Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications

Xin Zhang, Zakir Ibrahim, Muhammad Bilawal Khaskheli, Hamad Raza, Fanrui Zhou () and Imran Haider Shamsi ()
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Xin Zhang: School of Marxism, Zhejiang University, Hangzhou 310058, China
Zakir Ibrahim: Department of Agronomy, Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China
Muhammad Bilawal Khaskheli: School of Law, Dalian Maritime University, Dalian 116026, China
Hamad Raza: Lyallpur Business School, Government College University, Faisalabad 38000, Pakistan
Fanrui Zhou: Key Laboratory of State Forestry and Grassland Administration on Highly Efficient Utilization of Forestry Biomass Resources in Southwest China, College of Material and Chemical Engineering, Southwest Forestry University, Kunming 650224, China
Imran Haider Shamsi: Department of Agronomy, Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China

Sustainability, 2024, vol. 16, issue 17, 1-26

Abstract: Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges.

Keywords: abiotic stress; environment; bioinformatics educational tools; artificial intelligence; crop stress biology; crop improvement; sustainable agriculture (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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