Conceptual Framework for AI-Based Prediction of Climate Change Impact on Plant Growth

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Autore principale:	Devmalkar, Vijay
Natura:	Recurso digital
Pubblicazione:	Zenodo 2026
Soggetti:	Keywords: - Climate Change, Plant Growth, Artificial Intelligence, Predictive Modeling, Plant Physiology, Sustainable Agriculture, Climate Resilience.
Accesso online:	Acceder al recurso
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author	Devmalkar, Vijay
author_facet	Devmalkar, Vijay
contents	<p><strong><em><span>Abstract:</span></em></strong></p> <p><em><span>Climate change has emerged as one of the most pressing environmental challenges affecting plant growth and agricultural sustainability worldwide. Long-term variations in temperature, atmospheric carbon dioxide concentration, rainfall distribution and frequency of extreme weather events significantly influence plant physiological processes such as photosynthesis, respiration, transpiration, nutrient uptake and biomass accumulation. Although plants possess adaptive mechanisms including osmotic regulation, antioxidant activity and morphological modifications, persistent climatic stress may reduce growth efficiency and yield stability. The present study proposes a conceptual framework for integrating artificial intelligence-based predictive approaches with plant growth analysis to better understand and anticipate the impact of climate variability. The framework emphasizes systematic collection of climatic variables and plant growth indicators which is followed by structured analytical modeling to identify relationships and forecast potential growth trends under changing environmental conditions. Rather than focusing on complex computational details, the study highlights interdisciplinary collaboration between plant physiology and predictive data analysis to support sustainable agricultural planning. The proposed framework aims to strengthen early warning systems to improve crop management strategies and contribute to climate-resilient agricultural development.</span></em></p> <p> </p>
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spellingShingle	Conceptual Framework for AI-Based Prediction of Climate Change Impact on Plant Growth Devmalkar, Vijay Keywords: - Climate Change, Plant Growth, Artificial Intelligence, Predictive Modeling, Plant Physiology, Sustainable Agriculture, Climate Resilience. <p><strong><em><span>Abstract:</span></em></strong></p> <p><em><span>Climate change has emerged as one of the most pressing environmental challenges affecting plant growth and agricultural sustainability worldwide. Long-term variations in temperature, atmospheric carbon dioxide concentration, rainfall distribution and frequency of extreme weather events significantly influence plant physiological processes such as photosynthesis, respiration, transpiration, nutrient uptake and biomass accumulation. Although plants possess adaptive mechanisms including osmotic regulation, antioxidant activity and morphological modifications, persistent climatic stress may reduce growth efficiency and yield stability. The present study proposes a conceptual framework for integrating artificial intelligence-based predictive approaches with plant growth analysis to better understand and anticipate the impact of climate variability. The framework emphasizes systematic collection of climatic variables and plant growth indicators which is followed by structured analytical modeling to identify relationships and forecast potential growth trends under changing environmental conditions. Rather than focusing on complex computational details, the study highlights interdisciplinary collaboration between plant physiology and predictive data analysis to support sustainable agricultural planning. The proposed framework aims to strengthen early warning systems to improve crop management strategies and contribute to climate-resilient agricultural development.</span></em></p> <p> </p>
title	Conceptual Framework for AI-Based Prediction of Climate Change Impact on Plant Growth
topic	Keywords: - Climate Change, Plant Growth, Artificial Intelligence, Predictive Modeling, Plant Physiology, Sustainable Agriculture, Climate Resilience.
url	https://doi.org/10.5281/zenodo.18957337

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