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VOL. 1, ISSUE 3 (2025)
Emerging mathematical techniques for crop yield prediction: A comprehensive review
Authors
Ingale Jagruti D, Dr. Goswammi V S, Dr. Kailas L Vairal
Abstract
Crop yield prediction is critical for global
food security, agricultural planning, and climate-change adaptation. Recent
advances in mathematical modeling, statistical learning, and dynamical systems
have dramatically improved predictive accuracy over traditional empirical or
process-based models. This paper reviews emerging mathematical techniques from
2020–2025, including deep learning with spatiotemporal architectures,
physics-informed neural networks (PINNs), causal inference frameworks,
topological data analysis (TDA), dynamical systems with delay embeddings, and
hybrid quantum–classical approaches. We compare their theoretical foundations,
data requirements, interpretability, and reported performance on major crops
(maize, wheat, rice, soybean). The survey concludes with open mathematical
challenges and promising future directions.
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Pages:5-7
How to cite this article:
Ingale Jagruti D, Dr. Goswammi V S, Dr. Kailas L Vairal "Emerging mathematical techniques for crop yield prediction: A comprehensive review". International Journal of Research in All Subject, Vol 1, Issue 3, 2025, Pages 5-7
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