Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Swathi Kumari H., K. T. Veeramanju
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Abstract

Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.
优化灌溉调度和水资源管理的预测模型:人工智能和 ML 方法综述
目的:保持农业产量、保护水资源供应和减少对环境的影响都有赖于有效的水资源管理。本文旨在通过对文献的全面回顾以及对各种人工智能和 ML 技术的深入分析,阐明灌溉调度预测建模中使用的前沿方法。研究的目标是通过对各种算法、数据源和应用进行有条不紊的分析,确定基于人工智能和 ML 的灌溉管理系统的优缺点和未来发展方向。此外,该研究还试图展示数据驱动方法如何提高灌溉系统的可持续性、准确性和精确性。农业、水资源管理和环境保护领域的利益相关者可以通过全面了解预测模型的理论基础和实际应用,做出明智的决策,最大限度地提高灌溉调度技术。本研究还试图解决在实际灌溉管理中实施人工智能和 ML 解决方案时遇到的可扩展性、模型可解释性和数据缺乏等问题。最终,本综述的结论将推动我们了解如何利用人工智能和 ML 提高农业系统的适应能力和用水效率,从而在面临日益严重的缺水问题和气候变化的情况下,支持适应性和可持续的水资源管理战略:为了收集本综述研究的信息,对可靠来源的几篇研究文章进行了分析和比较:提供当前在最佳灌溉调度和水资源管理预测模型方面的研究空白,并建议使用人工智能和 ML 技术填补这些空白:为了应对水资源短缺和气候变化带来的日益严峻的挑战,本文的研究结果强调了人工智能和 ML 技术在优化灌溉调度、增强农业抗灾能力、提高用水效率以及支持适应性和可持续水资源管理战略方面的变革潜力:本文的独特性和意义在于其对人工智能和 ML 方法在理想水资源管理和灌溉调度预测建模中的应用进行了深入分析。论文类型:文献综述:文献综述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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