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Hybrid Classification Model with Tuned Weights for Crop Yield Prediction

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Abstract

One of the difficult problems in agriculture is predicting the crop production. At the international, regional, and crop level, it is crucial to make decisions on this. In Most of the cases, agricultural, land, climatic, atmospheric, and other characteristics are used to forecast crop production. ML is a crucial decision-support model for estimating agricultural yields, enabling choices about which crops to cultivate and what to do while they are in the growing season. Numerous ML and DL algorithms have been applied to support studies on agricultural yield prediction. In this paper, a new crop yield prediction model is proposed which includes preprocessing, feature extraction and yield prediction phase. In preprocessing, data cleaning will takes place. Higher order statistical feature, information gain and improved entropy based features are extracted in feature extraction phase. The prediction is done by the hybrid model that combines Bi-GRU model and Maxout classifiers. To enhance the performance of this hybrid classifier, a new Self Adaptive Archimedes Optimization Algorithm (SAAOA) is introduced for training the weight parameters optimally. Finally the overall performance is evaluated and the better result is determined.

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Data Availability

https://github.com/tryambakganguly/Yield-PredictionTemporal-Attention.

References

  1. Vani, P. S., & Rathi, S. (2021). Improved data clustering methods and integrated A-FP algorithm for crop yield prediction. Distributed and Parallel Databases. https://doi.org/10.1007/s10619-021-07350-1

    Article  Google Scholar 

  2. Elavarasan, D., & Vincent, P. M. D. R. (2021). A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters. Journal of Ambient Intelligence and Humanized Computing, 12, 10009–10022. https://doi.org/10.1007/s12652-020-02752-y

    Article  Google Scholar 

  3. Bi, L., & Hu, G. (2021). A genetic algorithm-assisted deep learning approach for crop yield prediction. Soft Computing, 25, 10617–10628. https://doi.org/10.1007/s00500-021-05995-9

    Article  Google Scholar 

  4. Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access, 9, 63406–63439. https://doi.org/10.1109/ACCESS.2021.3075159

    Article  Google Scholar 

  5. Prasad, N. R., Patel, N. R., & Danodia, A. (2021). Crop yield prediction in cotton for regional level using random forest approach. Spatial Information Research, 29, 195–206. https://doi.org/10.1007/s41324-020-00346-6

    Article  Google Scholar 

  6. Elavarasan, D., & Durai Raj Vincent, P. M. (2021). Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks. Neural Computing and Applications, 33, 13205–13224. https://doi.org/10.1007/s00521-021-05950-7

    Article  Google Scholar 

  7. Bal, S. K., Wakchaure, G. C., Potekar, S., Choudhury, B. U., Choudhary, R. L., & Sahoo, R. N. (2021). Spectral signature-based water stress characterization and prediction of wheat yield under varied irrigation and plant bio-regulator management practices. Journal of the Indian Society of Remote Sensing, 49, 1427–1438. https://doi.org/10.1007/s12524-021-01325-6

    Article  Google Scholar 

  8. Ali, A. M., Abouelghar, M., Belal, A. A., Saleh, N., Yones, M., Selim, A. I., Amin, M. E., Elwesemy, A., Kucher, D. E., Maginan, S., & Savin, I. (2022). Crop yield prediction using multi sensors remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 25(3), 711–716.

    Article  Google Scholar 

  9. Ziliani, M. G., Altaf, M. U., Aragon, B., Houborg, R., Franz, T. E., Lu, Y., Sheffield, J., Hoteit, I., & McCabe, M. F. (2022). Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model. Agricultural and forest meteorology, 313, 108736.

    Article  Google Scholar 

  10. Qiao, M., He, X., Cheng, X., Li, P., Luo, H., Zhang, L., & Tian, Z. (2021). Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks. International Journal of Applied Earth Observation and Geoinformation, 102, 102436.

    Article  Google Scholar 

  11. Måløy, H., Windju, S., Bergersen, S., Alsheikh, M., & Downing, K. L. (2021). Multimodal performers for genomic selection and crop yield prediction. Smart Agricultural Technology, 1, 100017.

    Article  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. Zhuo, W., Fang, S., Gao, X., Wang, L., Wu, D., Fu, S., Wu, Q., & Huang, J. (2022). Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 106, 102668.

    Article  Google Scholar 

  14. Elavarasan, D., & Vincent, P. M. D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE Access, 8, 86886–86901. https://doi.org/10.1109/ACCESS.2020.2992480

    Article  Google Scholar 

  15. Qiao, M., He, X., Cheng, X., Li, P., Luo, H., Tian, Z., & Guo, H. (2021). Exploiting hierarchical features for crop yield prediction based on 3-D convolutional neural networks and multikernel Gaussian process. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4476–4489. https://doi.org/10.1109/JSTARS.2021.3073149

    Article  Google Scholar 

  16. Luciani, R., Laneve, G., & JahJah, M. (2019). agricultural monitoring, an automatic procedure for crop mapping and yield estimation: The great rift valley of Kenya case. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2196–2208. https://doi.org/10.1109/JSTARS.2019.2921437

    Article  Google Scholar 

  17. Martínez-Ferrer, L., Piles, M., & Camps-Valls, G. (2021). Crop yield estimation and interpretability with Gaussian processes. IEEE Geoscience and Remote Sensing Letters, 18(12), 2043–2047. https://doi.org/10.1109/LGRS.2020.3016140

    Article  Google Scholar 

  18. Iniyan, S., & Jebakumar, R. (2021). Mutual information feature selection (MIFS) based crop yield prediction on corn and soybean crops using multilayer stacked ensemble regression (MSER). Wireless Personal Communications. https://doi.org/10.1007/s11277-021-08712-9

    Article  Google Scholar 

  19. Khosla, E., Dharavath, R., & Priya, R. (2020). Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environment, Development and Sustainability, 22, 5687–5708. https://doi.org/10.1007/s10668-019-00445-x

    Article  Google Scholar 

  20. Bhojani, S. H., & Bhatt, N. (2020). Wheat crop yield prediction using new activation functions in neural network. Neural Computing and Applications, 32, 13941–13951. https://doi.org/10.1007/s00521-020-04797-8

    Article  Google Scholar 

  21. Vignesh, K., Askarunisa, A., & Abirami, A. M. (2023). Optimized deep learning methods for crop yield prediction. Computer Systems Science and Engineering, 44(2), 1051–1067.

    Article  Google Scholar 

  22. Pham, H. T., Awange, J., & Kuhn, M. (2022). Evaluation of three feature dimension reduction techniques for machine learning-based crop yield prediction models. Sensors, 22(17), 6609.

    Article  Google Scholar 

  23. https://en.wikipedia.org/wiki/Higher- order_statistics#:~:text=In%20statistics%2C%20the%20term%20higher,first%2C%20and%20second%20powers).

  24. https://www.google.com/search?q=information+gain+formula&ei=5Ia5YpurE4iMseMPp6KB8A4&oq=information+gain&gs_lcp=Cgdnd3Mtd2l6EAEYADIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQRxCwAzIHCAAQsAMQQzIHCAAQsAMQQ0oECEEYAEoECEYYAFAAWABgzAtoAXABeACAAQCIAQCSAQCYAQDIAQrAAQE&sclient=gws-wiz

  25. Sun, Y., Zhang, J., Yu, Z., Zhang, Y., & Liu, Z. (2023). The bidirectional gated recurrent unit network based on the inception module (Inception-BiGRU) predicts the missing data by well logging data. ACS Omega, 8(30), 27710–27724.

    Article  Google Scholar 

  26. Kumar, A., & Sachdeva, N. (2021). A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media. World Wide Web. https://doi.org/10.1007/s11280-021-00920-4

    Article  Google Scholar 

  27. Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27, 1053–1073. https://doi.org/10.1007/s00521-015-1920-1

    Article  Google Scholar 

  28. Yilmaz, S., & Sen, S. (2020). Electric fish optimization: A new heuristic algorithm inspired by electrolocation. Neural Computing and Applications, 32(15), 11543–11578.

    Article  Google Scholar 

  29. James, J. Q., & Li, V. O. (2015). A social spider algorithm for global optimization. Applied Soft Computing, 30, 614–627.

    Article  Google Scholar 

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This research did not receive any specific funding.

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, 522237, India

    Venkata Rama Rao Kolipaka & Anupama Namburu

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  1. Venkata Rama Rao Kolipaka
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  2. Anupama Namburu
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Correspondence to Venkata Rama Rao Kolipaka.

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Kolipaka, V.R.R., Namburu, A. Hybrid Classification Model with Tuned Weights for Crop Yield Prediction. Wireless Pers Commun 133, 1325–1347 (2023). https://doi.org/10.1007/s11277-023-10781-x

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