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Crop Yield Prediction using Machine Learning: A Review of Recent Approaches

by Pankaj, P.K. Bharti, Brajesh Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 24
Year of Publication: 2023
Authors: Pankaj, P.K. Bharti, Brajesh Kumar
10.5120/ijca2023922994

Pankaj, P.K. Bharti, Brajesh Kumar . Crop Yield Prediction using Machine Learning: A Review of Recent Approaches. International Journal of Computer Applications. 185, 24 ( Jul 2023), 27-32. DOI=10.5120/ijca2023922994

@article{ 10.5120/ijca2023922994,
author = { Pankaj, P.K. Bharti, Brajesh Kumar },
title = { Crop Yield Prediction using Machine Learning: A Review of Recent Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 24 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number24/32841-2023922994/ },
doi = { 10.5120/ijca2023922994 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:58.426614+05:30
%A Pankaj
%A P.K. Bharti
%A Brajesh Kumar
%T Crop Yield Prediction using Machine Learning: A Review of Recent Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 24
%P 27-32
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Machine learning is an important tool for the prediction of crop yield. The prediction of yield can help the farmers as well as the policymakers to take timely decisions. With the advanced information on estimated yield, the farmers can make decisions on what to grow to meet the requirement of a growing population. Machine learning techniques can make better yield predictions based on the patterns and correlation information in images or data. There are several machine learning algorithms tested for crop yield prediction. In this work, the recent research works are analyzed in terms of algorithms and the type of information used in prediction studies. It is observed that deep learning techniques have achieved remarkable success in recent times. Most of such methods are based on images of different types such as color, multispectral, and hyperspectral images. This work presents a brief review of the machine learning techniques used for crop yield prediction. The major characteristics and challenges of the methods are discussed and research gaps are identified.

References
  1. Mengjia Qiao; Xiaohui He; Xijie Cheng; Panle Li; Haotian Luo; Zhihui Tian; Hengliang Guo, "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, vol. 14, pp. 4476-4489, 2021.
  2. R. Luciani, G. Laneve and M. JahJah, "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, vol. 12, no. 7, pp. 2196-2208, July 2019.
  3. Y. Ma and Z. Zhang, "A Bayesian Domain Adversarial Neural Network for Corn Yield Prediction," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022.
  4. S. P. Raja, B. Sawicka, Z. Stamenkovic and G. Mariammal, "Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers," IEEE Access, vol. 10, pp. 23625-23641, 2022.
  5. L. Martínez-Ferrer, M. Piles and G. Camps-Valls, "Crop Yield Estimation and Interpretability With Gaussian Processes," IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 12, pp. 2043-2047, Dec. 2021.
  6. D. Elavarasan and P. M. D. Vincent, "Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications," IEEE Access, vol. 8, pp. 86886-86901, 2020.
  7. H. Aghighi, M. Azadbakht, D. Ashourloo, H. S. Shahrabi and S. Radiom, "Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 12, pp. 4563-4577, Dec. 2018.
  8. Ömer Vanli, Ishfaq Ahmad and Burak Berk Ustundag, "Area Estimation and Yield Forecasting of Wheat in Southeastern Turkey Using a Machine Learning Approach," Journal of the Indian Society of Remote Sensing vol. 48, pp. 1757–1766, 2020.
  9. H. R. Seireg, Y. M. K. Omar, F. E. A. El-Samie, A. S. El-Fishawy and A. Elmahalawy, "Ensemble Machine Learning Techniques Using Computer Simulation Data for Wild Blueberry Yield Prediction," IEEE Access, vol. 10, pp. 64671-64687, 2022.
  10. Janmejay Pant, R.P.Pant, ManojKumar Singh, Devesh Pratap Singh and Himanshu Pant,"Analysis of agricultural crop yield prediction using statistical techniques of machine learning, "Materials Today: Proceedings,Volume 46, no 20, pp. 10922-10926, 2021.
  11. Mukesh Singh Boori, KomalChoudhary, Rustam Paringer and Alexander Kupriyanov,"Machine learning for yield prediction in Fergana valley, Central Asia," Journal of the Saudi Society of Agricultural Sciences, 2 August 2022.
  12. Nguyen-Thanh Son, Chi-Farn Chen, Youg-Sin Cheng, Piero Toscano, Cheng-Ru Chen, Shu-Ling Chen, Kuo-Hsin Tseng, Chien-Hui Syu, Horng-Yuh Guo and Yi-TingZhang, "Field-scale rice yield prediction from Sentinel-2 monthly image composites using machine learning algorithms," Ecological Informatics, Vol. 69, pp. 101618, July 2022.
  13. Minghan Cheng, Josep Penuelas, Matthew F McCabe, Clement Atzberger, Xiyun Jiao, Wenbin Wu and Xiuliang Jin, "Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China," Agricultural and Forest Meteorology, Vol. 323, pp. 109057, 15 August 2022.
  14. Moiz Uddin Ahmed and Iqbal Hussain," Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan," Telecommunications Policy, Vol. 46, no. 6, pp. 102370, July 2022.
  15. Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant and J.Senthilnath, "Integrated phenology and climate in rice yields prediction using machine learning methods," Ecological Indicators, Vol. 120, pp. 106935, January 2021.
  16. Sara Tokhi Arab, Ryozo Noguchi, Shusuke Matsushita and Tofael Ahamed, "Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach," Remote Sensing Applications: Society and Environment, Vol. 22, pp. 100485, April 2021.
  17. Juan Cao, Zhao Zhang, Fulu Tao, Liangliang Zhang, Yuchuan Luo, Jing Zhang, Jichong Han and Jun Xie, "Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches, "Agricultural and Forest Meteorology, Vol. 297, pp. 108275, 15 February 2021.
  18. Mohsen Shahhosseini,Guiping Hu,Isaiah Huber and Sotirios Archontoulis, " Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt, Scientific Reports, vol. 11, no. 1606, 2021.
  19. Sushila Shidnal, Mrityunjaya V. Latte and Ayush Kapoor, Crop yield prediction: two-tiered machine learning model approach, International Journal of Information Technology, vol. 13, pp. 1983–1991, 2021.
  20. Patrick Filippi, Edward J. Jones, Niranjan S. Wimalathunge, Pallegedara D. S. N. Somarathna, Liana E. Pozza, Sabastine U. Ugbaje, Thomas G. Jephcott, Stacey E. Paterson, Brett M. Whelan and Thomas F. A. Bishop, "An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning," Precision Agriculture, vol. 20, pp. 1015–1029, 2019.
  21. Dhivya Elavarasan and P. M. Durai Raj Vincent, "A reinforced random forest model for enhanced crop yield prediction by integrating agrarian parameters," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 10009–10022, 2021.
  22. Dhivya Elavarasan and P. M. Durai Raj Vincent, "Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks," Neural Computing and Applications, vol. 33, pp. 13205–13224, 2021.
  23. P. Murali, R. Revathy, S. Balamurali and A. S. Tayade, "Integration of RNN with GARCH refined by whale optimization algorithm for yield forecasting: a hybrid machine learning approach," Journal of Ambient Intelligence and Humanized Computing, 2020.
  24. Raí A.Schwalbert, Telmo Amado, Geomar Corassa, Luan Pierre Pott, P.V.Vara Prasadb, Ignacio A.Ciampitti, "Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil," Agricultural and Forest Meteorology, vol. 284, pp. 107886, 15 April 2020.
  25. Foyez Ahmed Prodhan, Jiahua Zhang, Til Prasad Pangali Sharma, Lkhagvadorj Nanzad, Da Zhang, Ayalkibet M.Seka, Naveed Ahmed, Shaikh Shamim Hasan, Muhammad Ziaul Hoque and Hasiba Pervin Mohana," Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach," Science of The Total Environment, vol. 807, no. 3, pp. 151029, 10 February 2022.
  26. Khaki S, Wang L and Archontoulis SV (2020) A CNN-RNN Framework for Crop Yield Prediction. Frontiers in Plant Science, vol. 10, pp. 1750, 2020.
  27. Muruganantham, Priyanga, Santoso Wibowo, Srimannarayana Grandhi, Nahidul Hoque Samrat, and Nahina Islam, "A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing" Remote Sensing, vol. 14, no. 9, pp. 1990, 2022.
  28. Ansarifar, J., Wang, L. & Archontoulis, S.V., "An interaction regression model for crop yield prediction," Scientific Reports,  vol. 11, pp. 17754, 2021.
  29. Geipel, J., Link, J., Claupein, W., “Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system,” Remote sensing vol. 6, pp. 10335–10355, 2014.
  30. Huang, H., Wei, X., Zhou, Y., “An overview on twin support vector regression. Neurocomputing vol. 490, pp. 80–92, 2022.
  31. Maimon, O.Z., Rokach, L., 2014. Data mining with decision trees: theory and applications. vol. 81, 2014
  32. Choudhary, K., Shi, W., Dong, Y., Paringer, R., Random forest for rice yield mapping and prediction using sentinel-2 data with google earth engine. Advances in Space Research, vol. 70, pp. 2443-2457, 2022
  33. Nevavuori, P., Narra, N., Lipping, T., “Crop yield prediction with deep convolutional neural networks,” Computers and Electronics in Agriculture vol. 163, pp. 104859, 2019.
Index Terms

Computer Science
Information Sciences

Keywords

Crop Yield Prediction Machine Learning Approaches Multispectral Images Algorithmic Classification Chronological Review

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