Open Access
Issue
ITM Web Conf.
Volume 81, 2026
International Conference on Emerging Technologies for Multidisciplinary Innovation and Sustainability (ETMIS 2025)
Article Number 01026
Number of page(s) 11
DOI https://doi.org/10.1051/itmconf/20268101026
Published online 23 January 2026
  1. M. Haseeb, Z. Tahir, S. A. Mahmood, and A. Tariq, "Winter wheat yield prediction using linear and nonlinear machine learning algorithms based on climatological and remote sensing data," Information Processing in Agriculture, Elsevier, Feb. 2025. [Google Scholar]
  2. V. Martos, A. Ahmad, P. Cartujo, and J. Ordonez, "Ensuring agricultural sustainability through remote sensing in the era of Agriculture 5.0," Applied Sciences, vol. 11, no. 13, p. 5911, 2021. [CrossRef] [Google Scholar]
  3. M. Kumari, Suman, and D. Prasad, "Crop Yield Prediction using Remote Sensing: A Review," in Proc. 2024 International Conference on Computational Intelligence and Computing Applications (ICCICA), 2024. [Google Scholar]
  4. M. C. Traore, A. Khamis, S. Zoghlami, M. W. Al Shorman, and A. D. Ghebrezgabher, "Developing an automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather datasets," Computers and Electronics in Agriculture, vol. 214, p. 108241, 2023. [Google Scholar]
  5. L. J. P. van der Linden, S. H. H. F. Smeets, J. Suomalainen, L. R. van der Voort, R. Heroldovâ, L. Kooistra, and S. J. de Jong, "Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning," Computers and Electronics in Agriculture, vol. 211, p. 107998, 2023. [Google Scholar]
  6. V. V. Rud, A. S. Shendryk, S. A. Morgounov, and A. A. Kiselev, "Spatial prediction of agrochemical properties on the scale of a single field using machine learning methods based on remote sensing data," Remote Sensing, vol. 15, no. 9, p. 2310, 2023. [Google Scholar]
  7. R. Lawes, G. Mata, J. Richetti, A. Fletcher, and C. Herrmann, "Using remote sensing, process-based crop models, and machine learning to evaluate crop rotations across 20 million hectares in Western Australia," Agronomy for Sustainable Development, vol. 42, p. 120, 2022. [Google Scholar]
  8. J. Yuan, Y. Zhang, Z. Zheng, W. Yao, W. Wang, and L. Guo, "Grain crop yield prediction using machine learning based on UAV remote sensing: A systematic literature review," Drones, vol. 8, no. 10, p. 559, oct. 2024. [Google Scholar]
  9. A. Tripathi, R. K. Tiwari, and S. P. Tiwari, "A deep learning multi-layer perceptron and remote sensing approach for soil health-based crop yield estimation," Int. J. Appl. Earth Obs. Geoinf., vol. 113, p. 102959, Aug. 2022. [Google Scholar]
  10. A. Maimaitijiang, A. Sagan, B. Sidike, L. Hartling, J. M. Peterson, and L. M. Shapiro, "UAV remote sensing for high-throughput phenotyping and for yield prediction of Miscanthus by machine learning techniques," Remote Sens., vol. 12, no. 1, p. 169, Jan. 2020. [Google Scholar]
  11. A. K. Singh, R. S. Dubey, and P. K. Mishra, "A review of hybrid approaches for quantitative assessment of crop traits using optical remote sensing: Research trends and future directions," Comput. Electron. Agric., vol. 203, p. 107493, Mar. 2023. [Google Scholar]
  12. R. K. Verma, S. P. Singh, and A. K. Yadav, "Crop yield prediction in agriculture: A comcomprehensive review of machine learning and remote sensing approaches," Inf. Process. Agric., vol. 10, no. 2, pp. 89-102, Mar. 2023. [Google Scholar]
  13. A. B. Kumar, S. Tiwari, and R. N. Mishra, "ConvLSTM-ViT: A deep neural network for crop yield prediction using remote sensing data," Comput. Electron. Agric., vol. 205, p. 107654, Apr. 2023. [Google Scholar]
  14. N. Singh, R. Kaur, and A. Sharma, "Artificial intelligence-enabled soft sensor and internet of things for smart agriculture," IEEE Internet Things J., vol. 10, no. 4, pp. 3456-3465, Feb. 2023. [Google Scholar]
  15. K. R. Joshi, M. S. Rao, and P. N. Desai, "A systematic literature review on crop yield prediction with deep learning and remote sensing," Remote Sens., vol. 15, no. 3, p. 567, Feb. 2023. [Google Scholar]
  16. Y. Liu, H. Zhang, and X. Wang, "Deep learning application for crop classification via multi-temporal remote sensing images," ISPRS J. Photogramm. Remote Sens., vol. 198, pp. 120–132, Feb. 2023. [Google Scholar]
  17. M. Rahman, T. Ahmed, and S. Chowdhury, "Optimised crop prediction and monitoring using ensemble machine learning algorithms," Sensors, vol. 23, no. 2, p. 789, Jan. 2023. [Google Scholar]
  18. A. Patel, R. Mehta, and K. Shah, "An efficient crop yield prediction framework using a hybrid machine learning model," Agric. Syst., vol. 201, p. 103567, Nov. 2023. [Google Scholar]
  19. S. Das, A. Roy, and T. Banerjee, "Deep ensemble model with hybrid intelligence technique for crop yield prediction," Inf. Process. Agric., vol. 10, no. 1, pp. 45-56, Jan. 2023. [Google Scholar]
  20. R. Gupta, S. Jain, and M. Verma, "Hybrid CNN-SVM classifier approaches to process multi-spectral remote sensing data for crop classification," IEEE Access, vol. 11, pp. 45678–45689, 2023 [Google Scholar]

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