-0-12-811318-9.00028-4
Garg, B., Kirar, N., Menon, S., & Sah, T. (2016). A performance comparison of different back propagation neural networks methods for forecasting wheat production. CSI Transactions on ICT, 4(2), 305-311. https://doi.org/10.1007/s40012-016-0096-x
Ghaffarian, S., van der Voort, M., Valente, J., Tekinerdogan, B., & de Mey, Y. (2022). Machine learning-based farm risk management: A systematic mapping review. Computers and Electronics in Agriculture, 192, 106631. https://doi.org/10.1016/j.compag.2021.106631
Guo, S., Zhang, Z., Zhang, F., & Yang, X. (2023). Optimizing cultivars and agricultural management practices can enhance soybean yield in Northeast China. Science of the Total Environment, 857(2), 159456. https://doi.org/10.1016/j.scitotenv.2022.159456
Haghverdi, A., Washington-Allen, R. A., & Leib, B. G. (2018). Prediction of cotton lint yield from phenology of crop indices using artificial neural networks. Computers and Electronics in Agriculture, 152, 186-197. https://doi.org/10.1016/j.compag.2018.07.021
Hammer, R. G., Sentelhas, P. C., & Mariano, J. C. (2020). Sugarcane yield prediction through data mining and crop simulation models. Sugar Tech, 22(2), 216-225. https://doi.org/10.1007/s12355-019-00776-z
Hernández Hernández, G. C., Gómez Gómez, J., & Jiménez-Cabas, J. (2025). Predictive models based on artificial intelligence to estimate crop yield: A literature review. Agriculture, 15(23), 2438. https://doi.org/10.3390/agriculture15232438
Ifaei, P., Nazari-Heris, M., Tayerani Charmchi, A.S., Asadi, S. & Yoo, C. K. (2023). Sustainable energies and machine learning: An organized review of recent applications and challenges. Energy, 266, 126432. https://doi.org/10.1016/j.energy.2022.126432
Joshua, V., Priyadharson, S. M., & Kannadasan, R. (2021). Exploration of machine learning approaches for paddy yield prediction in eastern part of Tamilnadu. Agronomy, 11(10), 2068. https://doi.org/10.3390/agronomy11102068
Kasthuri, V., & Selvakumar, S. (2021). Forecasting foodgrains production using arima model and neural network. American Journal of Neural Networks and Applications, 7(2), 30-37. https://doi.org/10.11648/j.ajnna.20210702.12
Khalifani, S., Darvishzadeh, R., Azad, N., & Rahmani, R. S. (2022). Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models. Industrial Crops and Products, 189, 115762. https://doi.org/10.1016/j.indcrop.2022.115762
Kuan, Y. N., Goh, K. M., & Lim, L. L. (2025). Systematic review on machine learning and computer vision in precision agriculture: Applications, trends, and emerging techniques. Engineering Applications of Artificial Intelligence, 148, 110401. https://doi.org/10.1016/j.engappai.2025.110401
Kumar, S., Kumar, V., & Sharma, R. K. (2015). Sugarcane yield forecasting using artificial neural network models. International Journal of Artificial Intelligence and Applications, 6(5), 51-68. https://doi.org/10.5121/ijaia.2015.6504
Kung, S. Y. (2014). Kernel methods and machine learning (1st ed.). UK: Cambridge University Press.
Kunwer, R., Pasupuleti, S. R., Bhurat, S. S., Gugulothu, S. K., & Rathore, N. (2022). Blending of ethanol with gasoline and diesel fuel–A review. Materials Today: Proceedings, 69, 560-563. https://doi.org/10.1016/j.matpr.2022.09.319
Kutyauripo, I., Rushambwa, M., & Chiwazi, L. (2023). Artificial intelligence applications in the agrifood sectors. Journal of Agriculture and Food Research, 11, 100502. https://doi.org/10.1016/j.jafr.2023.100502
Medar, R. A., Rajpurohit, V. S., & Ambekar, A. M. (2019). Sugarcane crop yield forecasting model using supervised machine learning. International Journal of Intelligent Systems and Applications, 10(8), 11. https://doi.org/10.5815/ijisa.2019.08.02
Mokarram, M., & Bijanzadeh, E. (2016). Prediction of biological and grain yield of barley using multiple regression and artificial neural network models. Australian Journal of Crop Science, 10(6), 895-903. https://doi.org/10.21475/ajcs.2016.10.06.p7634
Obe, O., & Shangodoyin, D. (2010). Artificial neural network based model for forecasting sugar cane production. Journal of Computer Science, 6(4), 439.
Oikonomidis, A., Catal, C., & Kassahun, A. (2022). Hybrid deep learning-based models for crop yield prediction. Applied Artificial Intelligence, 36(1), 2031822. https://doi.org/10.1080/08839514.2022.2031823
Parsaeian, M., Rahimi, M., Rohani, A., & Lawson, S. S. (2022). Towards the modeling and prediction of the yield of oilseed crops: A multi-machine learning approach. Agriculture, 12(10), 1739. https://doi.org/10.3390/agriculture12101739
Patan, K. (2019). Robust and fault-Tolerant control: Neural-network-based solutions (1st ed.). Springer.
Rocha, H., & Dias, J. M. (2019). Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agroclimatic data. Computers and Electronics in Agriculture, 157, 427-435. https://doi.org/10.1016/j.compag.2019.01.018
Sahoo, M., Dey, S., Sahoo, S., Das, A., Ray, A., Nayak, S., & Subudhi, E. (2023). MLP (multi-layer perceptron) and RBF (radial basis function) neural network approach for estimating and optimizing 6-gingerol content in Zingiber officinale Rosc. in different agro-climatic conditions. Industrial Crops and Products, 198, 116658. https://doi.org/10.1016/j.indcrop.2023.116658
Saroj, R., Soumya, S. L., Singh, S., Sankar, S. M., Chaudhary, R., Yashpal, Saini, N., Vasudev, S., & Yadava, D. K. (2021). Unraveling the relationship between seed yield and yield-related traits in a diversity panel of Brassica juncea using multi-traits mixed model. Frontiers in Plant Science, 12, 651936. https://doi.org/10.3389/fpls.2021.651936
Satpathi, A., Chand, N., Setiya, P., Ranjan, R., Nain, A. S., Vishwakarma, D. K., Saleem, K., Obaidullah, A. J., Yadav, K. K., & Kisi, O. (2025). Evaluating statistical and machine learning techniques for sugarcane yield forecasting in the Tarai region of North India. Computers and Electronics in Agriculture, 229, 109667. https://doi.org/10.1016/j.compag.2024.109667
Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/10.1016/j.compag.2022.107119
Sharifi, S., Monjezi, N., & Hafezi, N. (2020). Performance of multilayer perceptron neural network models and radial-based functions in estimation of sugar-cane crop yield. Journal of Agricultural Science and Sustainable Production, 30(4), 213-228. (In Persian) https://doi.org10.22034/saps.2020.12313
Sharif Ahmadian, A. (2016). Numerical models for submerged breakwaters:Coastal hydrodynamics &morphodynamics. UK: Butterworth-Heinemann.
Sharkawy, A. N. (2020). Principle of neural network and its main types. Journal of Advances in Applied & Computational Mathematics, 7(1), 8-19. https://doi.org/10.15377/2409-5761.2020.07.2
Shawon, S. M., Ema, F. B., Mahi, A. K., Niha, F. L., & Zubair, H. T. (2025). Crop yield prediction using machine learning: An extensive and systematic literature review. Smart Agricultural Technology, 10, 100718. https://doi.org/10.1016/j.atech.2024.100718
Singh, P., & Kaur, A. (2022). A systematic review of artificial intelligence in agriculture. In Poonia, R.C., Singh, V. and Nayak, S.R. (Eds.). Deep learning for sustainable agriculture, (pp. 57-80). Academic press. https://doi.org/10.1016/B978-0-323-85214-2.00011-2
Som-Ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., & Immitzer, M. (2021). Remote sensing applications in sugarcane cultivation: A review. Remote Sensing, 13(20), 4040. https://doi.org/10.3390/rs13204040
Souza, J. B. C., de Almeida, S. L. H., de Oliveira, M. F., dos Santos Carreira, V., de Brito Filho, A. L., dos Santos, A. F., & da Silva, R. P. (2025). Generalization of peanut yield prediction models using artificial neural networks and vegetation indices. Smart Agricultural Technology, 11, 100873. https://doi.org/10.1016/j.atech.2025.100873
Sridhara, S., Soumya, B. R., & Kashyap, G. R. (2024). Multistage sugarcane yield prediction using machine learning algorithms. Journal of Agrometeorology, 26(1), 37-44. https://doi.org/10.54386/jam.v26i1.2411
Taherei Ghazvinei, P., Hassanpour Darvishi, H., Mosavi, A., Yusof, K. b. W., Alizamir, M., Shamshirband, S., & Chau, K.-w. (2018). Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network. Engineering Applications of Computational Fluid Mechanics, 12(1), 738-749. https://doi.org/10.1080/19942060.2018.1526119
Van Klompenburg, T., Kassahun, A., & Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 177, 105709. https://doi.org/10.1016/j.compag.2020.105709
Vinayaka & Prasad, P. R. C. (2024). AI-enhanced remote sensing applications in Indian sugarcane research: A comprehensive review. Sugar Tech, 26(3), 609-628. https://doi.org/10.1007/s12355-024-01409-w
Waqas, M., Naseem, A., Humphries, U. W., Hlaing, P. T., Dechpichai, P., & Wangwongchai, A. (2025). Applications of machine learning and deep learning in agriculture: A comprehensive review. Green Technologies and Sustainability, 3(3), 100199. https://doi.org/10.1016/j.grets.2025.100199
Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C., Zhu, M., & Wu, X. (2019). Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Ecological Indicators, 101, 943-953. https://doi.org/10.1016/j.ecolind.2019.01.059
Zaki Dizaji, H., Monjezi, N., & Sheikhdavoodi, J. (2018). Investigating effective factors on sugarcane production performance to increase the production of sugarcane using data mining. Iranian Journal of Biosystem Engineering, 49(3), 501-511. (In Persian) https://doi.org/10.22059/ijbse.2018.248601.665021
Zaki Dizaji, H., Shirini, K., Taheri Hajivand, A., & Monjezi, N. (2024). Modelling variables affecting the yield of sugarcane fields using deep recurrent neural network. Iranian Journal of Biosystem Engineering, 55(2), 93-108. (In Persian) https://doi.org/10.22059/ijbse.2025.378958.665557
Zhou, Y., Pan, M., Guan, W., Fu, C., & Su, T. (2023). Predicting sugarcane yield via the use of an improved least squares support vector machine and water cycle optimization model. Agriculture, 13(11), 2115. https://doi.org/10.3390/agriculture13112115 | ||
|
آمار تعداد مشاهده مقاله: 176 تعداد دریافت فایل اصل مقاله: 244 |
||